Bassem Jalali
email: bassemfss@gmail.com

Marie-Alexandrine Sicre

Vincent Klein

Sabine Schmidt

Vittorio Maselli

Fabrizio Lirer

Maria-Angela Bassetti

Samuel Toucanne

Stephan J Jorry

Donatella Insinga

Paola Petrosino

Fanny Châles

Deltaic and coastal sediments as recorders of Mediterranean regional climate and human impact over the past three millennia

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Deltaic and Coastal Sediments as Recorders of Mediterranean Regional Climate and Human Impact

Over the Past Three Millennia

Bassem Jalali, Marie-Alexandrine Sicre, Vincent Klein, Sabine Schmidt, Fabrizio Lirer, Maria-Angela Bassetti, Donatella Insinga, Stéphan Jorry, Vittorio Maselli, Samuel Toucanne, et al.

To cite this version:

Bassem Jalali, Marie-Alexandrine Sicre, Vincent Klein, Sabine Schmidt, Fabrizio Lirer, et al.. Deltaic and Coastal Sediments as Recorders of Mediterranean Regional Climate and Human Impact Over the Past Three Millennia. Paleoceanography and Paleoclimatology, 2018, 33 (6), pp.579-593. 10.1029/2017PA003298. hal-02192660

Introduction

The Mediterranean Sea is particularly sensitive to climate changes because of its location between temperate and tropical zones, its relatively small size and the use of its surrounding lands [START_REF] Lionello | The Mediterranean climate: an overview of the main characteristics and issues[END_REF]IPCC, 2014). Indeed, the Mediterranean basin has supported the growth and development of human civilizations since the beginning of the Neolithic period and notably experienced deforestation as croplands, pastures and constructions were established [START_REF] Kaplan | The prehistoric and preindustrial deforestation of Europe[END_REF].

Mediterranean deltaic and coastal sediments are areas of high accumulation of organic material that provide ideal archives for generating high-resolution records. Organic biomarkers preserved in these sediments contain a wealth of information for reconstructing at decadal scale past environmental and climate variability [START_REF] Jalali | Holocene climate variability in the North-Western Mediterranean Sea (Gulf of Lions)[END_REF][START_REF] Jalali | High-resolution Holocene climate and hydrological variability from two major Mediterranean deltas (Nile and Rhone), The Holocene[END_REF]. Few highresolution sea surface temperature (SST) records of the late Holocene have been produced

from Mediterranean coastal sites [START_REF] Grauel | What do SST proxies really tell us? A high-resolution multiproxy (UK′ 37, TEXH86 and foraminifera δ18O) study in the Gulf of Taranto, central Mediterranean Sea[END_REF][START_REF] Sicre | Sea surface temperature variability in the North Western Mediterranean Sea (Gulf of Lion) during the Common Era[END_REF] more have used deeper sediments [START_REF] Moreno | The Medieval Climate Anomaly in the Iberian Peninsula reconstructed from marine and lake records[END_REF]Neito-Moreno et al., 2013;[START_REF] Incarbona | Mediterranean circulation perturbations over the last five centuries: Relevance to past Eastern Mediterranean Transient-type events[END_REF][START_REF] Cisneros | Sea surface temperature variability in the central-western Mediterranean Sea during the last 2700 years: a multi-proxy and multi-record approach[END_REF]. While these studies have contributed to improve our understanding of past oceanic circulation in the Western Mediterranean Sea, sub-basin-scale data are still very scarce, in particular in the Eastern basin, to get a comprehensive picture of past changes and their key drivers.

Co-registered signals of terrestrial and marine biomarkers in coastal sediments provide a powerful approach that has been little explored (Ohkouchi et al. 1994;[START_REF] Ternois | Climatic changes along the northwestern African continental margin over the last 30 kyrs[END_REF].

Climate variations inferred from marine proxies combined with biogenic terrestrial proxies have been recently applied with success in two major Mediterranean deltas shedding light on climate-driven changes over the Holocene in the two contrasted Nile and Rhone river drainage basins [START_REF] Jalali | Holocene climate variability in the North-Western Mediterranean Sea (Gulf of Lions)[END_REF][START_REF] Jalali | High-resolution Holocene climate and hydrological variability from two major Mediterranean deltas (Nile and Rhone), The Holocene[END_REF]. The Mediterranean basin offers a variety of coastal settings to fully develop this multi-proxy approach and expand it to the reconstruction of landsea transects.

In this study, we present a new high-resolution paired time-series that document SSTs and contemporary vegetation changes that have occurred in the watershed of the Var River [START_REF] Bonneau | Millennial-scale response of a western Mediterranean river to late Quaternary climate changes: a view from the deep sea[END_REF] connected to the deep Ligurian Sea (Northwestern Mediterranean Sea)

and three others from coastal sites of the South Adriatic Sea (SAS) where climate conditions were presumably drier. Data from the Var delta were compared to the same proxy records acquired in the nearby Rhone River mud belt resulting from the deposition of suspended particles delivered from a catchment basin extending far North as compared to that of the Var River [START_REF] Jalali | Holocene climate variability in the North-Western Mediterranean Sea (Gulf of Lions)[END_REF]. In the SAS, the three sediment cores are located along a near-shore to open sea transect to account for local hydrological features, i.e. the coastal Western Adriatic Current (WAC) and the SAS convection area, that are both major components of the Adriatic Sea circulation. The main focus of this work is to extend the use of our multi-proxy approach to the central/eastern Mediterranean to improve our understanding of the basin-scale response of the Mediterranean region to climate change.

Regional setting

The GoL and Ligurian Sea are the two regions of the Western Mediterranean Sea where deep-water formation takes place [START_REF] Béthoux | Deep water in the western Mediterranean: peculiar 1999 and 2000 characteristics, shelf formation hypothesis, variability since 1970 and geochemical inferences[END_REF]. Interactions between the mid-latitude westerly winds and the regional topography (Alps, Massif Central and Pyrenees) give rise to northerly winds, the Mistral and Tramontane, that are responsible for strong vertical mixing and convection in the GoL. The geostrophic Northern Current (Fig. 1) flowing westward along the NW Italian coast towards the Catalan coast [START_REF] Millot | Circulation in the Mediterranean sea[END_REF]) is a major hydrological feature of the NW Mediterranean Sea. This current contributes to the redistribution of sediments delivered by the Rhone River ("mud belt", [START_REF] Bassetti | Holocene hydrological changes in the Rhône River (NW Mediterranean) as recorded in the marine mud belt[END_REF] and smaller river streams of the Languedoc-Roussillon region [START_REF] Tesi | Source, transport and fate of terrestrial organic carbon on the western Mediterranean Sea, Gulf of Lions, France[END_REF] along the GoL continental shelf. More than 80% of sediments are supplied by the Rhone River. To the East, the Ligurian Sea is fed by small rivers from the Southern French Alps and western Apennines, such as the Var River, which is the main water stream discharging into the Ligurian Sea (Fig. 1). The hydrological regime of the Var (as that of other small rivers in southern France) is characterized by the pronounced seasonality typical of the Mediterranean climate, with two high discharge periods in autumn and spring [START_REF] Xoplaki | Wet season Mediterranean precipitation variability: influence of large-scale dynamics and trends[END_REF].

The Adriatic Sea is an elongated semi-enclosed basin connected to the Ionian Sea through the Strait of Otranto (Fig. 1). The SAS is characterized by a cyclonic surface circulation impacted by river runoff, wind stress and inflowing waters from the Ionian Sea through the Eastern Adriatic Current (EAC), heading northwards along the eastern Adriatic coast [START_REF] Milligan | Sediment dynamics in the western Adriatic Sea: From transport to stratigraphy[END_REF][START_REF] Zavatarelli | The Adriatic Sea modelling system: a nested approach[END_REF]. The WAC (Fig. 1), carrying freshwater to the South, is another feature of the SAS. The WAC is characterized by lower salinity and temperature, generating west-east thermal and salinity gradients [START_REF] Lipizer | Qualified temperature, salinity and dissolved oxygen climatologies in a changing Adriatic Sea[END_REF]. Dense water formation in the North Adriatic and deep convection in its southern part occur in winter due to heat loss induced by cold and dry Northeasterly Bora winds [START_REF] Turchetto | Particle transport in the Bari canyon (southern Adriatic Sea)[END_REF], making the Adriatic Sea the main source of deep-waters in the Eastern Mediterranean [START_REF] Lascaratos | Estimation of deep and intermediate water mass formation rates in the Mediterranean Sea, Deep Sea Research Part II[END_REF][START_REF] Chiggiato | Cascading Dense water Flow and its Impact on the Sea Floor in the Adriatic and Aegean Sea, Eastern Mediterranean[END_REF]. Dense-water formation is modulated by buoyancy variations due to fresh water inputs from the Po River and eastern

Apennine rivers discharging into the Adriatic Sea, especially in spring (Fig. 1). These rivers contribute more than 70% of freshwater runoff [START_REF] Raicich | On the fresh balance of the Adriatic Sea[END_REF]. The maximum Po River discharge occurs in spring as a consequence of snow melting and in autumn due to high rainfall [START_REF] Nelson | Hydrography, sediment dispersal, and recent historical development of the Po river delta[END_REF]. Other Apennine rivers with the same hydrological seasonal cycle also contribute to the sediment and freshwater budget of the Adriatic Sea [START_REF] Sekulic | Balance of average annual fresh water inflow into the Adriatic Sea[END_REF].

The Adriatic-Ionian Bimodal Oscillating System (BiOS) is an important feature of the Adriatic Sea thermohaline circulation and SAS circulation at multi-decadal timescales [START_REF] Gačić | Can internal processes sustain reversals of the ocean upper circulation? The Ionian Sea example[END_REF]. During BiOS cyclonic phases, the high-salinity Levantine waters spread into the South Adriatic Sea and favor deep-water formation by preconditioning convection.

Conversely, the anticyclonic BiOS mode allows less salty Modified Atlantic Waters (MAW) to enter the SAS, which then becomes less prone to dense water production due to increase buoyancy. Episodes of intermediate to deep-water formation in the Aegean Sea, known as the Eastern Mediterranean Transient (EMT) [START_REF] Roether | Recent changes in eastern Mediterranean deep waters[END_REF], occurred from 1988 to 1995, and affected convection in the SAS through the Ionian Sea [START_REF] Roether | Recent changes in eastern Mediterranean deep waters[END_REF][START_REF] Gačić | Salinity in the Sicily Channel corroborates the role of the Adriatic-Ionian Bimodal Oscillating System (BiOS) in shaping the decadal variability of the Mediterranean overturning circulation[END_REF]. Strengthened decadal scale variability and anticyclonique BiOS circulation was observed during the EMT [START_REF] Vilibić | Changes in the Adriatic oceanographic properties induced by the Eastern Mediterranean Transient[END_REF], which happened to be a period of stable positive NAO state. However, the driving mechanisms of the BiOS and notably the role of the atmosphere is still debated [START_REF] Pinardi | Mediterranean Sea large-scale lowfrequency ocean variability and water mass formation rates from 1987 to 2007: a retrospective analysis[END_REF].

The Mediterranean climate is influenced by the large-scale atmospheric circulation of the North Atlantic region [START_REF] Lionello | The Mediterranean climate: an overview of the main characteristics and issues[END_REF]. The East Atlantic (EA) and the North Atlantic Oscillation (NAO) are the main atmospheric modes impacting the western and central/ eastern

Mediterranean Sea Surface Temperatures (SSTs) and precipitations [START_REF] Hurrell | Decadal trends in the North Atlantic Oscillation: regional temperatures and precipitation[END_REF][START_REF] Josey | Impacts of atmospheric modes of variability on Mediterranean Sea surface heat exchange[END_REF]. The NAO reflects the mean sea-level pressure gradient between the Azores high and Icelandic low, controlling the direction and strength of the Westerly Winds. This mode of variability impacts the Atlantic and Mediterranean storm tracks, feeding precipitation into the region. Positive (negative) NAO states are associated with negative (positive) annual SST anomalies in the Ligurian, Tyrrhenian, Adriatic and Ionian seas, and below (above) average winter precipitation [START_REF] Skliris | Decadal scale variability of sea surface temperature in the Mediterranean Sea in relation to atmospheric variability[END_REF]. The EA is characterized by an anticyclonic (cyclonic) cell during its negative (positive) phases with its center of action located at 52.5°N and 27.5°W [START_REF] Barnston | Classification, seasonality and persistence of low-frequency atmospheric circulation patterns[END_REF]. According to [START_REF] Josey | Impacts of atmospheric modes of variability on Mediterranean Sea surface heat exchange[END_REF], the EA exerts strong control on SSTs in the Gulf of Lion (GoL) and the Adriatic Sea at an interannual timescale. The effect of these modes of variability on the low frequency climate has been little explored, because the instrumental records are short, and high-resolution proxy records, in particular for the EA mode are lacking.

3 Material and methods

Core locations and age models

One new core from the Northwestern Mediterranean Sea and three cores from the SAS were analyzed for this study, and compared to similar paired records from the GoL [START_REF] Jalali | KESC9-14 cores (this study). (b) TERR-alkane abundances at the INV12-15, CSS00-07 and SW104-ND-14Q cores[END_REF]. All AMS 14 C dates used here have been calibrated using CALIB7.1 software and the marine calibration curve Marine13 [START_REF] Stuiver | Extended 14 C data base and revised[END_REF][START_REF] Reimer | IntCal13 and Marine13 radiocarbon age calibration curves 0-50,000 years cal BP[END_REF]. The age-depth model of each core was established by linear interpolation between control points.

Northwestern Mediterranean Sea

KESC9-14 core (Fig. 1) was recovered from the NW Ligurian Sea in the vicinity of the

South Adriatic Sea

The piston core INV12-15 was retrieved from shallow waters of the SW Adriatic margin (41.56°N, 16.00°E; 15 m water depth, Fig. 1) during the 2008 INVAS12 cruise [START_REF] Maselli | Delta growth and river valleys: the influence of climate and sea level changes on the South Adriatic shelf (Mediterranean Sea)[END_REF] and recovered from the axial infill of a low stand incised valley system [START_REF] Maselli | Large-scale single incised valley from a small catchment basin on the western Adriatic margin (central Mediterranean Sea)[END_REF]. The age model (Table S1) is based on three AMS 14 C dates performed at the Poznań Radiocarbon Laboratory [START_REF] Maselli | Delta growth and river valleys: the influence of climate and sea level changes on the South Adriatic shelf (Mediterranean Sea)[END_REF] and 210 Pb dates performed at the EPOC laboratory (Table S1 andS2). We used a ∆R= 135.8±40.8 to correct AMS on the secular variation of Earth's magnetic field measurements [START_REF] Vigliotti | Palaeomagnetic and rock magnetic analysis of Holocene deposits from the Adriatic Sea: detecting and dating short-term fluctuations in sediment supply[END_REF] S3. According to the TAS (Total Alkali Silica) classification diagram (Le Maitre, 2005), samples ND14A-15 and ND14A-79 have a phonolitic composition with a few points for the latter straddling the boundary between foidite and tephri-phonolite fields, whereas ND14A-62 falls in the foidite field (Fig. 2).

Finally, sample ND14A-60 plots along the boundary between foidite and tephriphonolite fields (Fig. 2). The lithological (e.g. leucite occurrence) and chemical features allow us to relate the studied deposits to the mildly to highly silica undersaturated products of Somma-Vesuvio, erupted during the last 3000 years [START_REF] Santacroce | Age and whole rock-glass compositions of proximal pyroclastics from the major explosive eruptions of Somma-Vesuvius: A review as a tool for distal tephrostratigraphy[END_REF]. In order to establish proximal-distal correlations, the composition of the investigated samples is plotted in the CaOvsFeO and Na 2 O/K 2 OvsCaO+FeO diagrams (Fig. 2b,c). The average compositional fields of possible terrestrial correlatives are reported for comparison, taking into account the subplinian and plinian events during the considered time span. The composition of the lowermost sample ND14A-79 suggests a clear correlation to the Pompei (79 CE) products. In detail, the studied tephra can be the distal counterpart of the white pumice, phonolitic in composition [START_REF] Santacroce | Age and whole rock-glass compositions of proximal pyroclastics from the major explosive eruptions of Somma-Vesuvius: A review as a tool for distal tephrostratigraphy[END_REF]. Samples ND14A-62 and ND14A-60 well resemble the composition of the juvenile fraction of the Pollena eruption, a subplinian event which occurred in 472 CE. Stratigraphic position and chemical features (Fig. 2) link the youngest ND14A-15 to the 1631 CE subplinian eruption during which a high eruptive column formed rapidly, causing lapilli fallout east of the volcano [START_REF] Santacroce | Age and whole rock-glass compositions of proximal pyroclastics from the major explosive eruptions of Somma-Vesuvius: A review as a tool for distal tephrostratigraphy[END_REF] (See appendix for more details on the Tephrostratigraphy). These dates complement those obtained from 210 Pb for establishing the age model of core SW104-ND-14Q (Table S2). The SW104-ND-14Q core ranges from 700 BCE to 2003 CE and has been studied at an average temporal resolution of 24 years. Overall, the precision of the age model of these cores spans from a few years for the 20 th century section, dated with 210 Pb, to 50 -100 years for the oldest sections, dated with 14 C (Tables S1 andS2).

Biomarker analyses

All cores were sampled continuously at a 1 cm sampling step. A few grams of freeze-dried sediments were used to extract lipids following the experimental procedure described in [START_REF] Ternois | Climatic changes along the northwestern African continental margin over the last 30 kyrs[END_REF]. Alkenones and n-alkanes were isolated from the total lipid extract by silica gel chromatography using solvent mixtures of increasing polarity. Quantification of alkenones and n-alkanes was performed using a Varian CX 3400 gas chromatograph and prior

Results

Alkenone-derived SST signals

The SST reconstruction at KESC9-14 shows mean values around 17°C (Fig. 3a), warmer values during the Roman Period (RP), and a long-term cooling (~1°C) that seems to reverse at the end of the Medieval Climate Anomaly (MCA; 1250 CE). SSTs were lower than average during the Dark Ages Cold Period (DACP), and most of the MCA between 1000-1250 CE.

Records form the Adriatic Sea show strong centennial time-scale variability but no clear longterm trend (Fig. 3b). SSTs from near shore core INV12-15 are significantly higher than those in the other two records, most likely because of the shallow depth of this site (Fig. 3b

Land-derived inputs

The concentrations and compositional features of n-alkanes (ACL 27-33 ) at each core site are shown in Figures 3c,d ande. TERR-alkanes in the Var River sediments (KESC9-14 core)

show low concentrations in the early part of the record, and two centennial-scale peaks with higher concentration around 600 and 1000 CE (Fig. 3c). TERR-alkane records in the SAS show some variability and generally enhanced values during the DACP and MCA, with a sharp rise over the last 500 years (Fig. 3d). Contrary to the Var sediments, the SW104-ND-14Q record shows high concentrations during the early RP. In all four records, the ACL 27-33 values are rather stable, spanning between 30 and 30.3 indicating similar and no major change in the vegetation type in the NW Mediterranean and SAS regions (Fig. 3e).

Discussion

Instrumental and alkenone derived SSTs during the 20 th century

Figure 4a presents the annual mean SSTs of the central/eastern and western Mediterranean

Sea for the period 1955-2012 obtained from World Ocean Atlas database [START_REF] Locarnini | World Ocean Atlas 2013[END_REF]. Coldest SSTs (17°C) are found in the GoL, Ligurian and northern Adriatic Sea as a result of heat and buoyancy loss due to cold winds (Mistral, Tramontane and Bora) blowing in these convection regions, whereas in the Southern Adriatic annual SSTs are slightly warmer.

As shown by the monthly SSTs (Fig. 4b) the season cycle in the Adriatic and Ligurian Sea is similar, but summer values are cooler by 2°C in the Ligurian Sea. Among the three Adriatic Sea records, INV12-15 exhibits the warmest values (Fig. 4c) as expected from this near shore shallow site. According to [START_REF] Totti | Seasonal variability of phytoplankton populations in the middle Adriatic sub-basin[END_REF], maximum phytoplankton production along the western coast of the Adriatic Sea occurs during autumn and spring, due to enhanced nutrient inputs from Italian rivers transported by the WAC. However, SSTs in the upper part of the core, i.e. ~18.5°C (Fig. 4c) are close to the present annual mean (Fig. 4b; i.e. ~18°C)

suggesting that production occurs throughout the year. Note that SSTs in the second half of the 20 th century are consistent with the data provided by average 5°x 5° grid Kaplan SSTs V2 dataset (http:/ /www.esrl.noaa.gov/psd/data/gridded/data.kaplan_sst.html, [START_REF] Kaplan | Analyses of global sea surface temperature 1856-1991[END_REF] (Fig. 4c), but SSTs were significantly warmer before this period (~22°C). Yet, the rapid SST decline starting in the early 1900s does not seem to be due to sediment disturbance, because the 210 Pb data shows normal decay over the last century (Table S2). SST estimates for the inner-shelf located CSS00-07 (90 m water depth) and the more open sea SW104-ND-14Q

(1013 m water depth) sites are close to the annual mean, i.e. 18°C (Fig. 4b,c). The SW104-ND-14Q core-top value (17.9°C) is similar to that found in the convection region of the GoL [START_REF] Sicre | Sea surface temperature variability in the North Western Mediterranean Sea (Gulf of Lion) during the Common Era[END_REF] (Fig. 4a). SSTs in core CSS00-07 are also close to the annual mean, but slightly warmer and more strongly fluctuating during the last decades, as compared to instrumental data and core SW104-ND-14Q (Fig. 4c). Owing to its more coastal location, core CSS00-07 is expected to be variably influenced by the gyre circulation and the WAC (Fig. 1). Overall, this comparison shows that SSTs at the SW104-ND-14Q and CSS00-07 sites are broadly consistent with instrumental data except for warmer and more variable SSTs over the last decades in CSS00-07 and the exceptional temperature decline in the shallowest site since the onset of the 20 th century.

SST variability over the past 3000 years

Figure 5 plots our SST records together with the published high-resolution SST signal from the GoL [START_REF] Jalali | Holocene climate variability in the North-Western Mediterranean Sea (Gulf of Lions)[END_REF][START_REF] Sicre | Sea surface temperature variability in the North Western Mediterranean Sea (Gulf of Lion) during the Common Era[END_REF]. Figure 5d shows the reconstructed NAO index [START_REF] Trouet | Persistent positive North Atlantic Oscillation mode dominated the medieval climate anomaly[END_REF][START_REF] Olsen | Variability of the North Atlantic Oscillation over the past 5,200 years[END_REF].

In the NW Mediterranean Sea, both cores KESC9-14 and KSGC-31_GolHo-1B show warmer values during the RP, and long-term cooling thereafter, with low values during the DACP. However, the two SST records differ over the MCA. SSTs in the GoL increased between 1000-1200 CE, but decreased in the Ligurian Sea (Fig. 5a,b). A warmer MCA was also found in the Balearic Sea [START_REF] Cisneros | Sea surface temperature variability in the central-western Mediterranean Sea during the last 2700 years: a multi-proxy and multi-record approach[END_REF] and Alboran Sea (Nieto- [START_REF] Nieto-Moreno | Climate conditions in the westernmost Mediterranean over the last two millennia: An integrated biomarker approach[END_REF]. [START_REF] Skliris | Decadal scale variability of sea surface temperature in the Mediterranean Sea in relation to atmospheric variability[END_REF] show a negative correlation between NAO and year mean SSTs in the Ligurian Sea, and no correlation in the GoL. Our results are consistent with these observations. Furthermore, NAO had less impact on SSTs than the EA mode [START_REF] Josey | Impacts of atmospheric modes of variability on Mediterranean Sea surface heat exchange[END_REF]. This is in agreement with [START_REF] Sicre | Sea surface temperature variability in the North Western Mediterranean Sea (Gulf of Lion) during the Common Era[END_REF], who show evidence of a strong imprint of EA on the GoL SSTs, in particular the impact of negative EA (and associated North Atlantic blocking regimes) on cold spells during the 20 th century and the LIA.

SSTs in the Adriatic Sea cores show different characteristics with strong centennial timescale variability but no clear long-term cooling trend, as seen in the NW Mediterranean and reported by [START_REF] Mcgregor | Robust global ocean cooling trend for the pre-industrial Common Era[END_REF] for the global ocean. The sharp SST drop over the last century in near shore core INV12-15 is puzzling (Fig. 5c). Core INV12-15 is located in the Gulf of Manfredonia (GoM), south of the Gargano Promontory (Fig. 1), where the southward flowing WAC generates eddies that can reach the shelf [START_REF] Marini | Role of the Mid-Adriatic deep in dense water interception and modification[END_REF]. [START_REF] Carniel | Off-shelf fluxes across the southern Adriatic margin: Factors controlling dense-waterdriven transport phenomena[END_REF] reported that cascading of Northern Adriatic Dense Waters (NAdDW) occurs along the western Adriatic shelf along two veins, with a shallower one that can spread over the shelf of the GoM. However, in the absence of information other than SSTs we cannot conclusively interpret this cooling.

The short SST record from the nearby CSS00-07 core depicts different temporal features with two outstanding centennial scale oscillations and a well expressed cooling at ~1400 CE and ~1800 CE (Fig. 5c). Pronounced cold spells are also seen in the open sea core SW104-ND-14Q, but in earlier centuries, i.e. ~200 BCE, ~120 CE, ~230 CE, ~600 CE, ~900 CE and ~1150 CE. These cold spells seem to dampen over the past 700 years, when variability strengthened in CSS00-07. [START_REF] Skliris | Decadal scale variability of sea surface temperature in the Mediterranean Sea in relation to atmospheric variability[END_REF] indicate a strong negative correlation of the southern Adriatic Sea SSTs with the winter NAO index, as in the Ligurian Sea (see Fig. 9 in [START_REF] Skliris | Decadal scale variability of sea surface temperature in the Mediterranean Sea in relation to atmospheric variability[END_REF]. This is supported by our calculations using winter (Dec-Mar) Kaplan SSTs at our core site and the NAO index (r = -0.45; n=161; at the 95% confidence interval) over the last 161 years. This relationship seems to hold true on longer time scales, when using 50 years binned SSTs at SW104-ND-14Q and the winter NAO index of [START_REF] Trouet | Persistent positive North Atlantic Oscillation mode dominated the medieval climate anomaly[END_REF] over the last millennium (r = -0.33; at the 95% confidence interval). Nevertheless, the EA mode is more strongly correlated to SSTs in the Adriatic Sea [START_REF] Josey | Impacts of atmospheric modes of variability on Mediterranean Sea surface heat exchange[END_REF]Papadopolous et al., 2012) as seen by the positive correlation between annual EA index and annual Kaplan SST in SAS for the past 66 years (r = 0.56; at the 95% confidence interval).

SSTs in core SW104-ND-14Q show strong multidecadal variability between ~500 BCE and ~1300 CE, with NAO mostly in a positive state (Fig. 5c,d). A strong NAO is also associated with less precipitation in the Mediterranean Sea [START_REF] Hurrell | Decadal trends in the North Atlantic Oscillation: regional temperatures and precipitation[END_REF]. During the coldest excursion centered at ~900 CE, SSTs were close to temperatures of the Levantine Intermediate Water (LIW) (14-14.3°C; Stanfield et al., 2003), reflecting wind-induced strong vertical mixing and outcropping LIW (Fig. 6a). During the IP, SSTs show only a slight warming (~0.4 °C) in contrast to a steeper temperature increase in the GoL (~2 °C) (Sicre et al., 2016). This is in agreement with the calculated warming trends at the two core sites using annual Kaplan SSTs (0.7 °C in the GoL vs. 0.2 °C in the SAS, in the 1856-2016 CE interval).

As mentioned earlier, the BiOS circulation could have also played a role in preconditioning convection, by allowing in the SAS either MAW or LIW (Fig. 7). [START_REF] Pinardi | Mediterranean Sea large-scale lowfrequency ocean variability and water mass formation rates from 1987 to 2007: a retrospective analysis[END_REF] reported that during the period of positive NAO in 1987-1996 the BiOS circulation was anticyclonic, thus favoring advection of less salty MAW in the SAS. As a result, ADW produced during this time interval had a lower density, which led to a downwelling of the isobars and a progressive weakening of the anticyclonic circulation when the waters spread into the Ionian basin (until its reversal in 1997). Conversely, the cyclonic circulation of the BiOS allows the inflow of LIW causing the formation of saltier and denser ADW. This will in turn cause upwelling of the isobars in the Ionian Sea and progressive weakening of the cyclonic circulation, that will then invert [START_REF] Crisciani | Is the bimodal oscillating Adriatic-Ionian circulation a stochastic resonance?[END_REF]. We thus hypothesize that a sustained positive NAO could have contributed to stronger variability from ~500 BCE to ~1300 CE. Lower amplitude SSTs variations during the LIA occurred after the NAO index shifted to negative and more variable values at ~1450 CE (Fig 5d).

CSS00-07 exhibits strong centennial scale SST oscillations at times when the variability was much reduced in the core SW104-ND-14Q (Fig. 6c). SSTs in the central SAS were sensitive to wind-driven cooling and possibly affected by the BiOS variability, but coastal waters at the location of core CSS00-07 may reflect other influences, such as the cold WAC and southern Adriatic gyre (SAG) waters. To evaluate the contribution of the Po River discharge, we compare the CSS00-07 SSTs record to the flood reconstruction of [START_REF] Camuffo | The analysis of two bi-millennial series: Tiber and Po river floods[END_REF] and an air temperature record derived from the oxygen isotope of a stalagmite collected in Spannagel Cave (Central Alps) [START_REF] Mangini | Reconstruction of temperature in the Central Alps during the past 2000 yr from a δ 18 O stalagmite record[END_REF]. The latter natural archives have traditionally a more accurate age model, with precision on the order of several years to a decade (Fig. 6). Cold episodes in CSS00-07 are coherent with colder WAC waters. The WAC is fed by cold and freshwater originating mainly from the Po River and eastern Apennine rivers. However, temperature of WAC is dependent on the flooding season, and the waters are colder during spring, when snow melts. The two cold events at the CSS00-07 site do not coincide with highest floods of the Po River (Fig. 6b,c) but rather to severe winter intervals as indicated by the low values of  18 O in the Spannagel Cave stalagmite (Fig. 6d). The first cold spell at ~1400 CE broadly matched a single cold interval in central Alps, but the second one at ~1800 CE was coeval with several successive temperature minima in this region (Fig. 6c,d). Severe winter conditions during the LIA promoted snow accumulation in the Alps and Apennines, that would have largely affected the WAC temperature in spring. Overall, the contrasting Adriatic Sea high-resolution SST records presented here demonstrate strong spatial heterogeneity and complexity of this area, reflecting the local impact of both atmospheric and oceanic features.

Hydroclimate and human activities in the central/eastern and NW

Mediterranean over the past 3000 years

Concentrations and compositional features of n-alkanes (ACL 27-33 ) for all core sites are shown in Figure 8. TERR-alkanes in the GoL depict similar trends as in the Var River, except during the MCA. The ACL 27-33 values in the Var delta sediments are higher than in the GoL, in agreement with climatic conditions of the catchment of the two rivers (Fig. 8c). Indeed, lower ACL 27-33 values in the GoL core reflect the generally more humid conditions in the Upper Rhone River drainage basin [START_REF] Jalali | High-resolution Holocene climate and hydrological variability from two major Mediterranean deltas (Nile and Rhone), The Holocene[END_REF], reaching outside the Mediterranean climate zone, to a more temperate climate regime to the North (Fig. 8c). This is in contrast with the purely Mediterranean drainage basin of the Var River. More zonal and southerly westerlies during negative NAO are today responsible for wetter conditions in the Mediterranean region, whereas a positive NAO is associated with drier conditions. During the MCA, when presumably a positive NAO prevailed, TERR-alkanes consistently increased in the Rhone River sediments, but declined in the Var sediments. The 200 years binned Rhone prodelta ACL 27-33 data shows a significant correlation (r=0.66; n=12; at the 95% confidence interval) with summer (April-May-June) precipitation from central Europe over the last 2400 years [START_REF] Büntgen | 2500 years of European climate variability and human susceptibility[END_REF]. This result outlines the sensitivity of the temperate vegetation in the Upper Rhone watershed to summer precipitation variability in Europe. In contrast, the ACL 27-33 values of the Var indicate drier conditions, likely reflecting already established Mediterranean climate.

In the Adriatic cores, TERR-alkanes have high concentrations during the early RP, decreasing values during the MCA (also seen in the Var River sediments) and a sharp rise over the last 500 years, that is not seen in the GoL sediments (Fig. 8a,b). In all Adriatic Sea records, the ACL 27-33 are rather stable ranging between 30 and 30.2 (Fig. 8d), and indicating no major vegetation change in the Italian river watersheds. ACL 27-33 from the southern Adriatic Sea cores are similar to those from the Var (Fig. 8c,d), but the temporal evolution of TERR-alkane concentrations is rather different (Fig. 8a,b). The most striking feature of the Adriatic Sea records is the progressive increase over the last 500 years, which is more pronounced at the coastal sites (INV12-15 and CSS00-07 cores) (Fig. 8b). Taking into account that the ACL 27-33 values are stable, this result can be attributed to increased soil erosion, probably due to anthropogenic activities. This finding suggests that the Mediterranean vegetation was already established in this region 2500 years ago, as also evidenced from palynological data in the southeastern Adriatic borderland [START_REF] Sadori | Vegetation, climate and environmental history of the last 4500 years at lake Shkodra (Albania/Montenegro)[END_REF].

Based on model simulations, [START_REF] Kaplan | The prehistoric and preindustrial deforestation of Europe[END_REF] were able to estimate the forest fraction of usable land in the Mediterranean region over the past 3000 years (Fig. 8e). This model is forced by the population history and maps of suitable land for agriculture and pasture. Their reconstruction shows variations of the forest fraction related to human society development and demographic evolution. Human activities such as forest clearing and exploitation of wood for construction are reflected by low forest cover. Comparison with our southern Adriatic records (Fig. 8b,d) suggests a notable human influence on soil erosion and subsequent offshore delivery of land-derived material during the past five centuries. This is attested by the very low forested fraction of usable land as a consequence of forest clearance (Fig. 8e) at ~1500 CE which appeared to have been more important in Italy than Southern France (see Fig. 6 in [START_REF] Kaplan | The prehistoric and preindustrial deforestation of Europe[END_REF].

Conclusions

This

  determined on 3 g of dried sediment by  spectrometry using a low-background, highefficiency well-shaped germanium detector equipped of a Cryo-cycle (CANBERRA;[START_REF] Schmidt | Recent sediment transport and deposition in the Cap-Ferret Canyon, South-East margin of Bay of Biscay[END_REF].210 Pb xs was determined by subtracting the activity supported by its parent isotope, 226 Ra, from the total 210 Pb activity in the sediment. Errors in 210 Pb xs were calculated by propagation of errors in the corresponding pair ( 226 Pa and 210 Pb). The 210 Pb xs profiles show high core-top values followed by a downcore exponential decrease, from which a mean sedimentation accumulation rate (SAR) using the constant flux/constant sedimentation model was calculated. The deposition year of each sediment layer was obtained by dividing each depth by the sediment accumulation rate assuming a constant sedimentation rate (0.11 cm yr -1 ) and 2008 as the age of the core-top. This 210 Pb-based chronology confirms the 137 Cs onset early 50s (data not shown), indicating that the core top was well recovered. Based on the 210 Pb dating and linear interpolation between radiocarbon AMS 14 C dates, we found that the INV12-15 core spans from 150 to 2007 CE, with a temporal resolution of samples of about 5 years. A second core, CSS00-07, was collected in the southwestern Adriatic shelf (41.2°N, 16.8°E) at 90 m water depth (Fig. 1). It is located in an area characterized by a high sediment supply delivered mainly by the Ofanto River. The age model of the core is based on three control points based

  added 5α-cholestane. The unsaturation index of long chain alkenones with 37 carbon (U k' 37 )and the calibration of[START_REF] Conte | Global temperature calibration of the alkenone unsaturation index (UK′ 37) in surface waters and comparison with surface sediments[END_REF] (T (°C) = -0.957 + 54.293(were used to derive SSTs. The concentrations of high-molecular-weight homolog n-alkanes produced by the vegetation (hereafter named TERR-alkanes) were quantified to estimate the terrestrial content of the sediments. These compounds are constituents of epicuticular waxes that are used by the vegetation to limit water loss. The average chain length (ACL) has been adopted as an indicator of past moisture conditions based on the assumption that the synthesis of longer chain alkanes increases the hydrophobicity of leaf wax composition thereby reducing water loss by evapo-transpiration[START_REF] Gagosian | The importance of atmospheric input of terrestrial organic material to deep sea sediments[END_REF]. The distribution of n-alkanes differs between C3 plants (tree, shrubs and grasses) mostly found in temperate regions, and C4 plants mainly composed of tropical grass species. Rommerskirchen et al. (2006) calculated a mean ACL value of 30.7 in C4 plants and of 29 in C3 plants. This index has been used as an indicator of plants surrounding the area being studied for environmental conditions, and successfully applied in coastal and oceanic sediments to evaluate climate-related vegetation changes (Eglington and Eglington, 2008). Only the predominant odd carbon number n-alkanes (Σ [C27] + [C29] + [C31] + [C33]) were considered to estimate the biogenic terrestrial component. The ACL was calculated in the same carbon range using the following equation ACL 27-33 = ∑ {(27 * [C 27 ]) + … + (33 * [C 33 ])}/ ∑ ([C 27 ] + … + [C 33 ]), to derive information on changing moisture conditions and associated vegetation type in river catchments.

  , cyan curve). The DACP and MCA SSTs show centennial scale fluctuations until about 1600 CE. They become colder and more stable during the Little Ice Age (LIA), then decrease sharply by 3°C over the last century. The short SST record of the CSS00-07 core from the inner-shelf depicts different features with two major centennial scale oscillations (Fig. 3b, orange curve) associated with two cold spells at ~1400 and ~1800 CE and a final warming during the Industrial period (IP). Finally, SSTs in the open-sea core SW104-ND-14Q are the coldest among the South Adriatic cores, with mean values around 17°C and a pronounced cold event around 900 CE. Thereafter, SST oscillations seem to dampen (Fig. 3b, brown curve).

  study presents unprecedented high-resolution reconstruction of SSTs and paleoenvironments from NW Mediterranean and South Adriatic cores covering all or part of the past 3000 years. The SST records from the Gulf of Lion (GoL) and Ligurian Sea revealed a long-term cooling culminating during the Dark Ages Cold Period (DACP), that reversed at the onset of the Medieval Climate Anomaly (MCA), superimposed to multi-decadal to centennial scale variability reflecting atmospheric forcing from mainly East Atlantic (EA) and North Atlantic Oscillation (NAO). SSTs in the South Adriatic Sea (SAS) consistently reflected near-shore to open sea site influences. They show contrasting strong centennial time-scale variability, but no clear long-term trend. We demonstrated the impact of regional atmospheric and oceanic circulation features. Notably, we discuss the role of the BIOS dynamics on the SSTs centennial scale variability under prevailing positive NAO state through inflowing waters into the SAS. We also show the impact of Italian river discharge on the variability of coastal surface water properties through the WAC. Based on the ACL 27-33 , we could infer no major change in vegetation type in the Var and SAS sediments over the studied time period. However, terrestrial biogenic inputs revealed a strong impact of human activities on soil erosion and export of land-derived material to the SAS.

Figure captions Figure 1 :

 captions1 Figure captions

Figure 2 :

 2 Figure 2: (a) Total Alkali/Silica (TAS) classification diagram (Le Maitre, 2005) with the composition of the studied tephras, (b) CaO+FeO vs Na 2 O/K 2 O and (c) FeO vs CaO variation diagram of the analyzed tephras. The average compositional fields of possible proximal counterparts are reported for comparison. SEM-EDS data for AP1-AP2, Pompei, Pollena (dashed line) and 1631 AD from Santacroce et al. (2008). SEM-EDS data for Pollena (full line) from Rolandi et al. (2004).

Figure 3 :

 3 Figure 3: (a) Alkenone SSTs at the KESC9-14 core (NW Mediterranean). (b) Alkenone SSTs at the INV12-15 (Dark Cyan curve), CSS00-07 (Orange curve) and SW104-ND-14Q (Wine curve) cores (SAS). For (a) and (b), colored horizontal dashed lines represent the mean annual SST at each core site. (c) TERR-alkane abundances at the KESC9-14 core. (d) TERR-alkane abundances at the INV12-15 (Dark Cyan curve), CSS00-07 (Orange curve) and SW104-ND-14Q (Wine curve) cores. (e) Changes in ACL 27-33 in the KESC9-14 (Navy curve), INV12-15 (Dark Cyan curve), CSS00-07 (Orange curve) and SW104-ND-14Q cores (Wine curve). 50 years binning is applied to all signals to reduce the effect of proxy reconstruction error (Dark lines). Diamonds indicate the control points used for the age models, at 1σ uncertainty for the 14 C dates. Vertical dashed lines represent boundaries of historical periods. LBA: Late Bronze Age, RP: Roman Period, DACP: Dark Ages Cold Period, MCA: Medieval Climate Anomaly, LIA: Little Ice Age, IP: Industrial Period.

Figure 4 :

 4 Figure 4: (a) Spatial field of annual mean SSTs (1955-2012) from World Ocean Atlas database (Locarnini et al., 2013). Main winds blowing on the NW and central/eastern Mediterranean are shown by blue arrows. (b) Monthly averages SSTs at the studied core sites from World Ocean Atlas database (https://data.nodc.noaa.gov/las/getUI.do). (c) Comparison between reconstructed and instrumental SSTs from Kaplan et al. (1998) in the South Adriatic Sea over the Industrial Era.

Figure 5 :

 5 Figure 5: Regional response of Mediterranean SSTs to climate variability over the last 3000 years. (a) Alkenone SSTs at the KSGC-31_GolHo-1B core (GoL, Jalali et al., 2016 ; Sicre et al., 2016). (b) Alkenone SSTs at the KESC9-14 core (this study). (c) Alkenone SSTs at the INV12-15 (Dark Cyan curve), CSS00-07 (Orange curve) and SW104-ND-14Q (Wine curve) cores (SAS, this study). (d) The winter NAO index from the palaeo-reconstruction by Trouet et al. (2009) (Dark Cyan) and Olsen et al. (2012) (Navy). 50 years binning is applied to all signals to reduce the effect of proxy reconstruction error (Dark lines). Diamonds indicate the control points used for the age models, at 1σ uncertainty for the 14 C dates. Vertical dashed lines represent boundaries of historical periods. LBA: Late Bronze Age, RP: Roman Period, DACP: Dark Ages Cold Period, MCA: Medieval Climate Anomaly, LIA: Little Ice Age, IP: Industrial Period.

Figure 6 :

 6 Figure 6: SSTs variability in the Southern Adriatic Sea. (a) Alkenone SSTs at the SW104-ND-14Q core. Shaded grey bands broadly highlight the major colds spells in the open SAS.(b) Po River flooding probability[START_REF] Camuffo | The analysis of two bi-millennial series: Tiber and Po river floods[END_REF]. (c) Alkenone SSTs at the CSS00-07 core. Shaded blue bands represent the two major colds spells in CSS00-07.(d) Temperature in the central Alps (Spannagel Cave;[START_REF] Mangini | Reconstruction of temperature in the Central Alps during the past 2000 yr from a δ 18 O stalagmite record[END_REF]. 50 years binning is applied to all signals to reduce the effect of proxy reconstruction error (Dark lines).

Figure 7 :

 7 Figure 7: (a) Map of the Adriatic Sea showing the main surface and intermediate circulation and the strength of Western Adriatic Current and southern Adriatic Gyre during cyclonic mode of the BiOS favoring deep convection events in the open SAS. (b) The same for (a) but during anticyclonic mode of the BiOS favoring EMT events and weak convection in the SAS. WAC: Western Adriatic Current; SAG: southern Adriatic Gyre; ISW: Ionian Surface Water;

  14 C dates from local reservoir age, calculated by[START_REF] Piva | Late-Holocene climate variability in the Adriatic sea (Central Mediterranean)[END_REF] using two nearby sites in the western Adriatic Sea from the Calib 5.0.2 database.210 Pb was measured downcore until to detect negligible 210 Pb excesses ( 210 Pb xs ) (TableS2). The activities of 210 Pb, 226 Ra and 137 Cs were

  cryptotephras identified by magnetic susceptibility and 210 Pb dates performed on the first 11 cm (TableS1, S2 and S3). Magnetic susceptibility measurements were performed onboard the Urania R/V CNR after coring, and 210 Pb and 137 Cs measurements were made at EPOC laboratory, Bordeaux (France). The 210 Pb xs profile presents high surface values, followed by a downcore exponential decrease from which it was possible to estimate a mean sedimentation rate of 0.05 cm yr -1 . Based on the 210 Pb chronology, we calculated that the first 11 cm of the core range from 1793±22 to 2003±1. Glass fragments from cryptotephras were analyzed with SEM-Energy Dispersive Spectrometric (EDS) technique using a JEOL JSM-5310 at DiSTAR-University of Napoli Federico II through Oxford Instruments Microanalysis Unit, equipped with an INCA X-act detector. Full details of the

(Table

S1

). A third core, SW104-ND-14Q (41.

26°N, 17.61°E; 116

cm long, Fig. 1), was recovered in the open southern Adriatic Sea at 1013 m water depth. The age-depth model of the core is based on three analytical procedure can be found in Morabito et al. (2014). The samples selected to characterize the three cryptotephras were taken at 15, 60-62 and 79 cm depth b.s.l. and were labeled ND14A-15, ND14A-60, ND14A-62 and ND14A-79, respectively. They are generally represented by fine ash made up of loose crystals (feldspar, clinopyroxene and biotite), a minor glass fraction (generally scoria and micropumice, pale honey vesicular glass shards for sample ND14A-15) and lithic fragments, mostly lava types. Leucite occurs as phenocrysts on dark scoria fragments (ND14A-60 and ND14A-62) and lithics (ND14A-79). The results of chemical analyses are reported in Table

Acknowledgements

This work was financially supported by the MISTRALS/PaleoMex program and by the Project of Strategic Interest NextData PNR 2011-2013 (www. nextdataproject.it). Lionel Savignan is thanked for his participation in the biomarker analysis. Radiocarbon datings for core KESC9-14 have been funded by Institut Carnot Ifremer-EDROME (grant A0811101).

We also thank the Holocene North-Atlantic Gyres and Mediterranean Overturning dynamic through Climate Changes (HAMOC) project for financial support. The biomarker data presented here are available in the supporting information.


Leonardo Lalicata

Augusto Desideri

Francesca Casini

Luc Thorel

Luc Thorel Laterally

Laterally loaded pile in unsaturated soils: a numerical study

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Pile foundations are frequently used in geotechnical engineering problem to hold the combination of axial forces, lateral forces and concentrated moment. In some applications, the lateral component of external forces can be crucial in pile design, i.e., tall structures under wind load, bridge abutments, earthretaining structures, wind turbines, extreme loads such as emergency stop of a high-speed train on a viaduct. Usually the design of this foundation is not focused on ultimate conditions, both for soil and for pile cross-section, but it aims at ensuring the serviceability of elevated structures.

Starting from the 1950s, numerous research studies, both numerically and experimentally, have been performed on laterally loaded piles. In the early theoretical works, the soil is assimilated by a system of discrete springs with a constant sub-grade reaction. In these methods, the inclusion of non-linearity effects led to the so-called "p-y" methods developed by [START_REF] Reese | Analysis of laterally loaded piles in sand[END_REF][START_REF] Reese | Field testing and analysis of laterally loaded piles in stiff clay[END_REF]. Centrifuge tests on laterally loaded piles greatly enriched the data benchmarks, allowing validating the theoretical design methods for various soil conditions: [START_REF] Khemakhem | Static and cyclic lateral pile behaviour in clay[END_REF] have studied the soil pile interaction in fine graded soils varying the clay over-consolidation ratio.

The elastic continuum approach allowed considering the soil continuity in the load transfer mechanism. Major research contributions in this field can be found in the works of [START_REF] Banerjee | The behaviour of axially and laterally loaded single piles embedded in nonhomogeneous soils[END_REF], [START_REF] Randolph | The response of flexible piles to lateral loading[END_REF], [START_REF] Krishnan | Static and dynamic lateral deflexion of piles in non-homogeneous soil stratum[END_REF]. These studies were focused on better understanding the influence of geometrical (slenderness ratio L/D) and mechanical (relative pile-soil stiffness Ep/Es) parameters in the interaction problem. Randolph pointed out that significant pile deflection and bending moment are usually restrained in the first diameters of depth, and hence the pile-soil interaction is confined to near-surface soil. The so-called flexible piles are elements that do not involve the whole length in holding up the concentrated force or moment applied at the head. In practice, most of the piles have a flexible behaviour: in these conditions, the pile response strongly depends on the relative stiffness Ep/Es, evaluated in the significant volume of soil.

The significant soil volume, therefore, can be above the water table and hence in partial saturation condition. Several experimental studies have highlighted the suction influence on stiffness both at small strain levels [START_REF] Vassallo | Effects of net stress and suction history on the small strain stiffness of a compacted clayey silt[END_REF]) and medium deformation levels [START_REF] Casini | Interpretation of the behaviour of compacted soils using camclay extended to unsaturated conditions[END_REF][START_REF] Casini | Application to a compacted soil of a Cam Clay model extended to unsaturated conditions[END_REF].

The results of a preliminary numerical study about the influence of partial saturation in soil-pile interaction under lateral loading are presented in this paper.

Laterally loaded pile in unsaturated soils: a numerical study

F. Casini

Dipartimento di Ingegneria Civile e Ingegneria Informatica, Università` degli Studi di Roma "Tor Vergata", Rome, Italy.

L. Thorel

IFSTTAR, GERS Department, Geomaterials and Modelling in Geotechnics Laboratory, Bouguenais, France.

ABSTRACT: The influence of partial saturation on laterally loaded piles response has been studied performing three-dimensional simulation using a finite element code. The analysis simulated a single vertical floating pile embedded in a normally consolidated silty clay, subjected to a static horizontal force applied at pile head.

The study aims at exploring the influence of the relative position of water table with respect to the pile length. The hydro-mechanical propriety of soil is kept constant. The constitutive model adopted to simulate the soil stress-strain response is an extension of modified Cam-Clay model that considers the evolution of the yielding surface due to variation of saturation degree. The model is formulated in terms of modified Bishop effective stress, which allows switching continuously from saturated conditions to unsaturated conditions and vice versa. The modified formulation of the hardening parameter allows to correctly reproduce the volumetric behaviour observed in wetting and drying path.

FINITE ELEMENT MODEL

Geometry and boundary condition

A numerical model of a single vertical floating pile (L=24m, D=0.8m) embedded in a normal consolidated soil layer is carried out using the finite element code ABAQUS/standard. The problem must be studied with three-dimensional analysis. Due to the symmetry along the vertical plane containing the horizontal load, only half of the problem is modelled, as shown in Figure 1. Both soil and pile are modelled as three-dimensional solid continuum porous elements; beam elements with null stiffness (Young Modulus, E=1 kPa) are tied to the central nodes of the pile, in order to efficiently obtain the bending moment profile along the pile length. The soil follows the simple Cam Clay Model adapted to unsaturated conditions, while the pile is assumed to behave as an elastic material (Ep=36.0 GPa; =0.2).

Since the pile is modelled as a porous material, the frictional behaviour of the soil-pile interface is still governed by effective stress even above the water table, a value of =' is adopted, both for lateral interface and base interface.

Nodal displacements are fixed in the X, Y and Z directions at the base of the finite element mesh. The displacements at the lateral boundaries are constrained in the normal directions.

The pile head is at the ground surface level and no-load eccentricity is applied. A mesh refinement is adopted nearby the pile-soil interface both on lateral surface and at pile tip.

Mechanical soil behaviour

Mechanical soil behaviour is analysed with the Modified Cam Clay Model extended to partial saturation, [START_REF] Tamagnini | An extended cam-clay model for unsaturated soils with hydraulic hysteresis[END_REF]). The model is formulated in Bishop effective stresses, where the weighing factor  it is assumed equal to saturation degree:

    ' ij ij ij a r a w ij u S u u      (1)
where ij is the total stress; Sr is the saturation degree; (ua-uw) is the matric suction and ij is the Kronecker delta. The MCCM is an elastic-plastic hardening model widely used to describe the stressstrain response of fine-graded soils [START_REF] Soranzo | Face stability of shallow tunnels in partially saturated soil: centrifuge testing and numerical analysis[END_REF][START_REF] Rotisciani | Hydromechanical behaviour of an embankment during inundation[END_REF]. The flow rule is associated, yielding results both by the development of positive volumetric plastic strain and by the increase of saturation degree:

' ' ' p c c v c r v p p bp S       (2)
where, v is the specific volume;  and  are respectively the non-dimensional slopes of the normal consolidation line NCL and the unloading-reloading line URL; v p is the variation in volumetric strain; b is the mechanical parameter controlling the relative position of the unsaturated NCL at the current saturation degree with respect to the saturated one. The explicit dependence of yielding parameter (p'c) from saturation degree fairly allows reproducing the increase in apparent pre-consolidation pressure and collapse upon wetting observed in unsaturated soil behaviour. The failure criterion follows a pure frictional law: '

f q Mp  (3)
where, M is the slope of failure envelope in the invariant plane.

As the explicit formulation of soil water retention curve is required, the simple Van Genuchten model has been adopted:

    1 1 m r r res r sat r res n S S S S s          (4)
where, , n, m are parameters controlling the shape of the function. Classical values of silty clay soils are adopted. The Hydro-mechanical parameters of the model are summarised in Table 1.

Model validation

A similar model was used with satisfactory results in order to examine the results of an experimental campaign focused on pile response to horizontal loading in unsaturated soils [START_REF] Lalicata | Effect of saturation degree on the mechanical behaviour of a single pile subjected to lateral forces[END_REF]) developed in geotechnical centrifuge at the LCPC of Nantes [START_REF] Rosquoët | Lateral cyclic loading of sand-installed piles[END_REF]). In the centrifuge tests, the influence of suction was studied for a short rigid pile, embedded in a statically compacted silty soil (B-grade kaolin). The test was performed at 100g; the pile (tubular cross section, D=12mm; thickness t=1 mm) with an embedded length of 150 mm, was loaded both in partially saturated condition (water table zw at 70 mm from ground level) and in fully saturated condition. As for rigid piles, compared to saturated condition, pile head displacement and bending moment strongly reduces in unsaturated soils. For example, the comparison in terms of load deflection curves (at the model scale) for the two different hydraulic conditions is pointed out in Figure 2: black dots refer to experimental data obtained in unsaturated conditions while grey diamonds refer to data obtained in saturated conditions. The solid line represents the model prediction for the two cases. As previously mentioned, the model fairly reproduces the observed behaviour even for significant differences in the soil state conditions. Only for saturated conditions, the differences between model prediction and experimental data can be related to the uncertainties both on consolidation degree and void ratio distribution before the loading step and drainage conditions during loading (Lalicata 2017).

RESULTS AND DISCUSSION

This paper focuses on the influence of different water table elevations on the response of a free-head flexible pile subjected to lateral loading. A single slender pile (L/D = 30) with an elevated value of relative stiffness evaluated for fully saturated conditions (Ep/Es = 1.9×10 4 ) has been studied. In order to shows the results, convenient nondimensional parameters have been adopted. According to [START_REF] Krishnan | Static and dynamic lateral deflexion of piles in non-homogeneous soil stratum[END_REF], the geometrical variables are normalized by the diameter D, while the mechanical ones are normalized by soil modulus Es in saturated conditions. When the water table is at a ground level, the soil stiffness increases proportionally with depth. [START_REF] Randolph | The response of flexible piles to lateral loading[END_REF] found that, for usual pile-soil stiffness ratios the significant volume involved in the interaction problem can be preliminary stated at 10D of the depth. Considering that the most superficial soil layers are the most important, in this study the soil modulus Es used to define the relative soil-pile stiffness has been evaluated at z = 2.5D and therefore, considered representative of the stiffness in the significant volume.

Following the modified Cam Clay Model formulation, soil modulus Es is:

    ' ' 3 1 2 ' 3 1 2 ' vp Es E         (5)
In the simulations, the water table position, zw, ranges from zero to 24 m and the ratio zw /D from zero to 30.

Each analysis has been performed in several steps. As the soil has reached the equilibrium with the geostatic gravity load and the imposed hydraulic and boundary conditions, then the so-called "wishin-place" pile installation has been carried out. As a last step, the horizontal force has been increased monotonically in drained condition for soil. This loading step has been modelled with fully coupled analysis.

In Figure 3, the horizontal load H is plotted in relation to the head displacement y/D, for different elevations of water table, zw/D. As expected, initial stiffness increases for deeper water table level. Nevertheless, the increment of partial saturation positive effects becomes gradually less important, as zw/D increases.

The stiffness gain observed for increasing values of zw /D, can be explained by the increment of mean effective stress p', induced by partial saturation as in Equation (1) that is controlled, at the same time, both by suction (increasing with zw /D) and saturation degree (decreasing with the position of water table ).

The load-deflection curves obtained in unsaturated condition show higher load compared to the saturated one for any displacement value. The curves exhibit a non-linear trend due to progressive soil yielding induced by load increases. Due to the aforementioned p' increment, the operative soil stiffness in unsaturated conditions decays slowly compared to the saturated one in a given displacement range.

Figure 3. Influence of water table on the load deflection curves.

Although the system response is strongly dependent on operative stiffness, the significant influence of partial saturation can be observed also in the elastic soil modulus (Es) distribution with depth (Fig. 4).

With regard to the fully saturated distribution, black solid line in Figure 4, the distributions of soil modulus exhibit a non-zero values at z/D=0. Above the water table, the slope of Es depends on Sr: great variation of the parameter gives a steep slope to stiffness, when the saturation degree is close to 1, the slope is the same as observed in saturated conditions. In Figure 5, the normalized bending moment distribution M/HD is pointed out along normalized depth z/D, for different water table position (zw/D). The curves shown in Figure 5 refer to a very low load (H = 30 KN), therefore, it may be assumed as a low plasticization level in the soil, even for fully saturated conditions. As soil stiffness grows, the system flexibility increases, leading to a significant reduction of maximum bending moment and that also moves towards the ground surface: for zw/D = 0, the maximum moment is located at 5.5D while for zw/D = 30, it is reduced to 3.8D. As outlined by [START_REF] Randolph | The response of flexible piles to lateral loading[END_REF] and [START_REF] Krishnan | Static and dynamic lateral deflexion of piles in non-homogeneous soil stratum[END_REF], the critical length is a function of relative stiffness: here, due to the increase in soil stiffness induced by partial saturation the critical length decreases from 18D (zw/D = 0) to 14D (zw/D = 10-20-30). The same considerations made for higher loads emphasize the differences between saturated and unsaturated conditions related to the different yielding distribution in the soil. With respect to saturated soil, the plasticization level is always smaller in unsaturated conditions for any load; hence the differences pointed out in Figure 5 increase as load increases. For low deformation levels, it is still possible to refer to an elastic interpretation. The effects of partial saturation can be observed by looking at the trend of head displacement and maximum bending moment in function of zw/D, as shown in Figure 6

CONCLUSIONS

This study analyses the pile behaviour under lateral loading in saturated and unsaturated porous media using a hydro-mechanical model formulated in terms of Bishop's effective stress and implemented in the commercial code Abaqus/Standard. The study has been carried out by means of a three-dimensional finite element model that considers the main features of laterally loaded pile and partially saturated soils. The soil stress-strain response has been modelled with the Modified Cam Clay Model extended to unsaturated conditions; the SWRC is described by the Van Genuchten equation with the typical parameter of a silty clay.

The study highlights the importance of an increase of stiffness and strength induced by suction above the water table in the pile response. For freehead flexible piles in working condition, a marked reduction of pile head displacement and bending moment have been observed even for low depths of the water table. Due to increasing stiffness, the critical length reduces, and the soil-pile system becomes more flexible. The results can be extended to any pile with greater slenderness ratio but same initial relative stiffness. As load increases, the observed differences grow, both for head displacement and maximum bending moment. At the same load, the maximum bending moment is smaller in unsaturated soil conditions and it is closer to the pile head.

Figure 1 .

 1 Figure 1. Finite element mesh.

Figure 2 .

 2 Figure 2. Load-deflection curve at model scale (Lalicata 2017).

Figure 4 .

 4 Figure 4. Soil modulus, Es distribution.

Figure 5 .

 5 Figure 5. Normalized Bending moment for different water table positions.

  moment[START_REF] Krishnan | Static and dynamic lateral deflexion of piles in non-homogeneous soil stratum[END_REF]). The parameters have been evaluated for y/D = 0.01, and for soil modulus, the saturated values at z = 2.5D has been used.Switching from saturated condition to zw/D = 5, it can be observed that a steep reduction of 56% of head displacement after witch the displacement smoothly decreases up to a reduction of 80% for zw/D = 30. The variation of maximum bending moment with zw/D, Figure6(b) shows the same trend qualitatively. The bending moment appears to be less affected by partial saturation than lateral displacement. With respect to saturated condition, zw/D = 0, the reduction ranges from 40% (zw/D = 5) to 50% (zw/D = 30).

Figure 6

 6 Figure 6. a) Head displacement with respect to the water table position; b) Maximum normalized bending moment in function of water table position.

  .M.Lalicata & A. Desideri Dipartimento di Ingegneria strutturale e geotecnica. Sapienza Università di Roma. Rome, Italy.

L

Table 1 .

 1 Mechanical and hydraulic parameters.

	Mechanical	Hydraulic
	Parameters	Parameters
		0.1	Srsat	1
		0.02	Srres	0
	N0	2.38		0.02
	'	0.2	n	1.7
		1.07	m	0.41

b 5 Ksat (m/s) 1.0E-07


Mouna Labiadh
email: mouna.labiadh@univ-lyon1.fr

Christian Obrecht
email: christian.obrecht@insa-lyon.fr

Catarina Ferreira Da Silva

Parisa Ghodous
email: parisa.ghodous@univ-lyon1.fr

On the suitability of Data Selection for Cross-building Knowledge Transfer

Keywords: Data selection, domain generalization, knowledge transfer, data-driven modeling, energy consumption modeling

Supervised deep learning has achieved remarkable success in various applications. Such advances were mainly attributed to the rise of computational powers and the amounts of training data made available. Therefore, accurate large-scale data collection services are often needed. Once representative data is retrieved, it becomes possible to train the supervised machine learning predictor. However, a model trained on existing data, that generally comes from multiple datasets, might generalize poorly on the unseen target data. This problem is referred to as domain shift. In this paper, we explore the suitability of data selection to tackle the domain shift challenge in the context of domain generalization. We perform our experimental study on the use case of building energy consumption prediction. Experimental results suggest that minimal building description is capable of improving cross-building generalization performances when used to select data.

I. INTRODUCTION

Machine learning is dramatically improving a wide range of fields, from image and natural language processing, to genomics and robotics. However, machine learning models performance depends heavily on the representation of data they are provided. This makes feature engineering the key determining factor and the most challenging step in the machine learning pipeline. Deep learning [START_REF] Goodfellow | Deep learning[END_REF] alleviates this central challenge by automatically learning meaningful representations out of multiple simpler representations. The recent success of deep learning is mainly attributed to the rise of computational powers and the massive amount of available data used for training.

A powerful machine learning model should rely on insightful utilization of relevant data. Hence, scalable and accurate data collection techniques are considered as key success factors. Data collection for machine learning [START_REF] Roh | A survey on data collection for machine learning: a big data-ai integration perspective[END_REF] generally consists in acquiring new relevant datasets [START_REF] Chapman | Dataset search: a survey[END_REF], labeling acquired data samples, improving the quality of existing data or the training of an existing model. A classic data collection workflow as described in [START_REF] Roh | A survey on data collection for machine learning: a big data-ai integration perspective[END_REF] would start by checking if there is enough training data. Otherwise, we must either acquire relevant datasets, or generate an appropriate dataset. Once data is collected, we can choose to either label individual data samples within these datasets, to improve the quality of existing labels that may be noisy and biased, or to select an existing model and improve it using transfer learning techniques.

We seek to explore a main challenge of using existing data, the domain shift problem. Domain shift [START_REF] Sugiyama | Mixture regression for covariate shift[END_REF] causes models trained on one source domain to generalize poorly when applied to a target domain with mismatching data distribution. Consequently, learning scenarios in which we do not have enough and exactly representative training data of the intended testing context are heavily penalized. Proposed approaches addressing this challenge are mainly classified into domain adaptation and domain generalization. Domain adaptation [START_REF] Bousmalis | Domain separation networks[END_REF] [START_REF] Rozantsev | Beyond sharing weights for deep domain adaptation[END_REF] utilizes labeled source data and unlabeled or sparsely labeled target data to obtain a well-performing model on the target domain. However, in several cases, the target data are not available. Domain Generalization (DG) [START_REF] Blanchard | Generalizing from several related classification tasks to a new unlabeled sample[END_REF] [START_REF] Muandet | Domain generalization via invariant feature representation[END_REF] addresses such cases by utilizing multiple source domains. This paper considers the domain generalization area of research, which aims to train accurate models that perform well on unseen target domains, by leveraging knowledge from different but related source domains. For this purpose, we propose to explore the suitability of data selection in the context of domain generalization. This approach consists in selecting representative data from domains that are similar to the target domain on which we do not dispose of enough data. This similarity is based on available minimal domains descriptions.

Particularly, we consider the problem of data-driven building energy modeling, which aims to accurately predict future energy use from specific measures. Energy demand prediction plays an integral part in the efficient planning and operation of power systems. Prior studies in this framework require labeled data of the building in question, such as historical data, physical parameters of the building, meteorological conditions, or information about the building occupancy, in order to train a reliable building energy consumption model. Our approach goes beyond state-ofthe-art methods and proposes to transfer knowledge across multiple sources buildings while using minimal information about the target building, such as in the case of renovated or newly-built buildings. Our challenge is to build a model that accurately predicts the future energy consumption of a previously unseen building, given one or many training datasets.

To tackle these challenges, we investigate the suitability of data selection mechanism for cross-building domain generalization. To the best of our knowledge, our work is a first attempt to model a target building with incomplete or minimal information about it, and thus tackling the data unavailability problem by transferring knowledge from auxiliary buildings. Reported energy prediction approaches [START_REF] Amarasinghe | Deep neural networks for energy load forecasting[END_REF][10] usually rely on detailed information about the target building, e.g. historical consumption data, meteorological data, occupancy information, etc.

The remainder of this paper is structured as follows. Section II presents a classification of related works on domain generalization. Section III provides an overview on our proposed approach and the architecture of the model we utilize. Section IV depicts the experimental setup and summarizes results. Section V discusses experimental findings, and finally in Section VI, we draw conclusions and present an outlook and suggestions for future research.

II. APPROACHES TO DOMAIN GENERALIZATION

Proposed domain generalization approaches generally rely on the assumption that source domains and unseen target domains share common features that can be extracted. Hence, they seek to learn a domain agnostic representation or model. Domain generalization approaches proposed in literature may be roughly classified into four categories; (1) Data representation based techniques [START_REF] Muandet | Domain generalization via invariant feature representation[END_REF][11] that seek to learn domain agnostic representation that captures similarities across domains and where the domain discrepancy is minimized, (2) Model selection techniques [START_REF] Xu | Exploiting low-rank structure from latent domains for domain generalization[END_REF] that aim to select the most relevant domain to a target sample and use its corresponding model, (3) Model based techniques [13][14] that aim to find a model architecture and algorithm that generalizes well on unseen target domains, and (4) Metalearning based technique [START_REF] Li | Learning to generalize: Meta-learning for domain generalization[END_REF] that relies on a model agnostic training procedure that trains any given model so that it mitigates domain shift between domains.

Muandet et al. [START_REF] Muandet | Domain generalization via invariant feature representation[END_REF] propose to learn new domain invariant feature representations by minimizing the dissimilarity across domains via domain-invariant component analysis and a kernel-based optimization algorithm. Ghifary et al. [START_REF] Ghifary | Domain generalization for object recognition with multitask autoencoders[END_REF] propose a Multi-Task Auto-Encoder (MTAE) that extends auto-encoders into a model that jointly learns to perform self-domain data reconstruction and between-domain data reconstruction. Xu et al. [START_REF] Xu | Exploiting low-rank structure from latent domains for domain generalization[END_REF] use learned low-rank exemplar-SVMs, which can be defined as a linear Support Vector Machine (SVM) classifier trained on a single positive training instance and all negative training instances, for both domain adaptation and domain generalization. For domain generalization, the authors propose to either equally fuse all exemplar classifiers, or use the exemplar classifiers in the latent domain which the target data more likely belongs to. Given multiple source datasets/domains, Khosla et al. [START_REF] Khosla | Undoing the damage of dataset bias[END_REF] propose an SVM based approach, in which the learned weight vectors are common to all datasets. Li et al. [START_REF] Li | Deeper, broader and artier domain generalization[END_REF] proposed a low-rank parameterized convolutional neural network model for end-to-end DG learning. Li et al. [START_REF] Li | Learning to generalize: Meta-learning for domain generalization[END_REF] propose a Meta-Learning Domain Generalization (MLDG) approach. It consists in a model agnostic training procedure that can improve the domain generality of a base learner. This procedure is based on synthesizing virtual training and virtual testing domains within each mini-batch. The metaoptimization objective consists in minimizing the loss in the training domains, while simultaneously improving the virtual testing loss.

Our work is more related to the model selection techniques. We borrow the per-domain model building idea described in [START_REF] Xu | Exploiting low-rank structure from latent domains for domain generalization[END_REF]. However, we select domains rather than models and combine their respective data to form a representative training set. We assume in our case that we dispose of a minimal description of the target domain that will allow us to define our data selection criteria, such as building typology, and year of construction.

III. THE PROPOSED SYSTEM

We present an overview of our methodology pipeline in Figure 1. We propose a cloud-based system for relevant existing data collection and reuse in predictive modeling tasks. Our system main objective is to train a building energy model for an unseen target building based on its description. The training data is obtained through a data selection service-oriented workflow. Our approach consists in training a model that simultaneously minimizes the prediction error and the domain discrepancy between the collected source domains and the unseen target domain. The data selection service is responsible for preparing a training dataset that is relevant against the target domain description. In our special case, target domain description concerns high-level information about the target building we seek to model, e.g. typology, year of construction, location, etc. Once data is selected, the final training dataset is prepared and provided to the model training component. In this study, we attempt to explore the suitability of similar data selection in the context of building energy consumption modeling. The detailed description of the data selection workflow is beyond the scope of this paper.

A. Cross-building Knowledge Transfer

We explore the suitability of data selection for crossbuilding knowledge transfer. We start by training a model that captures energy use dynamics for each building, and then test its generalization performance across other unseen buildings. Our objective is to study the possibility to select representative buildings data based on available target building metadata. For this purpose, we start by identifying similar buildings based solely on their respective descriptions. We then perform cross-building knowledge transfer; we build one model for each building and study its transfer-ability across other unseen buildings. Our aim is to investigate whether cross-building performances are correlated with buildings' descriptions.

Our model learns to predict future building-level aggregate energy consumption based on energy consumption history and both past and future climate data. In reality, a wide range of factors may impact the energy use in buildings, such as occupants behavior, building typology, construction materials, etc. In this work, we focus on the meteorological data factor by feeding our model with past and future climate data along with the aggregate past energy consumption. The motivation behind utilizing both future and past climate data is to attempt to capture the relationship between climatic changes and building's energy load profile fluctuations.

B. Model Architecture

Figure 2 shows the architecture of our learning model. We consider a unidirectional Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) as our supervised predictor. RNNs [START_REF] Lipton | A critical review of recurrent neural networks for sequence learning[END_REF] are a powerful class of supervised machine learning models that are capable of modeling sequential data. LSTM [START_REF] Hochreiter | Long shortterm memory[END_REF] is a RNN architecture that helps to prevent the effect of vanishing and exploding gradients [START_REF] Pascanu | On the difficulty of training recurrent neural networks[END_REF] often encountered in recurrent networks. LSTM offers the ability to selectively pass information across sequence steps while processing sequential data one element at a time.

Our model is trained to predict daily energy consumption of subsequent week. As input, we provide our model with daily energy consumption of the previous week and climate time series of the subsequent week.

Our training set X = {(x (1) , y (1) ), (x (2) , x (2) ), ...} is structured into time-based sequences of fixed length. Input sequences are denoted by (x (1) , x (2) , ..., x (T ) ) where T denotes the sequence length, and each feature vector x (t) ∈ R n ∀t = 1..T is of size n. Feature vectors are composed of current week's aggregate energy consumption, air temperature, average horizontal solar irradiance, wind speed, and the same weather features as aforementioned for subsequent week. Similarly, target sequences are denoted by (y (1) , y (2) , ..., y (T ) ), where y (t) ∈ R is a real vector denoting the energy consumption at future time steps. The goal of the model is to predict future energy consumption y (t) from the input feature vector x (t) .

The architecture of the network is composed of several hidden layers. It consists of one or more LSTM layers followed by one or more fully-connected layers. The output layer is a fully-connected layer with a linear activation function. The model is trained using the Root Mean Squared Error (RMSE). We also use the batch normalization mechanism [START_REF] Ioffe | Batch normalization: Accelerating deep network training by reducing internal covariate shift[END_REF] to address the internal covariate shift problem usually encountered in deep neural networks training. Training phase were conducted using Backpropagation Through Time (BPTT) optimization algorithm in the context of LSTM networks.

During our experimental study, we explore variants of this architecture to fine-tune its hyperparameters, e.g. number of fully-connected layers, number of LSTM layers, etc. We retain the architecture variant that yields the best crossdomain and in-domain generalization results.

IV. EXPERIMENTAL SETUP

We perform our experimental studies on the use case of building energy consumption prediction. Our system transfers knowledge from several buildings, to one target building on which we assume we are facing a data unavailability problem.

A. Dataset

The proposed solution is experimentally evaluated using REFIT Electrical Load Measurements dataset [START_REF] Murray | An electrical load measurements dataset of united kingdom households from a two-year longitudinal study[END_REF]. The dataset contains cleaned electrical consumption measurements for 20 UK households on aggregate and appliance level. For each household, the whole house aggregate loads and nine individual appliance measurements at 8-second intervals were collected continuously over a period of approximately two years. During monitoring, the occupants were conducting their usual routines.

In addition, climate data was also collected from a nearby weather station. Figure 3 highlights the differences of energy load profiles across a subset of four buildings in the REFIT dataset. Descriptions about each building comprised information related to occupancy (number, ages, sex, etc.), size, construction year, typology, and total number of appliances owned. In Figure 4, we illustrate the REFIT dataset description with a heatmap. We consider five descriptive features for each building; the number of occupants, the construction year, the number of appliances, the building type, and the size To depict similarities between buildings, we start by hierarchically clustering them based on the provided description vectors. Categorical data was one-hot encoded as a further pre-processing step. We use the Euclidean distance to compute pair-wise similarities. Clustering results are illustrated in Figure 5 by a dendrogram. The figure identifies a cluster of fourteen similar buildings, which is composed of the subset of the following buildings {1, 3, 4, 7, 8, 9, 10, 11, 13, 14, 16, 17, 18, 20}. Buildings 17 and 8 are identified as the most similar buildings in the dataset. Looking at their descriptions, they share the same number of occupants, building type, and construction year class. Building 17 also has only one more bedroom compared to building 8. Building pairs {9, 11}, and {16, 20} are also respectively identified as mutually similar.

B. Evaluation metrics

Our goal is to achieve a good generalization performance by accurately predicting short-term energy consumption of unseen buildings. Therefore, we assess our proposed model using the Root Mean Squared Error (RMSE). RMSE is defined as the square root of the average squared distance between prediction and ground truth, using the formula:

RM SE = 1 N N i=1 (y i -ŷi ) 2 ,
where y i and ŷi respectively denote the true value and the predicted value of the i-th data sample, and N denotes the size of the dataset.

C. Model Training

For each building, we use data between April 2014 and May 2015 for training. For cross-building evaluations, we use data between April 22nd, 2014 and June 1st, 2014. The whole dataset was scaled so all values will be between 0 and 1, using min-max normalization algorithm. The input and the output sequences are of length 7. The input corresponds to a 7-dimensional feature vector. Our network is composed of two hidden layers; one LSTM layer of size 256, and one fully-connected layer of size 128. The Rectified Linear Unit (ReLU) is used as the non-linear activation function for hidden layers. The output layer consists of a fully-connected layer with linear activation function. The fine-tuning of weights is done using Gradient Descent algorithm with an exponentially decaying learning rate ranging between 10 -3 and 10 -5 . Weights initialization follows a normal distribution with zero mean and standard deviation σ = 1, whereas biases are initialized to zeroes. The gradients are back-propagated through timestep batches of length 80. For the training epochs number, we have fixed 1000 as the maximum number of epochs. To avoid over-fitting, we have implemented an early stopping mechanism which breaks the training loop when training cost does not improve on the training set after 20 epochs.

D. Experimental Results

We trained 19 models for each building following the same process. One building (number 12) was not considered due to insufficient training data. Each model was tested on the remaining unseen buildings in order to study its crossbuilding transfer-ability. Figure 6 depicts the predictions errors of cross-building model transfers as a heatmap. We can visually identify two clusters within each of them generalization performances are high. These clusters are respectively composed of the following subsets of buildings {2, 3, 18, 19} and {5, 6, 7}. We also notice that buildings 13 and 14 are mutually similar and that models trained on buildings 10 and 17 generalize well when applied to them during inference mode. Furthermore, we can visually conclude that all trained models perform poorly when applied to building 15. Model trained on building 15 also has poor generalization performances when applied to the remaining unseen buildings.

We now seek to examine similar buildings based on these results; our assumption is that similar buildings models are transferable among each other. Hence, a model that is trained on a building i will generalize well when applied to a building j if buildings i and j are similar. We start by processing the experimental results matrix (Figure 6) to transform it to a distance matrix. For this purpose, we simply compute pairwise averages between each element at row i and column j and its corresponding element at row j and column i. Drawn clusters from this distance matrix are illustrated in Figure 7 using a dendrogram. We use the Euclidean distance to compute pair-wise similarities. V. DISCUSSION From Figure 5 and Figure 7, we can notice that buildings 8 and 17 which were the most similar based on their descriptions are clustered under the same cluster based on their cross-domain generalization errors. This means that models trained on building 8 will generalize well when applied to building 17 during inference mode, and vice versa. Similarly, the two sets of buildings {9, 11}, and {16, 20} are identified as similar in both clustering schemes; based on descriptions and cross-domain generalization errors. Furthermore, poor cross-domain generalization performances of building 15 (Figure 6) is explainable by its dissimilarity with the rest of buildings (Figure 5).

We may therefore suggest that buildings, that are judged similar based solely on their descriptions, do yield to good prediction results when performing cross-building knowl- edge transfer.

In the context of this study, we have leveraged a very restricted set of building descriptions, i.e. number of occupants, typology, size, etc. Therefore, we believe that richer and broader building description would help to select similar data more accurately and more reliably. Furthermore, and due to the large variety of building typologies and design, and uncertainties surrounding its environment and occupancy patterns, we consider that data selection approaches based on similarity metrics are important in order to perform large-scale and accurate cross-domain domain generalization.

VI. CONCLUSION AND PERSPECTIVES

This paper discusses the suitability of the data selection approach for cross-building knowledge transfer. Evaluation work was conducted on the case study of building energy consumption modeling. For this purpose, we have trained per-building models and studied their transfer-ability across other unseen buildings. Experimental results show that minimal building descriptions are capable of guiding domain generalization applications in the context of energy modeling, by identifying similar buildings. Overall, we believe our results confirm the suitability of data selection mechanisms that are based on similarities of building minimal descriptions.

As future work, we will investigate large-scale data selection service approaches for domain generalization. We also intend to extend our system by automating the data selection algorithm based on user queries. User queries will contain the description of the target building to which we want to transfer knowledge.

Figure 1 .

 1 Figure 1. Data selection approach for domain generalization.

Figure 2 .

 2 Figure 2. Architecture of the LSTM-RNN model used to predict daily energy consumption of a whole week.

Figure 3 .

 3 Figure 3. Monthly energy load profiles across buildings.

  . The number of occupants in the REFIT dataset varies from one to four occupants. The construction years of buildings are grouped into eight classes based on year intervals spanning from 1850 to post 2002. Three house types are present in the REFIT dataset; detached, semi-detached, and mid-terrace. Building sizes are computed based on number of bedrooms.

Figure 4 .

 4 Figure 4. Heatmap of the REFIT dataset description after pre-processing; Missing data in one column were replaced with the most frequent value in that column, categorical features (type) were label encoded, ordinal features (construction year class) were converted to ordinal integers, resulted values were scaled between 0 and 1.

Figure 5 .

 5 Figure 5. Dendrogram of the hierarchical clustering of REFIT households based on their descriptions. Clusters within which distance is below 70% of the maximal cluster-wise distance Categorical are colored in green. Features in the buildings feature vectors were one-hot encoded. The distance used was the Euclidean distance.

Figure 7

 7 identifies two main clusters, which are respectively composed of the following subsets of buildings {5,6, 7, 8, 10, 13, 14, 16, 17, 20, 16} and {1, 2, 3, 4, 9, 11, 15, 18, 19}.

Figure 6 .

 6 Figure 6. Heatmap of the experimental test errors; we trained 19 models, each of them on one single building. Each model was tested on each building. Th y-axis represents buildings on which each model was trained, the x-axis represents the buildings on which each model was tested. The evaluation metric was RMSE. Final results were scaled between 0 and 1. House number 12 was not considered due to insufficient training data.

Figure 7 .

 7 Figure 7. Dendrogram of the hierarchical clustering of REFIT households based on experimental cross-building prediction results. Clusters within which distance is below 70% of the maximal cluster-wise distance Categorical are colored in green and red. The distance used was the Euclidean distance.