Natasha Bahrani
email: natasha.bahrani@univ-eiffel.fr

Juliette Blanc
email: juliette.blanc@univ-eiffel.fr

Pierre Hornych
email: pierre.hornych@univ-eiffel.fr

Fabien Menant
email: fabien.menant@univ-eiffel.fr

Paulina Leiva-Padilla
email: paulina.leiva-padill@univ-eiffel.fr.

Paulina Leiva Padilla

Continuous remote monitoring of a motorway section using geophones

Keywords: remote road monitoring, geophones, pavement instrumentation and pavement engineering

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Flow chart 1. Introduction

For any flourishing society, having efficient roads and transport infrastructure is essential not only for the mobility of people and goods but also for the economic growth and better quality of life. Therefore, it is important to maintain a high level of service of the road network. Millions of euros are spent each year on the monitoring and maintenance of roads. Regular and preventive maintenance is essential to ensure a better performance of pavement structures. For this reason, there has been a growing interest in long term monitoring of roads by using nondestructive techniques in order to study the effect of traffic and changing environmental conditions on the pavement, and detect early its deteriorations.

Several non-destructive testing methods have been developed to assess the structural capacity of asphalt pavements. The most commonly used are static measurement methods like the Benkelman beam and impulse loading methods like the FWD [START_REF] Hadidi | Comparative study of static and dynamic falling weight deflectometer back-calculations using probabilistic approach[END_REF], which measure the vertical deflections induced in the pavement. These methods present the advantage of reproducing closely the loading conditions and the stress state in the pavement. Some other methods are also based on the vibro-acoustic response of the pavement [START_REF] Fedele | Instrumented infrastructures for damage detection and management[END_REF]. However, these techniques can only measure the deflection at discrete locations, and require interrupting the traffic. These techniques are also costly and the measurements are recorded infrequently, i.e., typically once every year or every two years. There is a need to develop solutions for monitoring road condition in a remote and continuous manner, without the need of road closures. Instrumentation with adequate technology for characterising the conditions and deterioration caused by heavy traffic is an active topic of research in the pavement-engineering field. Ease of installation and durability of the sensors, accuracy, measurement frequency and instrumentation cost are very important factors. The most widely used sensors for pavement instrumentation are strain gauges, which measure horizontal or vertical strains in pavement layers [3][4]. Gauges are well suited for monitoring the response of a given pavement layer with time [START_REF] Lajnef | Toward an integrated smart sensing system and data interpretation techniques for pavement fatigue monitoring[END_REF] [START_REF] Duong | Continuous strain monitoring of an instrumented pavement section[END_REF]. Some smart sensing technologies, based on piezoelectric self-powered sensors are also used for health monitoring of pavement systems [7][8].

This work focuses on the measurement of pavement deflections (vertical displacements), which appears as an interesting alternative or complementary solution to strain gauges. The deflection is a more global parameter, which allows to characterise, with only one sensor, the overall response of the pavement, and can also be used to back-calculate pavement layer properties, if sufficient information about the pavement structure is available. In addition, the deflection can be measured at the pavement surface (or very close to the surface), which makes installation of sensors on existing in-service pavements easier.

Instrumentation using geophones and accelerometers to measure the deflection is a recent method, that is gaining interest. Levenberg [START_REF] Levenberg | Inferring Pavement Properties using an Embedded Accelerometer[END_REF] used accelerometers to record the mechanical response of the pavement and to back calculate layer moduli. Arraigada et al [START_REF] Arraigada | Evaluation of accelerometers to determine pavement deflections under traffic loads[END_REF] used accelerometers and deflectometers to measure pavement deflections. They worked on procedures to convert the acceleration measurements into deflections and removing the drift and noise in the signal. Similarly in the work of Liu et al. [START_REF] Liu | Measurement and evaluation on deterioration of asphalt pavements by geophones[END_REF], geophones were used to estimate pavement deteriorations. The pavement response was analysed using neural network algorithms. Duong et al. [START_REF] Duong | Monitoring of pavement deflections using geophones[END_REF] also used geophones to identify the different heavy vehicle silhouettes and to measure vehicle speeds. They also tried to measure pavement deflections and proposed a procedure for correction of the geophone signals. Encouraging results, indicating good predictions of maximum deflections, have been obtained. However, the shape of the corrected deflection signals was not completely realistic, and could still be improved.

The study presented in this paper is the continuation of the work done by Duong et al [START_REF] Bahrani | Alternate method of pavement assessment using geophones and accelerometers for measuring the pavement response[END_REF] and describes the results of the monitoring of a real motorway section, under continuous traffic, using geophones. The motorway, called here Ax (for confidentiality reasons) was instrumented with geophones and temperature probes connected to a remote data acquisition system. A detailed description of the section, and a first analysis of the results, have been presented in [6][13]. The aim of this study is to re-analyse the geophone measurements; using a newly developed signal treatment procedure, and to explore different possibilities of interpretation of the deflection measurements. More precisely, the objectives of the work are:

• To evaluate the sensor's ability to measure the vertical deflections, in real field conditions;

• To monitor the deflections on the motorway site, and their evolution with time;

• To analyse the evolution of pavement layer moduli with temperature and traffic;

• To characterise the heavy vehicle traffic.

For this purpose, a laboratory study was first conducted, to test the response of the sensors to controlled displacement signals. [START_REF] Bahrani | Alternate method of pavement assessment using geophones and accelerometers for measuring the pavement response[END_REF].

The measurements made with the geophones were then validated by tests performed on the accelerated pavement testing facility of Université Gustave Eiffel. The data was processed using the same data treatment process and showed favourable outputs, when compared with deflections measured with a reference displacement sensor [START_REF] Bahrani | Inverse Analysis of Pavement Layer Moduli Based on Data Collected by Buried Accelerometers and Geophones[END_REF].

Finally, the geophone measurements made on the motorway section were analysed to explore different possibilities of interpretation of the deflection measurements.

Geophone signal treatment

Characteristics of the geophones used in the study

The geophones are of type GS11D 4000, with a sensitivity of 89.2V/m/s, a natural frequency of 4.5 Hz and a closing resistance of 47 kΩ. In the pavement, under the passage of each vehicle, the geophones deliver a voltage signal which is proportional to the vertical displacement velocity. In order to get the pavement vertical displacement (deflection), the signal needs to be integrated. However, previous studies have shown that a simple integration does not give realistic displacements. Figure 2 (left) shows a comparison of the integrated geophone signal (with a simple integration), with the theoretical deflection signal under a 5-axle semi-trailer vehicle, calculated with the French pavement design software ALIZE-LCPC [START_REF] Remaud | Alizé-LCPC Software : Analysis and structural design of pavement structures[END_REF] and obtained with a reference laser sensor. This software is based on the multi-layer linear elastic model of Burmister [START_REF] Burmister | The theory of stress and displacements in layered systems and applications of the design of airport runways[END_REF] in compliance with the French pavement design guide [START_REF] Corte | Design of Pavement Structures : The French Technical Guide[END_REF][18] [START_REF] Nf P | Dimensionnement structurel des chaussées routièresapplication aux chaussées neuves[END_REF]. The pavement layers are assumed to be homogeneous, isotropic and infinite in the horizontal plane.

The figure 2 shows that after a simple integration, the deflection signal obtained is not correct, probably because the transfer function of the geophone (voltage delivered by the geophone, for a given velocity) is strongly dependent on frequency.

Following this result, an original signal treatment procedure has been be developed, to obtain accurate deflection measurements. This procedure, developed using laboratory tests, is described in details in [START_REF] Bahrani | Alternate method of pavement assessment using geophones and accelerometers for measuring the pavement response[END_REF] and [START_REF] Bahrani | Pavement instrumentation for condition assessment using efficient sensing solutions[END_REF].

It includes the four following steps (see figure 1):

• Noise filtering, using an Infinite Impulse Response (IRR) high pass filter, with a cut off frequency depending on the vehicle speed.

• Integration to convert the velocity to vertical displacement.

• Amplification of the signal, as the signal amplitude is reduced after the filtering process.

• Finally, application of a Hilbert Transform, to eliminate the oscillations in the signal. This final treatment eliminates the positive peaks, and leads to a response close to the theoretical truck deflection.

Figure 1. Signal treatment method (example with geophone G1 (see Figure 3))

Step 1: Filtering the vertical velocity signal

Step In a second phase, the geophones were also tested in real field conditions, under moving wheel loads, on the accelerated pavement testing (APT) facility of Université Gustave Eiffel, to validate the treatment procedure. The field tests (with different speeds, loads and positions) are described in details in [START_REF] Bahrani | Pavement instrumentation for condition assessment using efficient sensing solutions[END_REF].

It was observed in the experimental tests that the filtering needs to be adjusted to the speed of the vehicle. The cut off frequency of the high pass filter was set to 8.5 Hz assuming a mean heavy traffic speed of 90 km/h (corresponding to a loading frequency around 13 Hz, based on the results of [START_REF] Bodin | Viscoelastic asphalt pavement simulations and simplified elastic pavement models based on an "Equivalent asphalt modulus" concept[END_REF] and [START_REF] Chupin | Determination of an equivalent elastic system to a multilayer viscoelastic structure: Application to the case of thick flexible pavement[END_REF], which established that 10 Hz is equivalent to a speed of 70km/h).

An amplification factor of 3 was used, to compensate the amplitude loss after filtering.

It can be seen on figure 2 (right) that the proposed processing leads to a realistic signal, both in shape and maximum displacement amplitude. This figure confirms the efficiency of the treatment which now can be applied to real field measurements.

Presentation of the instrumented site on motorway Ax

Pavement structure

The instrumented section is located on a French motorway. The rehabilitated pavement structure consists of 3 new layers, built in September 2013 on the existing, 20 years old, road base (Table 1). The three upper layers consists of a wearing course (very thin asphalt concrete, VTAC, standard NF EN 13108-2), a binder course (high modulus bituminous concrete, HMBC, standard NF EN 13108-1), and a base course (high modulus asphalt concrete, HMAC, standard NF EN 13103-1). The lower layers include a cracked road base asphalt material (GB), a cement treated sand layer and the subgrade. Table 1 summarizes the characteristics of the different

Instrumentation

The experimental section was instrumented with 4 geophones and 8 temperature probes. The temperature sensors are of PT100 type and the geophones are of type GS11D 4000. These geophones are of the same type as those used in the laboratory tests and accelerated pavement tests presented in [13] [20]. The geophones were installed in the slow lane, in the wheel path close to the emergency lane. The temperature sensors were positioned at the edge of the slow lane. Figure 3 shows the positions of the sensors in the pavement structure. They include:

• 8 temperature probes (2 probes located, very closed to each other, at the base of each bituminous layer) • 4 geophones (G1, G2, G3, G4) placed at the base of the HMAC layer Geophones G1, G3 and G4 were placed 20 cm apart in the transversal direction, to evaluate the lateral position of the vehicle wheels with respect to the sensors. Geophones G1 and G2 were placed 1 meter apart in the longitudinal direction, in order to determine vehicle speeds. The geophones were put in place during the pavement rehabilitation works.

Geophones are placed after the construction of each layer. For this purpose, holes (slightly larger in diameter than the sensor) are made in the pavement. The geophone is placed in the hole and then covered with resin. A thin trench is then made to pass the cables and sealed by the same resin. The temperature probes are installed in the same way.

All the sensors were connected to a remote data acquisition system located on the road side by cables, embedded in small trenches, cut in the pavement.

The platform used for remote data acquisition on the Ax motorway, called PEGASE, was developed by Le Cam et al. [23] [24]. The PEGASE system is a modular wireless solution used for instrumentation purposes. It includes DSP (digital signal processing) and input output ports, a 32-bit low power processor optimized to route to RAM and FLASH memories; it also has a WIFI PCMCIA module for wireless communication. The PEGASE mother board is integrated with the Blackfin 537 processor, a wireless communication module and a GPS module. The PEGASE card is Linux kernel based and configured with communication protocols, specified as the IP (internet protocol) and the global protocol; this allows the Wi-Fi network or 3G/4G connection to have remote sessions. The PEGASE platform can be remotely managed to program new data acquisition and processing algorithms, or modify existing ones. The PEGASE system is very compact, modular, and consumes little energy. On the site, a 220-volt power supply was available. However, as the system has low power consumption, it can also operate using solar cells and batteries. It has been used for various applications like bridge inspection, cable monitoring, railway monitoring or pavement monitoring [25][26][27] [START_REF] Lenglet | A smart road that warms its network manager when it begins cracking[END_REF].

The sampling frequency of the acquisition of the geophone signals was fixed at 2000 Hz. For every vehicle passage, a trigger with an adjustable acquisition duration, is used to record the signal. The duration is set by a pre-trigger and post-trigger, and in our application the signals of all the sensors were recorded in total for 10 seconds. A threshold was also set for recording the geophone data. The value of this threshold was set to record only signals corresponding to a minimum deflection of 5 mm/100. This allows to limit the number of recordings, by recording only deflections under heavy vehicles with a sufficiently high load, and not under passenger cars. The threshold of 5 mm/100 led to record approximately 100 to 160 heavy vehicle passages per day. On the other hand, the temperature measurements were recorded continuously (without any trigger), at a rate of one measurement every 15 minutes.

The system started working in 2014 and after some preliminary tests in March, continuous measurements were carried out from October 2014 to December 2016. However, the data acquisition was interrupted from January to February 2015 and from January to March 2016 due to failure of some data acquisition boards. • Remote control of instrumentation is particularly appropriate in the context of a heavy traffic motorway, due to difficulties of access to the site.

• Remote data transfer allows to monitor continuously pavement response, under normal traffic (the transducer signals under the passage of all heavy vehicles can be recorded, for example). It is thus possible to study in detail the variations of the pavement response with traffic and climatic variations (temperature, moisture conditions, etc..).

Conversely, difficulties associated with such monitoring under real traffic lie in the absence of precise information on the characteristics of the traffic loads (vehicle speeds, transverse wandering, axle loads). Therefore, in this work, the objective was to use the sensor signals to determine the pavement deflections, but also to obtain additional information on the heavy vehicle traffic. For that purpose, a new exploitation of the geophone measurements (and associated temperature measurement) has been performed, based on new treatment methods, developed by Bahrani et al. [START_REF] Bahrani | Alternate method of pavement assessment using geophones and accelerometers for measuring the pavement response[END_REF], which allowed to determine:

• The configurations and speeds of the heavy vehicles; • Vertical displacements (deflections) at the positions of the different geophones;

• The evolution of deflections with temperature and time;

• Subgrade bearing capacity and its evolution with time.

Characterisation of the heavy traffic

Characterisation of heavy vehicle configurations

The first proposed application of the geophones is detecting and counting the heavy vehicle configurations. Vehicle types can easily be identified, because the signals are very sensitive to each axle load: on the geophone deflection signal, each downward displacement peak corresponds to the passage of one axle, and thus the number of axles of the vehicle, and their spacing can be determined.

To automate the detection of the peaks, a procedure based on the calculation of successive increments of the displacement has been proposed. The peaks of the signal are detected by the changes of sign of these increments.

If the deflection di (vertical displacement in mm) measured by the geophone at a time ti (in seconds) is defined by a function di = f(ti), the increment ratio of the deflection, i, can be calculated, by successive intervals of 20 data acquisition points (i to i+20), by equation (1):

∆ 𝑖 = 𝑓(𝑡 𝑖+20 ) -𝑓(𝑡 𝑖 ) 𝑡 𝑖+20 -𝑡 𝑖 ( 1
)
Where i is the increment ratio of deflection versus time, in mm/s. i is calculated by successive intervals of 20 points, to eliminate the effect of noise.

Peak detection is based on the sign of increment i: when i changes from negative to positive, a downward peak, which is assumed to correspond to the passage of a wheel, is detected. A procedure based on this principle was developed using the Scilab software, to identify different vehicle configurations. The criterion used to identify each type of vehicle is defined in Table 2.

Evolution of pavement deflections

Determination of an average monthly deflection value

In order to understand the evolution of the performance of the pavement with traffic, and with climatic variations, it is important to analyse the evolution of the deflection over a long continuous period of time.

On the experimental section, 3 geophones G1, G3 and G4 were positioned at different lateral positions in the wheel path (see figure 3), to detect the different lateral positions of the passing vehicles. For each vehicle, it was assumed that the position of the vehicle wheels is close to the geophone giving the maximum deflection amplitude, and the signal of this geophone was selected for the analysis of the deflection basin. The analysis of the data indicated that the maximum deflection amplitude was obtained most frequently for geophone G3 (placed on the right of the wheel path), less frequently for geophone G1 (the central geophone) and rarely for geophone G4 (placed on the left). This indicates that most heavy vehicles drive with their wheels close to the emergency lane, which is close to geophone G3.

To analyse the evolution of the pavement deflections with time, the following approach was used, for each recorded vehicle signal (corresponding only to T2S3 trucks): i.

Signals corresponding to T2S3 trucks only were selected ii.

For each truck, the geophone giving the maximum deflection was determined.

iii.

For this geophone, the peak deflection under the first axle of the truck was determined. iv.

The monthly average of these values was calculated for each geophone, G1, G3 and G4, and the evolution of these monthly average values with time was plotted.

As these monthly average values were calculated for a large number of vehicles (several thousand), of the same type (5-axle semi-trailers), it was considered that the statistical distribution of the loads is very similar each month, and thus that this "average" deflection can be considered as representative of the average stiffness of the pavement during the considered month.

Analysis of average monthly deflections

Figure 7 shows the monthly average deflection values obtained for the two-year monitoring period for the three geophones. This figure indicates that:

• The highest average deflection values are obtained for geophone G3; those of geophone G1 come second, and those of geophone G4 are the smallest. As already discussed in section 5.1, these differences can be explained by the lateral positions of the vehicles, which drive mostly with their wheels close to the emergency lane, so close to G3.

• For all geophones, the monthly average deflection values present important seasonal variations, due to temperature variations. The deflections increase significantly in the summer, when the pavement temperatures increase, and decrease in the winter, when the pavement temperatures decrease. These variations are due to the high sensitivity to temperature of bituminous materials. Due to these effects of temperature, it is difficult to determine if there is any long-term variation of deflections, which could be due, for instance, to pavement damage. For this reason, a procedure for temperature correction of deflection measurements was developed. (under the first axle of T2S3 trucks)

Analysis of the influence of temperature on deflections

To eliminate the influence of temperatures on the measured deflections, two temperature correction methods were tested. The temperature corrections were applied to vehicle signals which were maximum for geophone G1, which is the central geophone and for which the wheels of the vehicles are centred on G1. The temperature at the base of the HMAC layer was selected for the correction.

The first temperature correction method consisted in defining an experimental relationship between the average temperature values, and the average measured deflections. This relationship was determined using the measurements made from March to July 2015 (beginning of the continuous monitoring period). A short period was used to eliminate, as much as possible, the influence of any long term evolution of the pavement deflections (due to damage or aging for example).

The resulting temperature correction is shown in Figure 8. A practically linear variation of deflections with temperature was observed, and therefore, a linear correction function was proposed, as defined by equation ( 2

Where:

d15°C is the deflection corresponding to a reference temperature of 15°C

𝑑 𝜃𝑚𝑒𝑠 is the deflection measured at the temperature 𝜃 𝑚𝑒𝑠

The second approach, for correcting the effect of temperature, consisted in using the method proposed in the French Technical Guide on Pavement Reinforcement [START_REF] Cerema-Idrrim | Guide technique -Diagnostic et conception des renforcements de chaussées[END_REF] (eq. 3). This guide defines the following equation ( 3), for the temperature correction of deflection measurements:

𝑑 15°𝐶 = 𝑑 𝜃𝑚𝑒𝑠 1 + 𝐾 * (𝜃 𝑚𝑒𝑠 -15)/15 (3)
Where: d15 is the deflection corresponding to a reference temperature of 15°C 𝑑 𝜃 𝑚𝑒𝑠 is the measured deflection at the temperature 𝜃 𝑚𝑒𝑠 K is a constant, function of pavement type, which value is equal to K=0.2 for the Ax section (thick bituminous pavement) Figure 8 shows the evolution of deflections with and without temperature correction, for geophone G1. Both temperature correction methods are compared and it can be seen that the two methods give very similar results; the maximum difference in percentage between the two corrections is about 2% for geophone G1. This confirms the validity of the formula proposed in the Pavement Reinforcement Guide [START_REF] Cerema-Idrrim | Guide technique -Diagnostic et conception des renforcements de chaussées[END_REF].

Finally, it can be concluded that:

• Both correction methods are efficient, and eliminate the seasonal variations of deflections with temperature. It may be noted, however, that for this pavement structure, the variations of deflections with temperature are relatively limited, probably due to the cement-treated subbase.

• After correction, the measurements indicate relatively stable deflection values during the whole monitoring period; there are no seasonal variations of deflection (once the temperature effect is corrected), and no significant continuous evolution, which be due to pavement deterioration. This seems logical, because the pavement has been reconstructed in 2013, and is not expected to deteriorate after only 3 years of service.

Pavement response modelling and comparison with the measured deflections

Objectives

As the instrumented section of the motorway had no other reference sensor, to compare the geophone measurements with, it seemed important to make a comparison with calculated theoretical signals. In this section, the measured deflections are compared with deflections predicted with the linear elastic pavement modelling software ALIZE [START_REF] Remaud | Alizé-LCPC Software : Analysis and structural design of pavement structures[END_REF].

Selection of representative geophone signal for each month

On the instrumented section the data acquisition system records large numbers of deflection signals (several hundred heavy vehicle signals per day). Therefore, it was decided to choose a limited number of representative signals for the comparison with the Alize calculations. The signals obtained with geophone G1, which has a central position in the wheel path, where chosen for this analysis. To select "representative" signals of geophone G1, a method defined by Broutin [START_REF] Broutin | Auscultation des chaussées souples aéronautiques au HWD-Guide technique[END_REF] [START_REF] Broutin | Assessment of flexible airfield pavements using Heavy Weight Deflectometers -Development of a FEM dynamical time-domain analyssis for the backcalculation of structural properties[END_REF] for the analysis of the HWD measurements was used, for each month. The

Where : 𝑑 𝑚𝑎𝑥,𝑖 𝑡ℎ 𝑝𝑒𝑎𝑘 is the measured deflection corresponding to the i th peak of the signal 𝑑 𝑚𝑒𝑎𝑛,𝑖 𝑡ℎ 𝑝𝑒𝑎𝑘 is the monthly average deflection, for the i th peak of the signal n is the number of peaks of the deflection signal (n=5 for T2S3 trucks)

Approach for modelling of the deflection signals

The following approach was used for the analysis of the signals:

i. Selection of a representative signal for each month (as described in section 6. b) Definition of a reference 44 tons T2S3 truck (see figure 9): For the ALIZE calculations, a reference truck was also defined. From previous studies, it was found that the load of the first axle of T2S3 trucks depends mainly on the weight of the tractor and is little influenced by the weight of the cargo of the trailer. In average, for different tractors, the weight of the first axle is equal to 6.5 tons [START_REF] Schmidt | Investigation of truck weights and dimensions using WIM data[END_REF]. For the calculations, a T2S3 truck, with a total load of 44 tons, which is the maximum weight allowed in France, was considered. The load of the first axle was set at 6.5 tons, as discussed. The load of the second axle was set at 11 tons, and the loads of the tridem axles were adjusted, to reach a total load of 44 tons.

The characteristics of the reference truck are shown on Figure 9. Adjustment of the modulus of the subgrade. The first step consisted in comparing the "representative" measured deflection signal with the response calculated with the initial pavement layer moduli, and the reference 44 tons T2S3 truck loading (see figure 9).

Based on these comparisons, the modulus of the subgrade was adjusted until a good fit with the measured deflection was obtained for the first axle of the truck, supposed loaded at 6.5 tons.

iv.

Adjustment of the load of the trucks. After fixing the subgrade modulus, a comparison was made between the calculated and measured geophone responses. In this calculation, the first peak of the deflection signal (corresponding to the 1 st axle, with a known load of 6.5 tons [START_REF] Schmidt | Investigation of truck weights and dimensions using WIM data[END_REF]) was relatively well predicted. Then, the loads of the other axles of the x truck were adjusted in ALIZE (assuming that the loads of the three rear axles are equal), by iterations, until a good match between the model response and the geophone measurements was obtained. This procedure represents a simple means of estimating the truck loads from the deflection signal

ton

Example for the month of September 2015

This section describes in detail an example of simulation of the deflection signals with ALIZE, following the procedure described in § 6.3, for the month of September 2015. The representative signal for September 2015 is the one recorded at 23h 11min 30s, on September 7, 2015. The truck speed is 28 m/s (100 km/h) and the temperature is 21°C (see Figure 10).

Preliminary calculation: A calculation was performed with the initial pavement layer moduli, and with the reference 44 tons vehicle load, and compared with the "representative" measured deflection signal. The deflection was calculated with the elastic model, for the month of September 2015, for a temperature of 21°C and a frequency of 14 Hz which corresponds to a vehicle speed of 28m/s (the speed / frequency equivalency proposed by [21][22] indicates that a vehicle speed of 70 km/h, or 20 m/s corresponds to a deflection signal which "equivalent" frequency is 10 Hz, assuming a sinusoidal shape of this signal; therefore, by extrapolation, a speed of 28 m/s leads to an "equivalent" frequency of 14 Hz). The truck load corresponding to the reference T2S3 semi-trailer truck, loaded at 44 tons (see figure 9) was used. The moduli of the bituminous layers (VTAC, HMBC, HMAC and GB layer) were adjusted to the temperature and frequency of the deflection signal. The initial value of the subgrade modulus was set at 150 MPa.

Adjustment process: Then, assuming that the load of the first axle is known and equal to 6.5 tons [START_REF] Schmidt | Investigation of truck weights and dimensions using WIM data[END_REF], the modulus of the subgrade was adjusted, until the best fit with the first peak of the measured deflection signal was attained. A final subgrade modulus equal to 160 MPa was thus obtained. Figure 10 shows that, as expected, the measured and calculated deflections are very similar in amplitude for the first axle, (and also relatively close for the second axle) but that the measured deflections are lower for the 3 rear axles. This can be explained by the fact that the real weight of the vehicle (which is unknown) is lower than the standard value of 44 tons, assumed in the modelling, which is the maximum legal weight in France. The loads of the other axles of the truck were then adjusted in ALIZE (assuming equal loads for the 3 rear axles), by successive iterations, until the best match with the geophone measurements was found.

Final result: The figure shows that after adjusting the loads, the deflection under the first two axles is not modified, but that the deflection under the last three axles is much better predicted.

The total truck weight found after the adjustment is equal to 34 tons. In some cases, a difference was observed between the calculated and measured positions of the deflection peaks, due to the difference between the real and theoretical truck dimensions (and axle spacings). To eliminate these differences, the distances between the axles were also adjusted in ALIZE, in order to improve the simulation.

Figure 10. Example of simulation of the representative geophone signal, for the month of September 2015

Evaluation of the subgrade modulus

For each month of the monitoring period, the subgrade moduli were adjusted, using similar calculations as those presented in section 6.4, and the corresponding values are shown in Figure 11. This figure shows that the subgrade modulus presents seasonal variations during the twoyear period: in the summer, the subgrade modulus presents higher values, varying between 180 The same trend is observed during the two years (2015 and 2016). These variations of modulus are probably due to seasonal water content variations of the subgrade, with lower water contents (corresponding to higher moduli) in the summer, and higher water contents in the winter. The same variations are observed by [START_REF] Ksaibati | Effect of moisture on modulus values of base and subgrade materials[END_REF]. The maximum legal weight for T2S3 trucks in France is 44 tons, whereas for the whole of Europe, the maximum weight for T2S3 trucks is 40 tons. The Ax motorway carries mostly international traffic between France and Spain, and so it is expected that the maximum weight will be approximately 40 tons, rather than 44 tons. As the "representative" deflection signals over the two-year period were indicating lower loads than the maximum authorized load, it was decided to select a signal (presented here for the month of September 2015) where the deflections under the rear axles were maximum. To verify the level of load corresponding to the selected signal, two comparisons with ALIZE calculations were performed, as follows:

• The bituminous layer moduli were adjusted in function of the speed and temperature.

• The subgrade modulus used was the one determined in the previous calculations, for the same month (here 160 MPa; see figure 11)

• The loads of the truck were set first to 44 and then to 40 tons in the calculations.

Figure 13 presents the comparisons between the selected "maximum" signal and the • the subgrade modulus, using the deflection under the first axle, for which the load is supposed known, and equal to 6.5 tons.

• the vehicle loads, by adjusting the loads of the 4 other axles in the model, to fit the measured deflections

Although the loads obtained with this simple approach seem realistic, additional test are planned, to compare the loads estimated with the geophones with reference measurements made with a weight in motion system, to verify the accuracy of the approach.

Possible application of the geophone measurements for pavement monitoring

The objective of the work presented in this paper was to evaluate the potential of using embedded geophones (associated with temperature sensors), instead of classical deflection measuring equipment, like FWD or TSD, for pavement monitoring. Obviously, the advantage of an instrumentation with fixed sensors is the possibility to make continuous measurements, and thus to obtain much more detailed information on the response of the pavement to variations of traffic and climatic conditions. This opens new perspectives of application of the deflection used, but this number could be increased to obtain more accurate information.

2) Evaluation of pavement condition: the deflection measurements can be used to backcalculate pavement layer moduli, and to monitor continuously their evolution with time.

After applying a temperature correction, as described in the paper, the evolution of layer moduli can be used to detect damage in real time, and this information can be used for pavement management and preventive maintenance.

3) Estimation of heavy vehicle loads: after defining a model of the pavement structure (for instance by backcalculation), the deflection signals of different vehicles can be used to estimate vehicle loads. After defining a vehicle configuration, and initial axle load values, iterative calculations can be performed with the ALIZE software, by adjusting the axle loads, until a good match is obtained between the measured and calculated deflections. This approach has been illustrated in section 6.4, for a T2S3 truck. However, the accuracy of the load estimation still needs to be verified, by comparison with reference measurements made with a weight in motion system. It is planned to perform such comparisons in the near future. In conclusion, measurements with embedded geophones offer interesting possibilities for pavement monitoring applications. However, their deployment on a large scale, at network level, still faces several challenges: the first one concerns the instrumentation costs, which remain relatively high, and need to be optimized. The second one concerns the connection of the transducers: the geophones used on the test section were wired, and this complicates sensor installation, and may also impact durability. In the future, possibilities to develop wireless sensors will be investigated. Finally, in this project, the data treatment was done manually, by post treatment. After defining the different treatment procedures, work has started to implement these procedures directly in the data acquisition system, in order to automate the data treatment, and obtain directly easily exploitable information (deflections, heavy traffic composition, vehicle loads).

Conclusion and perspectives

This article presents a new approach for monitoring deflections, on a test section of the Ax motorway, under real traffic, using geophones. The instrumentation included 4 geophones and 8 temperature probes, located at the base of each pavement layer. These sensors were connected to a data acquisition system based on PEGASE data acquisition boards, associated with a wireless transmission system, which sends the data via 3G/4G network to a remote database.

The geophones measurements were treated with the data processing technique developed by Temperature data was used to evaluate the influence of temperature on pavement deflections.

After correcting the effects of temperature, it was concluded that the pavement deflections presented no significant evolution during the two years of monitoring.

Finally, simulation of the geophone signals was carried out. For that purpose, the geophone signals were sorted, and only signals corresponding to T2S3 trucks, and passing in the centre of the wheel path (close to geophone G1) were retained. To evaluate the simulation approach, one "representative" signal for each month was selected. These "representative" signals were compared with calculations with the linear elastic software ALIZE.

The calculations with ALIZE were carried out, at first, considering a standard T2S3 vehicle, loaded at 44 tons (maximum legal weight in France). These comparisons indicated a good agreement between the measured and calculated deflections for the front axle of the vehicle, (loaded at 6.5 tons). For the other axles, differences between the measured and calculated deflections were more important. This was attributed to the fact that for T2S3 trucks, the load of the front axle is little influenced by the loading of the truck, and is close to 6.5 tons. For the other axles, the load depends much more on the weight of the cargo. In a second step, the axle loads were adjusted with ALIZE (except for the front axle), until a good fit with the measurements was obtained. With this approach, it was possible to estimate the truck loads and also subgrade moduli. However, the accuracy of the load estimations remains to be validated, by comparison with weight in motion measurements.

As discussed, the different information obtained from the geophone measurements (continuous monitoring of traffic composition, loads and deflections), could be used for pavement management. However, for a larger deployment of such solutions, it is necessary to fully automate the data treatment, and to try to reduce the overall costs of the instrumentation.

  validation of deflection signals •Identification of the heavy traffic through the description of shape of the deflection signals Exploration of the data obtained in a monitoring exercise along a period of time ✓Truck speeds ✓Deflection variation ✓Subgrade modulus variation

Figure 2 .

 2 Figure2. Comparison of the signal obtained with the reference displacement sensor and the signal obtained with geophone GS11D before and after the treatment[10][17]

  treatment layers. The moduli of the existing old layers (GB, cement treated sand, subgrade) were determined from FWD tests. The moduli of the new asphalt layers were estimated from laboratory tests.

Figure 3 .

 3 Figure 3. Pavement structure and instrumentation

Figure 4 3-axle truck 3 4-axle truck 4 5 2 Figure 5 .Figure 6 .Figure 6 .

 4342566 Figure 4 presents examples of signals corresponding to the different types of vehicles. In all the cases, the truck configuration can easily be identified by the number of downwards peaks and their distance. By applying this automated truck-type detection procedure to all the recorded signals, it is possible to determine the composition of the truck traffic, and establish various statistics. Figure 5 presents, as an example, the average monthly heavy traffic composition (in terms of different truck configurations) observed on the Ax motorway between October 2014 and December 2016. This graph shows that, on this motorway, semi-trailers, with two axles on the tractor (called here T2S3 trucks) are predominant, and represent around 80 % of the heavy vehicle traffic. Because the T2S3 trucks represent a large part of the heavy traffic on the motorway, it was decided, in this study, to focus the analysis on the deflection signals of the T2S3 trucks only.

Figure 7 .

 7 Figure 7. Monthly average deflections measured by geophones G1, G3 and G4

Figure 8 .

 8 Figure 8. Evolution of deflections measured by geophone G1 with and without temperature correction.

2 )

 2 ii. Modelling of the deflection signal with the Alize software a) Definition of an initial pavement structure for the ALIZE calculations, for comparison with the representative measured signals. The initial pavement layer moduli were determined using results of Falling Weight Deflectometer (FWD) tests performed on the experimental section in October 2013. These tests were used mainly to back calculate the moduli of the cement treated layer, the former GB layer and the soil. Standard modulus values, obtained from the ALIZE database, were used for the bituminous layers (VTAC, HMBC and HMAC layer) (see the parameters of table 1). The moduli of the bituminous layers (VTAC, HMBC, HMAC and GB layers) were adjusted to the temperature and frequency of the selected deflection signals. In the calculations, all layer interfaces were considered bonded, and values of Poisson ratio of 0.35 were used for all layers.

Figure 9 .

 9 Figure 9. Reference 44 tons T2S3 truck dimensions and weights

  , and in the winter, the modulus decreases, and varies between 90 and 140 MPa.

Figure 11 .

 11 Figure 11. Monthly variations of the subgrade modulus during the years 2015 and 2016

Figure 12 .

 12 Figure 12. Monthly variations of the average T2S3 truck loads during the years 2015 and 2016

1 )

 1 data for traffic monitoring, and pavement management. According to the results obtained on the Ax motorway, several practical applications, which are of interest for road management, can be foreseen: Monitoring of heavy traffic: As shown in § 4 of the paper, the geophone signals can be used to count the number of heavy vehicles, to detect vehicle configurations, and to measure vehicle speeds. If several geophones are placed at different lateral positions, it is also possible to characterise the lateral wander of In this work, geophones at 3 lateral positions were

4 )

 4 Estimation of actual pavement damage and residual pavement life. Pavement design calculations are generally based on simplified assumptions concerning the traffic and climatic conditions. By monitoring continuously pavement layer moduli, pavement temperatures and traffic loads (as explained above), it becomes possible to calculate pavement damage using the real traffic and temperatures experienced by the pavement. This can help evaluate better real pavement damage, and also expected pavement residual life.

[ 13 ]

 13 , based on laboratory tests, and validated by full scale accelerated pavement tests. Deflection and temperature data recorded from March 2015 to November 2016 was used to analyse pavement performance. The geophones, installed at different lateral position, were used to determine the pavement deflections, but also to obtain additional information on the heavy vehicle traffic, like vehicle types (number of axles), vehicle speeds and lateral positions. This information was used to get statistics about the composition of the heavy traffic on the Ax motorway.

Table 1 .

 1 Structure of the experimental section

	Materials	Elastic modulus (MPa) at 15°C, 10Hz	Thickness (cm)
	Very thin asphalt concrete (VTAC)	3 000	2.5
	High modulus bituminous concrete (HMBC)	11 000	6
	High modulus asphalt concrete (HMAC)	14 000	7
	Old road base asphalt material (GB)	5 000	8
	Cement treated sand	3964	30
	Subgrade	150	100

Table 2 .

 2 Identification criteria for different configurations of heavy vehicles

	Heavy vehicle type	Number of downward peaks
	2-axle truck	2

  ):Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

					d 15°C = d 𝜃𝑚𝑒𝑠 + 0.1058(θ 𝑚𝑒𝑠 -15)
		-12
	Temperature ( C) (mm/100) Deflection	0 10 20 30 40 50 -10 -8 -6 -4 -2 0	Summer	G1 2014	Autumn G3	Winter G4	Spring	Summer	2015	Autumn	Winter	Spring	Summer	2016	Autumn	G1 20cm Traffic lane for 20cm G4 slow vehicles	G3
		Jul	Aug Sep	Oct Nov	Dec Jan	Feb Mar Apr	May	Jun Jul	Aug	Sep Oct Nov	Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

  Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan "representative" signal is defined as the recorded signal which maximum deflection is closest to the monthly average deflection value calculated previously. It is defined by the minimal difference (named e) between the peak deflection values and the monthly average values, which corresponds to the equation 4:

		-12
	(mm/100) Deflection	-11 -10 -9 -8 -7	G1 Deflection without temperature correction G1 deflection linear correction G1 deflection reinforcement guide correction √ ∑ (𝑑 𝑚𝑎𝑥,𝑖 𝑡ℎ 𝑝𝑒𝑎𝑘 -𝑑 𝑚𝑒𝑎𝑛,𝑖 𝑡ℎ 𝑝𝑒𝑎𝑘 ) 2 𝑛 𝑖=1 𝑒 = 𝑖
		-6
			2014					2015			2016
	Temperature ( C)	0 10 20 30 40 50	Summer	Autumn	Winter	Spring	Summer	Autumn	Winter	Spring	Summer	Autumn
		Jul	Aug Sep	Oct Nov Dec Jan	Feb	Mar Apr	May	Jun Jul	Aug Sep	Oct Nov Dec Jan	Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

  simulations, with truck loads of 40 and 44 tons. The results indicate a good agreement between the model and the measurements; for the load of 40 tons, confirming that the loads of the heaviest vehicles are close to 40 tons.

		36
		35
	Load (Tons)	31 32 33 34
		30
		29
		28
		Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
	Spring Figure 13. Comparison of the deflection obtained with the maximum signal of September 2015 Summer Spring Autumn Summer Winter Autumn Winter
	2015 (02/09/2015, 16h 14 min 16s, 28.6 m/s and HMAC= 27°C) with simulations with loads of 44 2016
	tons and 40 tons respectively

In conclusion, it has been shown that if an initial model of the pavement is available (pavement layer moduli, layer thicknesses), the simulation of the in-situ geophone deflection signals, with the ALIZE software, can be used to estimate successively:

Funding

The research presented in this paper was carried out as part of the SMARTI H2020-MSCA-ETN-2016. This project has received funding from the European Union's H2020 Program for research, technological development and demonstration under grant agreement number 721493.


Phase Change Materials For Building Envelopes

In Reunion Island, France L. Trovalet (1) , L. Liu (1) , D. Bigot (1) and B. Malet-Damour (1) (1) Laboratory of Physics and Mathematical Engineering for Energy, Environment and Buildings (PIMENT)

CISBAT Liquid: 3600 J.K -1 .kg -1 Thickness 0.005 m

Experimental results

Period

Numerical results

The work presented in this paper aims to evaluate the effects of Phase Change Materials (PCM) on thermal comfort in buildings on Reunion Island.

❖ PCM was not optimally used due to its partial fusion: indeed, the complete melting needs temperatures ranging from 26°C to 40°C.

❖ The fusion and solidification temperatures intervals have to be clearly described to optimize the PCM efficiency and its impact on the building's thermal performance.

❖ Temperatures of thermal neutrality (in winter comfort and summer comfort) should be considered in the future as phase change temperature.

❖ Finally, simulation results have shown a good prediction of building's thermal behavior with and without PCM, and future work will focus on sensitivity analysis and thermal prediction.

Conclusion

  2021 -Lausanne, Switzerland Carbon Neutral Cities -Energy Efficiency & Renewables in the Digital Era EPFL, September 8 th -10 th , 2021 Objective: determining selection criteria for suitable PCMs in a tropical climate. ❖ Development of tools to collect and analyze local data; ❖ Modeling of the experimental benches with the measured properties of the selected PCM; ❖ Validation of the selected parameters with the simulation code, EnergyPlus TM . The "Phase Change Materials: an innovation for Tropical Buildings" (MCP-iBAT) project is conducted to identify and develop local resources for the improvement of thermal comfort and the energy management of buildings in Reunion Island (France).The present study evaluates the effects of a selected Phase Change Material (PCM) on an experimental lightweight building, located at Saint-Pierre, representative of the coastline area.

Figure 1 :

 1 Figure 1: Mean annual evolution of the outdoor air temperature in 2018-2019, city of Saint-Pierre, Reunion Island.

Figure 2 :

 2 Figure 2: Top view of the lightweight building, with the dimensions and the composition of the walls.

  kJ.m -2 112 kJ.m -2 Duration of release 3h 8h30min Energy Plus TM simulations with the characteristics of the experimental set-up and the material properties.

Figure 4 :

 4 Figure 4: Temperatures and heat fluxes on North walls

Figure 5 :

 5 Figure 5: Simulation results for North wall without PCM


domain: spi chim phys

text:
Loic Mastromatteo
email: loic.mastromatteo@onera.fr

Ludovic Gaverina

Jean-Michel Roche

Florian Lavelle

Francois-Xavier Irisarri

Loïc Mastromatteo

Durability of Surface Mounted PZT and FBG Guided Wave Sensors Under Reusable Launch Vehicle Representative Thermal Cycling

This paper addresses the durability of cobonded PZT and FBG sensors for hybrid Guided Waves (GW) SHM systems under thermal cycles partially representative of a Reusable Launch Vehicle (RLV) use case. The cobonding process is tested as a potential alternative to the secondary bonding process for attachment of SHM sensor network onto composite structures. The evolution of the sensors with the thermal cycles is continuously monitored through measurement of the electromechanical impedance (EMI) spectrums (for PZT) and reflection spectrums (for FBG). Then the effects on the PZT sensors performances are investigated by measuring GW pitch-catch signals at different stages of the thermal cycles. As for FBG sensors, the thermal cycling is still ongoing and is expected to be completed by the time of the conference: additional results will be discussed then.

INTRODUCTION

Over the past decade, the development of reusable launch vehicles (RLVs) to reduce the cost of access to orbit has become a strategic concern for the space industry. However, to achieve this goal, the additional cost of reusability (recuperation and revalidation of stages between launches) must be as low as possible. In this context, SHM systems could help to optimize inspection and maintenance operations on structures. The overall context of this work focuses on the implementation of an hybrid guided waves (GW) SHM system using both PZT transducers for GW sensing and generation and FBG sensors for GW sensing in order to harness the advantages and specificities of both types of sensors. As the SHM sensors networks usually are permanently attached to the structures, the durability of the sensors assembly (sensor+bonding layer) under the environmental and operational conditions is key to the long-term reliability of SHM systems [START_REF] Gorgin | Environmental and operational conditions effects on Lamb wave based structural health monitoring systems: A review[END_REF]. One of the critical issues for GW sensors is the mechanical coupling between the sensors themselves and the host structure in which GW propagate. PZT transducers are often coupled to the host structure by a secondary bonding process with an additional adhesive layer. However, the degradation of this adhesive layer can significantly affect the performance of SHM systems, and bonding defects are one of the main durability issues for the sensors. Specifically for composite structures, an alternative to the secondary bonding process is the cobonding process, in which the sensor is applied directly to the uncured composite. The sensor and host structure are then cured together, with the sensor being bonded by the matrix of the composite material. This process, which avoids the use of an additional adhesive layer, could help increase the durability and reliability of the bond between the sensor and the structure. From a manufacturing standpoint, it should also enable a reduction of manual labour required for secondary bonding of large SHM network thanks to the mutualization of the work with the curing of the host structure [START_REF] Dobmann | Challenges of an Industrialized Acousto-Ultrasonic Sensor System Installation on Primary Aircraft Structure[END_REF]. This study focuses on testing the cobonding process for PZT and FBG sensors and investigating their durability under thermal cycling. The temperature cycles are chosen to be representative of the thermal loadings that some areas of an actual RLV composite structure under its thermal protection might be subjected to: limited number of cycles (about 10) of short duration (5 minutes) and temperatures up to 150°C. The evolution of the responses of the sensors with the thermal cycling are continuously monitored with a measurement of the electromechanical impedance (EMI) spectrums of the PZT and the reflection spectrums of the FBG. The impact on the GW sensing capabilities is also measured through GW pitch-catch measurements after 0, 5 and 10 thermal cycles. The thermal cycling applied to the FBG sensors has not been completed for the time being; results from these specific tests will be presented at the conference.

METHODOLOGY

Materials and Sensors Layout

The sensors used in this study are C-6 PZT discs from FUJI-Ceramics (20 mm in diameter and 200 µm thick) and 5 mm long uniform FBG from IDIL Fiber Optics. These sensors are attached onto two 350 mm x 400 mm layered T700/M21GC CFRP composite plates, one for each type of sensors (see Figure 1). The quasi-isotropic layout is [0,-45,90,45]s with a total thickness of 2 mm. The sensors that are submitted to the thermal cycling are arranged in a 30° tilted square to reduce direct wave reflections. All PZT sensors are cobonded to the surface since it was shown in a previous work that cobonding guarantees better durability properties than secondary bonding [START_REF] Mastromatteo | Thermal Cycling Durability of Bonded PZT Transducers Used for the SHM of Reusable Launch Vehicles[END_REF]. As for the FGB sensors, both cobonding and secondary bonding (with a Loctite 495 cyanoacrylate adhesive) are considered in order to evaluate their respective resistance to thermal cycling. In the center of both plates a reference PZT that is not submitted to the thermal cycles is cobonded to emit and receive GW to and from the other sensors.

Thermal cycling setup

The thermal cycle was chosen to be as representative as possible of a thermal environment that RLV composite components might be exposed to during their lifetime (around ten flights of a few minutes each). In contrast to this short exposure time, the thermal loads are intense (high maximum temperatures and steep thermal ramps, as seen in Figure 2) compared to other studies in the literature [START_REF] Salmanpour | Airborne Transducer Integrity under Operational Environment for Structural Health Monitoring[END_REF], [START_REF] Attarian | Long-term stability of guided wave structural health monitoring using distributed adhesively bonded piezoelectric transducers[END_REF], which often focus on aircraft applications and have much longer and smoother thermal cycles. Ten cycles are applied to the sensors (excepted the central one, used as reference), generated using a CO2 laser while the backside of the plate is cooled with compressed air in order to reach thermal equilibrium and have stable backside surface temperature during the cycles. The thermal profile at the plate surface is controlled using an MWIR infrared camera. Laser heating has several advantages in our case. First, it allows much steeper thermal ramps than a furnace; second, the generated heating is non-uniform, presenting a thermal gradient between the hot side (where the transducers are located) and the cold side of the coupon. This is typical of the thermal profiles RLV structures are exposed to, as the heat comes from the outside due to the backfiring of the engines while the inside is generally much cooler due to the cryogenic ergols. Finally, the Laser heating makes it possible to apply cycles to one sensor at a time without affecting the others, so the reference transducer remains undamaged.

Sensor characterization

EMI SPECTRUM MONITORING (PZT)

The EMI method is based on the fact that when a PZT is coupled with a host structure, its electrical impedance is related to the mechanical impedance of the structure through the piezoelectric effect that generates a coupling between the mechanical and electrical variables. This method is often used for damage detection [START_REF] Na | A Review of the Piezoelectric Electromechanical Impedance Based Structural Health Monitoring Technique for Engineering Structures[END_REF] but can also be used for the transducers assembly self-diagnosis by identifying the signatures of possible sensor debonding or degradation in the impedance or admittance spectra [START_REF] Park | Sensor Self-diagnosis Using a Modified Impedance Model for Active Sensing-based Structural Health Monitoring[END_REF], [START_REF] Jiang | Electromechanical impedance based self-diagnosis of piezoelectric smart structure using principal component analysis and LibSVM[END_REF]. In this study, the electrical admittance spectrums are measured in the [80 -440] kHz range, using an HP 4194A Impedance Analyzer. This frequency range is chosen to include the main resonance of the PZT transducers and the signature of potential debonding. As the PZT and the host structure form a coupled system, this frequency range of interest strongly depends on the sensors (geometry, piezoelectric and mechanical properties…) and the mechanical properties of the host structure.

REFLECTION SPECTRUM MONITORING (FBG)

In the literature, many approaches have been used for GW sensing with FBG, including different FBG designs or different demodulation technique [START_REF] Soman | Optical Fiber Sensors for Ultrasonic Structural Health Monitoring: A Review[END_REF], [START_REF] Wu | Ultrasonic Structural Health Monitoring Using Fiber Bragg Grating[END_REF]. In this work we used some widely available uniform FBG sensors and the demodulation for GW sensing is based on the edge filtering technique for its good sensitivity [START_REF] Wu | Ultrasonic Structural Health Monitoring Using Fiber Bragg Grating[END_REF]. This technique makes use of the steep edges of the FBG reflection spectrum and a narrow band accordable laser in order to enhance the sensitivity to high frequency but very small strain ultrasonic signals. As this technique depends on the slope of the reflection spectrum, it is relevant to monitor the shape of this spectrum throughout the thermal cycling as possible degradation could impact its shape and thus the slope. The spectrum is monitored using a HBM FS22 FBG interrogator.

GUIDED WAVE PERFORMANCES

The targeted application of this work is for a GW hybrid SHM system coupling FBG for sensing and PZT for sensing and GW generation. Thus, it is interesting to look at the PZT sensor capability to receive and emit GW into the host structure. Due to the presence of a reference, undamaged PZT, it is possible to test the PZT discs both in reception and in emission. As the FBG are passive sensors, they cannot be use to emit GW and thus they are only tested in reception. The GW signals are recorded in the initial state, then after 5 and 10 cycles to evaluate the impact of the thermal cycling. PZT input voltage for the GW generation is a 5-cycle Hanning windowed sinusoidal toneburst at 10 V peak-to-peak. In order to avoid complex mode superposition, the frequency range is limited to have only the two fundamental Lamb wave modes, namely the first antisymmetric (A0) and symmetric (S0) modes. For our composite plate, this limits the frequency at around 350 kHz. Therefore, three measurements are done in the [15-25] kHz range (maximum A0 amplitude) and three in the [150-250] kHz range (maximum S0 amplitude).

RESULTS & DISCUSSIONS PZT transducers

Concerning the PZT, the results for the cobonded transducers are compared with results from a previous work presented at EWSHM 2022 [START_REF] Mastromatteo | Thermal Cycling Durability of Bonded PZT Transducers Used for the SHM of Reusable Launch Vehicles[END_REF] for secondary bonded PZT. For the evolution of PZT transducers with the thermal cycling, different degradation scenarios were identified in [START_REF] Mastromatteo | Thermal Cycling Durability of Bonded PZT Transducers Used for the SHM of Reusable Launch Vehicles[END_REF], with or without the appearance of PZT debondings. The result for cobonded PZT are compared with the best case scenario in which the bonded PZT do not show any debonding with only an evolution of the adhesive properties. In contrast, for the four cobonded PZT in this work no debonding of the sensors were spotted and every PZT showed a similar behavior throughout the thermal cycling. Figure 3 shows the comparison of the admittance spectrums for one of the cobonded PZT and a secondary bonded PZT (without debonding) [START_REF] Mastromatteo | Thermal Cycling Durability of Bonded PZT Transducers Used for the SHM of Reusable Launch Vehicles[END_REF]. First, we can notice that the evolutions are quite different: for the cobonded PZT disc, the entire spectrum is stable throughout the thermal cycling while for the bonded one a significant evolution of the spectrum can be seen. For the imaginary part, there is an overall increase in the slope of the spectrum. For the real part the impact is the most clear for the resonance peak, with a small downshift in the resonance frequency along with an increase in amplitude. Although a difference can be identified on the admittance spectra, the most important criterion from a SHM standpoint is whether this difference affects the GW performances of the sensors or not. Figure 4 shows the amplitude variation (compared to the initial state) of the GW emitted and received after 5 and 10 thermal cycles for the same PZT discs than in Figure 3. Major differences are spotted regarding the GW performances. On the one hand, cobonded PZT discs present only a few percent variation of amplitude between the initial state and after 10 thermal cycles without a clear visible tendency. On the other hand, the secondary bonded PZT discs are strongly impacted by the thermal cycles, with a 15 % to 20 % drop of amplitude for the reception capabilities. Thus, the cobonding process proves to be, in this case, more stable throughout the thermal cycling while also being easier to implement due to the mutualization of the bonding process with the manufacturing of the host structure.

FBG sensors

For the FBG, both cobonded and secondary bonded sensors are tested on the same composite plate with symmetric placement around the reference PZT used to emit the GW. The first important thing to look at is the impact of the bonding process on the reflection spectrum of the FBG. As the edge filtering technique is used to measure the GW signals, the shape of the reflection spectrum and especially its maximum slope will greatly impact the sensitivity of the sensing. The normalized reflection spectrum for one cobonded and one secondary bonded sensors are presented Figure 5 along with the derivative of each spectrum. We can notice that for the secondary bonded FBG the spectrum is only slightly shifted and the shape is almost not impacted with a maximum slope close to the one of the free FBG. For the cobonded FBG the spectrum is much more shifted and its shape is also impacted. This is likely due to residual strain created in the fiber by the shrinkage of the composite matrix during the autoclave curing. We can notice that the maximum slope and thus the optical sensitivity to GW signals is reduced to 5.0 nm -1 compared to the 7.4 nm -1 for the secondary bonded FBG. However, the slope is only one part of the measurement chain and the strain transfer through the coupling layer plays an important role in the overall measurement of GW signal. Figure 6 shows the baseline raw waveform signal acquired from a cobonded and a secondary bonded FBG placed symmetrically around the reference PZT emitting the GW in the plate. As we can see the amplitude of the raw signals are indeed smaller for the cobonded FBG, nevertheless both signals are in the same order of magnitude and quite similar in the shape of the waveform. This difference of sensitivity should not significantly influence the GW sensing capabilities of the FBG. In fact, for SHM applications, especially when using a baselined approach, the stability of the system throughout the life cycle of the structure will likely be more important for the reliability of the SHM system to avoid false positive and degraded detection capabilities.

Given the previous results for the cobonded PZT transducers showing a good stability throughout the thermal cycles the same kind of behavior is expected for the cobonded FBG compared to the secondary bonded ones.

CONCLUSIONS AND PERSPECTIVES

In this paper, the potential of the cobonding process to attach SHM PZT and FBG sensors to RLV composite structures was tested in comparison with the classic secondary bonding process.

First, from a manufacturing standpoint, the cobonding process was proven faster and produced a more controlled and reproducible bonding than the secondary bonding process thanks to less manufacturing steps and a better control over the bonding process as it is paired with the curing process of the host structure.

Second, regarding the durability stake, the cobonded PZT sensors submitted to thermal cycling did not show any significant evolution or negative impact on their performances. This is in stark contrast with the results obtained for secondary bonded PZT in a previous work [START_REF] Mastromatteo | Thermal Cycling Durability of Bonded PZT Transducers Used for the SHM of Reusable Launch Vehicles[END_REF] in which significant impacts were observed due to debondings and evolution of the adhesive layer properties. Although work could be done to optimize the secondary bonding process for a specific use case (more suitable adhesive, surface preparation), the cobonding process showed a better durability and reproducibility of the bonding with a simpler and easily implemented process as most of the work is already done for the manufacturing of the host structure.

Lastly, the cobonding process seems to be also suitable for FBG sensors as cobonded FBG were perfectly functional, although with a slightly reduced sensitivity compared to the ones attached to the plate by secondary bonding. A distortion of the FBG spectrum during the curing in autoclave might be responsible for this. The durability to the thermal cycling was not completed in time to be included in this article, but given the good stability observed for cobonded PZT, promising results for the cobonded FBG are expected as well. These upcoming results will be presented and discussed during the conference.

Figure 1 :

 1 Figure 1: Sensors layout on the composite plate, (a) for the cobonded PZT, (b) for the FBG either bonded or cobonded

Figure 2 :

 2 Figure 2: Sensor temperature profile (a) and Laser Thermal cycling setup (b)

Figure 3 :Figure 4 :

 34 Figure 3: Evolution of the Real and Imaginary part of the admittance for cobonded (a,b) and secondary bonded (c,d) PZT

Figure 5 :

 5 Figure 5: Reflection spectrum and slope for a secondary bonded FBG (a,b) and a cobonded FBG (c,d)

Figure 6 :

 6 Figure 6: Raw guided wave signals at 30 kHz (a) and 200 kHz (b)


Dr Toky Rakotoarivelo

B Malet-Damour

Thermal comfort analysis: comparison between model and experimental data in tropical climate

Modeling & Experiments results

Reunion Island

Tropical climate

Comfort indexes

Hourly evolution of the average comfort index (over the group)

-1 -0,5 0 0,5 1 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00

Comfort temperatures

Comparative analysis of average results of 𝑇 ! -./0-1/ with 𝑇 "# 2345264 and occupant perception Experiment analysis

• Sensations between "slightly cold" and "slightly warm"

Indexes analysis Experiment analysis

• Neutral situation = thermal comfort • 𝑇 !" !"#$%&' = 26.3 °C
Comfort temperatures analysis

Comfort temperatures analysis

• Auliciems : overestimation (≃ 4°C, different trend) probably due to relative humidity (76% in the room // 50%)

• Sharma : same trend with a difference of 1.5 °C due to the climatic conditions under which this approach was developed (hot and humid climate of India)

… more results are available in the article (ID: 209) !

Article ID 209

Thermal Comfort means two things: 1.

No physiological thermo-regulation 2.

Hedonic feeling through thermal perception

How do we predict thermal comfort?

For the right people and the right environment The highest response rate to the comfort questionnaires was obtained on two particular days:

The questionnaire was answered through online forms according to a normalized scale (ASHRAE)

Measurement

The apparatus gathered all physical parameters proper to the room and physiological parameters for each participant through sensors

Minute

  )Tc $%&'(% & *( *(&% TrSI T + !"#$%$&'( T + )"'*+,&-(

  morning); overcast sky (afternoon) Light ambiance: bright (morning); intermediate (afternoon) "Active" porosity: 13% Spreadsheet The spreadsheet was used to calculate models results with the variables measured in situ. Three types of data were obtained: Create a generic model Build a numeric model generator Experiment Identify the least intrusive perception assessment techniques possible Consistent results Highlighting the inadequacy of temperate climate studies in the tropical climate context Perspective for experimentDevelop a field survey combining physicsphysiology-psychologyAim: to show that there is a need to promote the studies of thermal comfort for the Reunion context

  Evolution of 𝑇𝑟𝑆𝐼, 𝑇 $%& and 𝑇 "# 2345264 and occupant perception

		Sensation EXP	EXP (Top)	TrSI (Sharma)	Tsub (Auliciems)
	17 21 25 29 33	°C °C °C °C °C 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00	-2 -1 0 1 2	Warm Neutral Cool

• Brager and De Dear + Auliciems : inappropriate • Humphreys : most efficient, but insufficient • Possible origins of bias : physical (relative humidity or airspeed) or physiological (clothing, metabolic level, acclimatization) parameters : not considered Neutrality temperatures