Michel Crampes
email: michel.crampes@site-eerie.ema.fr

Sylvie Ranwez
email: sylvieranwez@site-eerie.ema.fr

Ontology-Supported and Ontology-Driven Conceptual Navigation on the World Wide Web

Keywords: Adaptive hypertext, conceptual navigation, metadata, XML, ontology, narration, time optimization, WWW

This paper presents the principles of ontology-supported and ontology-driven conceptual navigation. Conceptual navigation realizes the independence between resources and links to facilitate interoperability and reusability. An engine builds dynamic links, assembles resources under an argumentative scheme and allows optimization with a possible constraint, such as the user's available time. Among several strategies, two are discussed in detail with examples of applications. On the one hand, conceptual specifications for linking and assembling are embedded in the resource meta-description with the support of the ontology of the domain to facilitate meta-communication. Resources are like agents looking for conceptual acquaintances with intention. On the other hand, the domain ontology and an argumentative ontology drive the linking and assembling strategies.

INTRODUCTION

Navigation on the World Wide Web relies on two main technical basis, hypertext and Information Retrieval based on search engines. Each technique constitutes a paradigm, i.e. a specific way of looking at this World.

For the hypertext paradigm, the World Wide Web is a network of links between and within documents through which the user navigates using visual invitations (marks) on the documents. Meanwhile, IR search engines use key words and index databases to gather everything that may resemble a user's query. Each approach is very powerful and has proven to be efficient within its own paradigm. Practically, readers combine both. The lexical search is to look for unknown documents on specific topics, and the hypertext approach uses authors' links to complete the coverage of the topic as needed.

It is accepted that there is no ideal solution to a complex problem and a coherent paradigm may present limits when considering the complexity and the variety of the users' needs. Let's recall some of the traditional criticisms about hypertext. The readers get lost in hyperspace. The links are predefined by the authors and the author's intention does not necessary match the readers' intentions. There may be other interesting links to other resources that are not given. The narrative construction which is the result of link following may present argumentative pitfalls.

As regards the IR paradigm, there are other criticisms. The search engines leave the readers with a list of weighted documents having no other relation than the lexical one. The set of documents is a set of local results and there is no means for managing redundancy, or a lack of information. The order of presentation is often the decreasing order of the weights and there is no narrative construction between documents.

Beyond these specific criticisms, both approaches present other common limits. The reader is the one who must decide most of the navigation strategy. This responsability would not be a problem if the readers already knew the content of the documents they are invited to visit. But when the readers have very little idea about the documents, their content and their volume, which is usually the case, they have not enough information to decide what the best strategy is for meeting their goals.

Finally, no constraint is handled by the hypertext navigation on the behalf of the users, such as the time they have available to read the documents they access. This consideration has not inspired much research, but practically, this is the sort of constraint that influences quite a lot the readers' strategies.

The research project of our team is to define a new approach where an agent uses ontologies to work on the behalf of readers to find relevant documents, select among them the most appropriate, organize them, and establish links between them with a possible argumentative construction. During the work, the agent takes into account readers' requirements and constraints, particularly the readers' content objectives and their available time constraints. This paper presents the principles of ontology-supported and ontology-driven conceptual navigation. Several possible models of conceptual navigation strategies are introduced. We illustrate two of them with different applications. We analyse the architectural differences and the advantages and disadvantages they bring about. As a conclusion, we show that what is at stake is not only adaptivity to the users' needs, but also interoperability and reusability.

ONTOLOGY AND CONCEPTUAL NAVIGATION : PRINCIPLES AND EXAMPLE

Principle

The general principle of ontology-supported conceptual navigation is the following :

• The system takes charge of the user's profile involving objectives and constraints. This approach differs from hypertext because resources have no specific links, be these explicit as in most hypertext systems, or implicit as in [START_REF] Shipman | Generating Web-Based Presentations in Spatial Hypertext[END_REF] where a visual spatial hypertext is used to show relationships and linkages between documents. In conceptual navigation links between resources and their narrative or pedagogical roles are computed from their description in a formal conceptual language and may vary according to the situation. The selection of the most pertinent resources and their organization are not controlled by the user, but are the result of a computation that involves an ontology of the domain, and possibly an argumentative ontology. It differs also from IR as far as resource retrieval is more semantically grounded with the ontology of the domain.

The system does not only deliver a list of semantically related resources, but also an order of consultation to comply with domain content and narrative/pedagogy structures.

Ontology-supported conceptual navigation may use several navigation strategies. We introduce the first strategy with the Karina project (participants to all cited projects are acknowledged at the end of the paper).

Example of application in adaptive course building : the Karina project

The objective of the Karina project is to dynamically build courses which are adapted to the needs of a Phase two: Dynamic adaptive course building. In order to build courses, Karina needs to know the learner's profile, i.e. the present knowledge, the knowledge objective and the learner's constraints. The main constraint which is considered is time. An engine called Conceptual Evocative Engine is in charge of selecting among the available indexed resources those that can entirely, or most often partly, fulfill the conceptual description of the learner's objectives. When chosen pedagogical material has prerequisites, those prerequisites become an intermediate objective for the engine (backward conceptual navigation). The result is a list of pedagogical resources which is ordered according to the objectiveprerequisite navigation process.

Phase three: Courseware material presentation. The titles of the selected pedagogical resources are presented in a small frame, down the left of the screen (see Figure 1). It is important to note that the resources are presented in the order of the conceptual backward navigation result. The resource is displayed on the right-hand side of the screen. Every time a resource is closed by the learner, it is considered to have been consulted and its title is erased from the bottom-left frame and rewritten in the top-left frame. All the resources in this frame can be consulted again at any time. For those resources that have not yet been consulted, a little dot on the left indicates with a color whether they are ready to be consulted or not depending on whether the prerequisites have been satisfied.

ONTOLOGY-SUPPORTED CONCEPTUAL INDEXATION IN XML

The Karina's DTD

The Karina's DTD1 is a XML-written document which allows the qualification of complete resources, or parts of resources called "segments". The DTD is composed of several "elements" which contain most of the necessary information for retrieving a resource on a conceptual and argumentative basis, analysing it and assembling it with other resources [START_REF] Crampes | Spécification et proposition d'une DTD pour la qualification de matériaux pédagogiques adaptatifs[END_REF]. In the following description of the DTD, we only discuss some features that are used for ontology-supported conceptual navigation, and more precisely for conceptual backward navigation :

<!ELEMENT description_conceptuelle (phrase_kldp+)> contains a conceptual description of the resource, <!ELEMENT pre_requis (phrase_kldp+)> contains the conceptual prerequisites of the resource, <!ELEMENT ontologie (%uri;) > contains the address of the ontology which is used for indexing, <!ELEMENT temps_utilisation (#PCDATA)> contains the time value a learner should spend on the resource.

The time value is estimated by the tutor who indexes the resource. In future developments, this time value should be the result of statistical observations of readers' practices. The attribute "phrase_kldp" refers to Karina's Conceptual Language (KCL), which is described below.

Karina's Conceptual Language (KCL)

This language is defined in the Karina DTD using XML.

It formalizes conceptual descriptions of content into a structure called a Conceptual State Vector (CSV) presented in [START_REF] Crampes | Adaptive Narrative Abstraction Actes[END_REF]. A CSV is a weighted sum of conceptual assertions. Each assertion is represented by a conceptual graph (CG) [START_REF] Sowa | Conceptual Structures: Information Processing in Mind and Machine[END_REF].

Simplified Conceptual Graphs in Karina. Although Sowa's CGs are very useful to formalize knowledge, they present some drawbacks in the context of Karina. They are not simple to use for a non-specialist. They are not easy to implement in order to be manipulated. They do not take into account the new languages being developped to describe meta-data for the Internet, particularly XML and RDF. In order to solve these different problems, Karina recognizes a simple form of CG, and uses some of its properties to retain its power of expression. They contain a maximum of three conceptual terms, the source, the destination and a predicate (a relation between the source and the destination). In order to deal with a complex knowledge content, it is necessary to use several of these graphs. For instance, the sentence (from 'Alice in Wonderland') "Alice looks at the Caterpillar for some time in silence" would be described in KCL with a conceptual graph CG i composed of several three term conceptual graphs :

CG i : {CG i,1 : [LITTLE_GIRL : Alice] [LOOK_AT] -[CATERPILLAR : #], CG i,2 : [LITTLE_GIRL : Alice] [LOOK_AT] [SILENTLY], CG i,3 : [LITTLE_GIRL : Alice] [LOOK_AT] [TIME-PERIOD : #] }
The traditional CG relations like (AGNT) or (OBJ) have disappeared, but they are still implicit taking into account the ontology of the domain as it is explained below. As far as these three simple graphs describe the same situation, they can be merged applying Sowa's operation "copy", "restrict", "join", and "simplify" in order to rebuild the initial conceptual graph. To give more details to the situation, we simply need to add new assertions in the set. For example, if we want to enrich the CG i with the assertion that the caterpillar also speaks to Alice, we can add the following conceptual graph :

CG i,4 : [ CATERPILLAR : #]]

[SPEAK_TO] [LITTLE_GIRL : Alice } Conceptual typing with the help of the ontology of the domain. An ontology is "an axiomatic characterization of the meaning of a logical vocabulary" [START_REF] Guarino | Semantic Matching: Formal Ontological Distinction for Information Organization, Extraction, and Integration[END_REF]. It is modelled as a hierarchy of types and a set of relations beween those concepts which specify which assertions it is possible to make about a world corresponding to the domain. In Karina, semantic correctness and interoperability is supported by an ontology of the domain which is written in KCL. An ontology is stored as a resource specified with a particular DTD written in XML 1 .

Karina's indexing interface makes use of the ontology of the domain to facilitate the indexing process and to prevent any mistakes. It opens up three slots for each Karina conceptual graph to be edited. The slots are constrained according to the ontology used for indexing the document. The first slot stands for the "source" of the conceptual graph. It contains the hierarchy of concepts from the ontology. When a concept is chosen, the indexer limits the hierarchical menu in the second slot to the concepts that are related to the source in the set of predicates in the ontology. It is then possible to choose in the concept hierarchy a concept among the children of these selected concepts. The same process is applied to the slot destination, with a hierarchical menu limited to the concepts that are in relation to the source and the predicate.

Compatibility with RDF. We have discussed the interest of this approach as far as simplicity and implementation are concerned. It is easier to implement a frame fixed in size in a database. This representation is also interesting because of its proximity to the RDF syntax which is the 'W3C Recommendation' for representing the semantics of resources on the Web [START_REF]W3C REC-rdf-syntax-19990222 Resource Description Framework (RDF) Model and Syntax Specification, W3C Recommendation[END_REF]. In RDF, a "statement" is a "specific resource together with a named property plus the value of that property for that resource. These three individual parts of a statement are called, respectively, the subject, the predicate, and the object". Karina's {source, predicate, destination} triplet is very near the RDF's {subject, predicate, object} statement structure. Although when designing Karina we wanted to pay attention to a possible compatibility with RDF, we built our representation in a more practical and straightforward formalism. 'Karina Conceptual Language' lies between Sowa's conceptual graphs, and the RDF syntax, with particular attention paid to practicality.

Conceptual State Vectors

In order to emphasize specific statements, or concepts inside statements, each statement in the set of statements describing a resource is endowed with a weight having a real value between 0 and 1. A justification for this weight has been given in [START_REF] Crampes | Auto-Adaptative Illustration through Conceptual Evocation in[END_REF]. As a result, a Karina conceptual description of a resource is a Conceptual State Vector (CSV), i.e. a symbolic sum of weighted conceptual graphs.

Translation and independant saving

All the information entered for qualifying a resource is translated automatically into XML using Karina's DTD. It is a Resource Description (RD) which is stored in an independant file from the resource in order to avoid polluting a possible original meta-description of the resource. This choice is the result of several considerations :

• A resource can keep its genuine meta-description which has a specific meaning in the original context. • The argumentative points of view may vary according to different tutors and there should be different RDs according to the different contexts.

• By keeping the resource in its original state, we partly avoid some problems with rights. • Finally, it is easier to scan a separate metadescription stored in a database and it takes less space to store it. The meta-description can be local, and the resources distant.

DEFINITION OF A LEARNER'S PROFILE

In the present version of Karina, the learner's profile consists in three main specifications :

The conceptual description of the learner's initial knowledge. This description is done using a Conceptual State Vector which contains the main concepts and relations supposedly known by the learner. It is entered by the learners with a simplified interface to avoid manipulating conceptual graphs (see below).

The conceptual description of the knowledge to be taught.

The knowledge objective to be taught is also described using a Conceptual State Vector. As for the learners' initial knowledge, since the CSV description is too formal for the learners to specify their goals, they are, in fact, invited to choose a set of sentences in natural language in a pre-specified list. Each sentence hides a CSV. To further simplify this approach, it is possible for the tutors to define conceptual curricula which consist of a list of contents and give it a name. It should be noted that the result of the specification of a content for Karina is not a list of pedagogical material, but a list of conceptual content descriptions.

A time constraint. The learner can give a time constraint to indicate how long he or she can spend on learning. This information is important. It allows Karina's engine to optimize the training and to avoid combinatorial explosion.

BACKWARD CONCEPTUAL NAVIGATION STRATEGY

When asked to design a course for a particular learner, Karina takes into account the initial profile and tries to build an ordered list of resources that can together meet the learner's objectives. To do so, it only considers the RDs that are available in the RD database. The resources may be stored locally or on another server. Their localisation will be taken into account only when they are presented to the learner. The backward conceptual navigation process is conducted in several steps. In each step, the process is not straightforward and several heuristics may be used on which we shall not go into detail. Karina's engine is written in Java and before analysis by the engine, the RDs are translated for manipulation into an Oracle relational database.

The backward conceptual algorithm

Objective update. The first step consists in updating the objective. Karina takes the CSV corresponding to the objective and withdraws those CGs that are present in the learner's initial model. The weights are not taken into account at this stage. This suppression is made with a total match between the slots of the CGs, i.e. when a slot is empty in one CG, and the corresponding slot is not empty in the other CG, the two CGs are considered not to match.

Conceptual Proximity computation. In a second step, the engine explores the different RDs and computes a match beween the learner's updated content objective and the conceptual contents of the resources. This process uses a unification algorithm to compute a Conceptual Proximity (CP) between two CSVs. This algorithm has been formally described in [START_REF] Crampes | Auto-Adaptative Illustration through Conceptual Evocation in[END_REF].

Choice of the best resource. The resource with the highest CP as regards the updated objective is selected. If several resources have the same CP value, Karina selects the one with the lower time value. This choice is justified because the shorter the resource, the more it will be possible to confine the course in the time constraint given by the learner. If two resources have the same duration, one is arbitrarily chosen. The other one is memorized in case the selection needs to be reviewed at the end (backtracking).

Objective and profile updating. Then Karina withdraws the content of the selected resource from the objective and adds this content to the learner's profile. It behaves as if the learner had consulted the resource. It also adds the prerequisites of the resource to the objective. When doing this, it only adds the prerequisites that are not already present in the learner's profile to avoid looking for contents that have already been dealt with by other selected resources or by the learner's initial knowledge. Any selected resource is tagged so that it will not be considered again during the following round of selection.

End of selection. The selection process ends when there is no content left in the objective, or if there are no resources matching the objective. The different resource durations are added up. If the result exceeds the learner's time constraint, Karina tracks back to choose the second-best selected resource in the queue which presents a shorter time value to try another path. If there is no path meeting the time constraint, Karina proposes the shortest path.

Organisation of the resources

The selected resources are finally presented in the order imposed by the prerequisites with the first on the list being the one requiring no other prerequisites than the learner's initial knowledge.

OTHER CONCEPTUAL NAVIGATION STRATEGIES

Traditional navigation strategies are still possible since the resources keep their original hyperlinks and the DTD allows the introduction of keywords for IR engines. But what is most interesting is the numerous conceptual navigation strategies that are possible. We present here some of them that we are studying and that are representative of the power of ontology-supported conceptual navigation.

Conceptual expansion

The backward navigation strategy presented above takes into account only the intitial goals and the different resource conceptual prerequisites to look for resources. Although this approach may lead to a good coverage of the domain being taught, it may leave aside interesting related concepts that are neither in the objectives nor in the prerequisites. In order to enlarge the conceptual domain of search, Karina's DTD provides a means for expanding the objectives with the element <!ELEMENT relation_conceptuelle (phrase_kldp+)>. Conceptual expansion may be applied in two ways. In the first case, the user may ask "more" about a subject when studying a resource, and the evocative engine will look for conceptually related resources. Since this conceptual relation may be attached to several segments of a resource, the expansion process may help to look in detail at different aspects of the content. The second type of conceptual expansion may be used by the application itself when there is a lack of material to build a sufficient delivery within the time constraint. In such a resource starvation context, the conceptual expansion policy allows for the filling up of the gaps. Conceptual expansion opens up many interesting possibilities that we are studying for other multimedia applications.

Forward conceptual navigation

Conceptual expansion can be used as a whole strategy which replaces backward navigation. A first resource is chosen and through conceptual expansion other resources are selected. In their turn, they may be used for expansion up to the point where the time constraint is reached. This process looks very much like free navigation in a hypertext, with the difference that here it is based on conceptual evocation and not hyperlinks. The risk is to get lost in a set of resources which are not linked through narrative constraints. It needs some conceptual railing.

The conceptual specification strategy and its application in narrative abstraction

The conceptual prerequisites and the conceptual relation constitute conceptual specifications for linking a resource to other resources. The advantage of embedding conceptual navigation specification within the resources is that the resources are independant, self-contained, and also cooperative. It is a first step to seeing resources as cooperative agents. The drawback is that the narrative/pedagogic strategy cannot be specified independantly from the resources. This drawback can be overcome with a strategy which is based on a conceptual specification of the expected final resource.

It consists in building a purely conceptual resource, i. e. an empty resource that only contains conceptual descriptions of segments. The engine goes to the first segment, takes its description as conceptual objectives and looks for resources that match these objectives. Then the engine proceeds to the next segment keeping the time constraint as a parameter for optimization. We have already presented this type of strategy implemented in the Godart project [START_REF] Crampes | Adaptive Narrative Abstraction Actes[END_REF] which builds narrative abstraction from a linear narrative. If the application is educational, the conceptual content of a segment must be added to the learner's profile before going on to the next segment in order to avoid as much redundancy as possible. This strategy allows the formal specification of a conceptual pedagogical or narrative path, step by step, without requiring any resources to materialize it. When it is applied in different pedagogical contexts, i.e. with different pedagogical resource environments, it leads to different concrete pedagogical solutions.

FROM ONTOLOGY-SUPPORTED TO ONTOLOGY-DRIVEN CONCEPTUAL NAVIGATION

The domain ontology can drive the conceptual navigation

In the different conceptual navigation strategies described above, navigation has only been "supported" by the ontology of the domain, i.e. the ontology of the domain has been used only for indexing in order to be sure that the resources and the users' objectives are described with the same concepts and relations. Since an ontology of a domain contains a great deal of information about the domain, it is not surprising that we could use it in a more profitable way. [START_REF] Guarino | Formal Ontology and Information Systems[END_REF] defines an "ontology-driven IS (Information System)" as an IS application where "the ontology is just another component (typically local to the IS), cooperating at run time towards the "higher" overall IS goal". Our central idea for ontology-driven conceptual navigation is to design an architecture where the engine only relies on ontologies for selecting resources, ordering them and adding a narrative/pedagogic intention during the linking process.

In pedagogic applications, this idea hinges on the observation that a table of content of a course looks very much like an ontology of the domain being taught. Titles and subtitles contain keywords that are presented in a hierarchy. Therefore, we can imagine that the ontology can be the basis for a training course when endowed with pedagogical properties. This is what we present in the next application example, Sybil.

Example of an ontology-driven conceptual application

Sybil [START_REF] Rosenberg | Locus Looks at the Turing Play: Hypertextuality vs. Full Programmability[END_REF] teaches classical music such as the concept "sonata". Two ontologies have been formalized using conceptual graphs, the ontology of the domain and an ontology of pedagogy. As usual, the domain ontology contains a hierarchy of domain concepts and relations between the concepts. The pedagogic ontology contains a hierarchy of pedagogic concepts, pedagogic rules and pedagogic strategies. When given a learner's objective, the engine, written in the language Scheme, builds paths through resources which are conceptually described. A Resource Description is written in the conceptual graph formalism and contains the resource's content (it is limited to the concept the resource presents), its URL, its pedagogical role (i.e. exposure, example, explanation, test, etc. from the pedagogic ontology), and even its media (see Figure 2).

For example, when asked to explain what a sonata is, the engine browses the resources to find those that explain the concept. In order to select the most pertinent resources and to organize them in a proper pedagogic structure, the engine uses the resources' pedagogical roles from the RDs and the pedagogical rules from the pedagogic ontology. For instance, there is a rule which says: "IF an Explanation and an Example refer to the same topic, THEN the URL of the Explanation must precede the URL of the Example".

Moreover, if the general exposition strategy is "Top-Down", the engine will find in the domain ontology that a sonata is composed of four parts: the "exposition", the "development", the "recapitulation", and a "coda". These concepts become new goals for the exposition. As one can see, the conceptual navigation is driven by both the ontology of the domain and the pedagogic ontology, along with the RDs which contain the resources' conceptual description and pedagogic roles. The three structures are independant and reusable although there is a certain limit as far as the resources are concerned as we see next.

Comparison of the two approaches

Both the Karina and the Sybil approaches are domain ontology-supported through indexation. In Karina, the conceptual navigation is the result of the engine strategies and the conceptual specifications embedded in the resources' description. In Sybil, the strategy is driven by the pedagogic ontology and the domain ontology. Both have pedagogic roles embedded in the resource descriptions. In Sybil, the pedagogic role is part of the resource description conceptual graph. In Karina, the element 'prerequisite' is a particular role for other related resources. There is also a specific element in the DTD called "type_pedagogique" which can be used to give a role to the resource. The fact that the description of a resource contains the pedagogical role of the resource is very open to criticism because a resource may have several pedagogic roles according to the context. To solve this problem, we are working to have this role driven by the ontology, which means that it will be calculated through the ontology of the domain using the hierarchy property of concepts and relations, and the conceptual operations of the conceptual graph theory. Then the independence between the conceptual navigation strategies and the resources will be stronger, and all the material (ontologies, and resources) more interoperable and reusable.

DISCUSSION AND RELATED WORKS

Our research on ontology-supported/driven conceptual navigation stems from three main research origins, adaptive hypermedia, information retrieval and dynamic narrative constructions.

In adaptive hypermedia systems, the aim is to find a compromise between guiding users and letting them browse on their own [START_REF] Brusilovsky | Methods and Techniques of Adaptive Hypermedia[END_REF][START_REF] Greer | Guided Navigation Through Hyperspace[END_REF][START_REF] Sterb | The difficulties in Web-Based Tutoring, and Some Possible Solutions[END_REF][START_REF] Weber | User Modeling and Adaptive Navigation Support in Proc[END_REF]. These approaches are attempting to find ways of adapting pre-existent hypermedia. They do not aim at the construction of new links and their narrative organization in response to user needs is predefined.

The use of metadata to help with information retrieval and to share resources is a well-established practice. It is the basis of search engines such as Yahoo or Alta Vista when using indexes. But the efficacity of this brute force approach for computing similarities beween resources is limited by the biases caused by synonymy and polysemy (see [START_REF] Clever | Members of the Clever Project, Hypersearching the Web[END_REF] for a good insight into this problem). To avoid this pitfall, there are two possibilities.

The first one is to automatically build links under the constraint of an ontology which contains synonyms and relations between words (semantic networks). It is the case of Green [START_REF] Green | Automated Link Generation: can we do better than term repetition? Seventh International World Wide Web Conference[END_REF] who automatically builds similarity links beween resources considering the fact that resources that are about the same thing will tend to use similar (although not necessary the same) words. He makes use of the WordNet database to build synset (sets of synonyms) weight vectors (the counterparts of Karina's conceptual state vectors).

The other possibility is to annotate resources under structural and semantical constraints to ensure interoperability [START_REF] Milosavljevic | Dynamic Hypertext Catalogues: Helping Users to Help Themselves[END_REF]. Resource description articulates around complete resources, or parts of resources like in Karina, and makes use of either specific descriptors [START_REF] Baldonado | Metadata for Digital Libraries: Architecture and Design Rationale in Proc[END_REF] or descriptors already established as standards or recommendations [START_REF]Dublin Core Metadata Element Set : Reference Description[END_REF][START_REF] Marshall | Toward an Ecology of Hypertext Annotation in[END_REF][START_REF]IMS, Educause, Instructional Management Systems[END_REF]. The XML (eXtensible Mark-up Language) [START_REF] Bray | W3C Extensible Markup Language (XML) 1.0[END_REF] language allows the description of electronic resources by means of a DTD (Document Type Definition). The use of DTDs for describing Internet resources is a recent yet already well-established practice [START_REF] Luesebrink | The Moment in Hypertext: A Brief Lexicon of Time[END_REF]. [START_REF] Auffret | Audiovisual-based Hypermedia Authoring: using structured representations for efficient access to AV documents[END_REF] proposes a DTD written in XML to describe the content of Audiovideo (AV) archives with meta-data. The authors also use an ontology to ascertain that several different resources are described with the same vocabulary. Then resource retrieval is based on dynamic linking either by taking an ontology or any resource as a point of entry. As far as only information retrieval is concerned, their approach is close to ours in many ways. We think, however, that the use of conceptual graphs and conceptual state vectors is more fruitful when it comes to building conceptual links. Moreover, our goal is also to build links with narrative commitment, and to comply with constraints, in particular the time constraint.

In [START_REF] Genest | An Experimant in Document Retrieval Using Conceptual Graphs[END_REF], the authors propose an indexing approach based on conceptual graphs for information retrieval. They mention the support of an ontology and define a valuation function between two conceptual graphs in order to calculate their semantic proximity. Their objective is limited to information retrieval and they do not use any DTD to index the resources. When it comes to building coherent resources from different sources, information retrieval is not enough. We need a narrative/pedagogic structural organization. With the simple objective of building guided tours in hypermedia, Walden's Paths [START_REF] Shipman | Using Paths in the Classroom: Experiences and Adaptations[END_REF] allows teachers to define a directed path made up of resources extracted from the web out of their context, with annotations as meta-data in order to build a rhetoric structure. The introduction of narrative and pedagogic roles is one step towards the introduction of rhetorics. The roles of links and link typing have already been investigated [START_REF] Ranwez | Pedagogical Ontology and Teaching Strategies: A New Formalization to Improve Life-Long Learning[END_REF][START_REF] Wentland | Hypertexte pédagogique et aide conceptuelle : le projet HIPOCAMPE[END_REF]. But they are only considered as a means to add information to traditional links to help users build more consistent, argumentative paths.

In conceptual navigation, roles are dynamically defined in relation to the resource contents and the ontology of the domain. [START_REF] Cleary | Practical Method for Automatically Generating Typed Links[END_REF] investigates dynamic role construction. But conceptual navigation really begins when the systems dynamically assemble resources. Few examples can be found. [START_REF] Parunak | Ordering the Information Graph[END_REF] presents dynamic hypertext building based on NLG (Natural Language Generation) where the ontology of the domain clearly drives the composition along with linguistic rules. AHAM [START_REF] De Bra | AHAM: A Dexterbased Reference Model for Adaptive Hypermedia[END_REF] comes close to our ontologydriven approach. It uses a domain model, a user model and a teaching model which consists of pedagogical rules to build adaptive hypermedia courses. Another similarity with Karina is the use of conceptual prerequisites. The AHAM authoring interface, still under study, will be based on the language Z, whilst Karina proposes an interface based on conceptual graphs to create XML compliant descriptions of resources. Sybil relies also on conceptual graphs to allow deep knowledge modelling and powerful conceptual operations.

In Karina's approach to conceptual navigation, the time constraint is used in order to prune the space search of related resources and to give a limit to the final delivery. This facility relies on the fact that the initial resources have been indexed with a time value which corresponds to the reading time hypothesized by the person who indexes. But, as [START_REF] Marchionini | Content + Connectivity => Community: Digital Resources for a Learning Community[END_REF] puts it, "reading time is a difficult thing to determine, and would benefit from further research". It may depend on the conceptual complexity of the content, its volume, the medium, the user's personality, intention, and level of acquaintance with the subject. Moreover, all these parameters may combine to define the reading time. Notwithstanding these difficulties, most teachers are able to give an average time length to the study of pedagogical material, be this value intuitive. We also explore a statistical definition of the time value from learners' practices. Other more objective constraints than time should also be taken into account, such as economical constraints when an on-line resource must be paid for. We think with Varian [START_REF] Varian | The Economics of Search, Keynote Address[END_REF] that this temporal and economical dimension in hypermedia navigation should deserve more attention because it is clearly a very important parameter for efficient navigation on the World Wide Web.

CONCLUSION: BEHAVIOR WITH (CONCEPTUAL) STRUCTURE

In [START_REF] Shipman | Using Paths in the Classroom: Experiences and Adaptations[END_REF], Rosenberg distinguishes two ways of approaching cybertext. On the one hand, hypertext systems are constructed with units bearing their own user interface, the navigation dynamics being localized in the units of text through visible links. On the other hand, full programmed cybertexts hide the dynamic hyper construction from the user. "The user is present as the algorithm unfolds its results, much as the viewer is present at the cinema". Rosenberg calls the first approach structural, and the second one behavioral. Conceptual navigation can be considered to be of the second kind. However, we do not expect an algorithm to build a meaningful path only through pure calculation even with good bricks of information. We have introduced two conceptual structures, the domain ontology and some argumentative ontology, be it narrative or pedagogic, to drive the hidden part of programmed cybertexts. Like any living entity, we think that programmed cybertexts should be endowed with a behavior and a structure. From such a point of view, the difference between hypertext and programmed cybertexts does no lie in the presence or the absence of a structure, but it is more about the nature of the structure, its localization, its visibility and which entity makes use of it. In hypertext, the structure is made up of visible links which are manipulated by the reader. In programmed cybertext, the structure is a deep hiddenknowledge structure which drives the engine. But most of all, in hypertext the structure is localized in the resources.

In programmed cybertext, and particularly conceptual navigation, the structure is independant from the resources and the resources are self-contained. These last qualities allow better flexibility, interoperability and reusability of all components.

Figure 2

 2 Figure 2

•

  It automatically builds intentional weighted semantic links between documents or parts of documents.• It gives roles (affordances, pragmatics) to these links, taking into account the ontology of the domain and an ontology of argumentation.

• It chooses among these links which are the best according to a particular context and a particular reader's intention.

• It assembles the resources using the most appropriate narrative or pedagogic strategy amongst possible strategies. During this computation, it complies with the user's time constraint, or any other economical constraint.

  particular learner. The courses are composed of pedagogical resources that are available on line. Karina's long range objective is to propose several conceptual navigation strategies, among which the system will choose the best adapted to the learner's needs. For the moment, only the backward conceptual navigation strategy has been implemented. It will be discussed later on. Besides these strategies, Karina still allows for navigation using the traditional methods, i.e. word indexation and hyperlinks. Three main phases in the conceptual navigation process can be distinguished in Karina. These phases are summarized below. The first two phases are discussed in detail in other sections since they are at the core of conceptual navigation.

	Phase one: document selection and indexation. The first
	phase is the production or the selection of resources that
	may be used or reused in the construction of training
	courses. These resources may have been produced either
	by a unique author or by different authors. Karina does
	not speculate on who is in charge of producing/selecting
	resources or how. The resources are indexed. A DTD
	(Document Type Definition), written in XML, is used to
	structure indexing. Help is obtained from indexing tools
	which propose a vocabulary and semantic constraints
	derived from an ontology of the domain.

  This element contains a conceptual description of other concepts or conceptual relations with which the content of the resource is in relation. Conceptual expansion consists in looking for resources that best match this conceptual description. It is a sort of open conceptual digression, a conceptual counterpart of links in hypertext.

The Karina DTD and the ontology DTD can be freely downloaded at the address: http:// www.site-eerie.ema.fr/~multimedia

ACKNOWLEDGMENTS

The Sybil project is sponsored by Digital Equipment, CEC Karlsruhe, Deutschland. The participants are Leidig T., from CEC Karlsruhe, Ranwez S. (main developper), and Crampes M., from Ecole des Mines d'Ales (EMA), France. Karina, was developped under a contract with the French Ministry of Industry. The developpers are Crampes M., Bayard L., Gamrane N., Jacob O., Romagnoli E., from EMA, Gelly A., and Uny P., from Ecole Supérieure des Ingénieurs de Marseilles.


Abdelilah Bannour

Mohamed Elomari

Lakhal El Khadir

Mohamed Directeur

La Direction Des

Routes Afechkar

Bouchra B Lamrini

Calibration study of HDM-4 Model of structural cracking models for flexible pavements in Moroccan Context

Keywords: HDM-4, Flexible pavements deterioration, Structural cracking, test section

Calibration study of HDM-4 Model of structural cracking models for flexible pavements in Moroccan Context.

BANNOUR Abdelilah * (1) , EL OMARI Mohamed (1) , LAKHAL El Khadir (1) , AFECHKAR Mohammed (2) , LAMRINI Bouchra (3) et JOUBERT Pierre (4) (1): Automation Laboratory of Environment and Transfer Processes, Faculty of Science Semlalia, Cadi Ayyad University, Marrakech, Morocco.

(2): Director of the National Centre for Studies and Road Research -NCSRR, Rabat, Morocco. * abdelilah.bannour@ced.uca.ma

Résumé

Le présent travail a pour objet, l'étude de calage du sous-modèle de fissuration structurelle qui a une influence assez notable sur le processus de détérioration des chaussées routières, pour ce faire on est basé sur l'exploitation de la base de données routière, résultant du relevé visuel effectué par le CNER (Centre National d'Eudes et de Recherches Routières). Un suivi étalé sur 6 ans de 99 sections test implantées sur le territoire national en considérant 10 familles de structures modifiant les matériaux constituant le corps de chaussée ainsi que leurs épaisseurs. Méthode de calage des sous-modèles de comportement de chaussées recommandée par les concepteurs du modèle HDM-4 est la méthode "windows". Afin d'établir des facteurs d'ajustement notés Kcia pour l'initiation de fissures et Kcpa pour la propagation de fissures. Cette méthode est basée sur un traitement des données en utilisant la régression linéaire simple avec la ligne d'égalité. En utilisant les équations de fissuration structurelle du modèle HDM-4 par une simulation sur Excel, on aboutit lors de cette étude à des résultats permettant un bon calage par l'utilisation les coefficients suivants : Kcia = 1,27 et Kcpa = 0.79.

Abstract

The present work aims the study of timing of structural crack Sub-model that has a fairly significant influence on the process of deterioration of road pavements, to do this it is based on the exploitation of the road database resulting visual survey conducted by the NCSRR (National Centre of Studies and Road Research). A follow-up spread over 6 years of 99 test sections located on national territory by considering 10 family's structures modifying the floor body constituting materials and their thicknesses. Calibration method of road behavior of submodels recommended by the designers of the HDM-4 model is the method "windows". To establish adjustment factors noted Kcia for initiation of cracks and Kcpa for crack propagation. This method is based on a data processing using simple linear regression with equal line. Using the equations of structural cracking HDM-4 model a simulation on Excel, we arrive in this study to results allowing good timing by using the following coefficients: Kcia = 1.27 and Kcpa = 0.79.

1-Introduction

The World Bank has developed computational systems to support professionals involved in pavement management. These models, developed in the 70's, have been important tools for economic development of highways and their technical evaluation1. Moreover, these systems incorporate performance models, which must be calibrated according to specific conditions of the country, or region where they are to be used [START_REF] Heman De Solminihac | Calibration of structural cracking models for asphalt pavements: HDM-4 case[END_REF].

Resource allocation decisions concerning the design, preservation and improvement of transportation infrastructure facilities are evaluated and selected based on their long-term economic consequences evaluation involves processing data related to current condition1 and using them to forecast the effect of these decisions on future condition [START_REF] Chu | Estimation of dynamic performance models for transportation infrastructure using panel data[END_REF].

Planning of road maintenance and development activities, setting priorities for construction, reconstruction and repair of automobile roads, preparing economic evaluation and project justification are inevitably connected with the need to forecast road pavement condition. For this purpose, the pavement deterioration models are used, which are often integrated into more complex computing system [START_REF] Braga | Adaptation of pavement deterioration models to Lithuanian automobile roads[END_REF].

Our study is part of the problem of optimizing maintenance roads that generate enormous investment costs. In parallel, the budgets allocated to these operations are very limited.

2-Need and objectives of the study

All projections and evaluations depend upon predictions of the rate at which roads in the network will deteriorate and of the effectiveness of different maintenance options, dependent on the current state and projected trends of traffic, economic growth and available resources. At the heart is a model of road deterioration, which may be as simple as a fixed estimate of life (for example, "paved roads need rehabilitation every twenty years") or may be more complex, for example, taking into account the traffic projections, existing road structure, and specific standards of service and design [START_REF] Paterson | Road Deterioration and Maintenance Effects: Models for Planning and Management[END_REF].

Maintenance and preservation of road assets is a priority road policy of the Government in view of its replacement value estimated at over 50 billion dirhams. Thus, the Government intends to raise the service level of the road network through effective maintenance.

In 2014, these operations have focused on building 1000km, widening 630km and the repair or reconstruction of 40 bridges. This road asset preservation effort will continue in 2015 through maintenance of a 2,230-linear km, including 1,130 km covering 600km in Km 500 in strengthening and enlargement [START_REF]Projet de loi de finance Marocaine pour l'année budgétaire[END_REF].

The aim of this study was to propose performance-based model calibration recommendations and to establish the most suitable and appropriate calibration factors for different HDM-4 pavement deterioration models for flexible pavements in different terrains of Morocco. This will also increase predictability of the models and the possibility of conducting an efficient pavements management process.

3-Highway development and management model (HDM-4)

Among the most answered models for optimization of road maintenance is HDM4. It is a powerful system: When considering the applications of HDM-4, it is necessary to look at the highway management process in terms of the following functions [START_REF] Henry | Série développement et gestion des routes Vol. 1-Traduction en langue française effectuée par JOUBERT Pierre : Vue d'ensemble de HDM-4 Version2.0[END_REF]: (Planning; Programming; Preparation and Operations). It is important that prior of using HDM-4 for the first time in any country, the system should be configured and calibrated for local use. In the HDM-4, thus no attempt for calibration without calibration, however, HDM-4 relationships predict "average expected" values for various variables that can naturally involve large margins of error depending on the situation. Thus, the study will produce only an indicative evaluation of the suitability of HDM-4 as a design tool [START_REF] Tsunokawa | Optimal Pavement Design and Maintenance Strategy for Developing Countries: An Analysis using HDM-4[END_REF]. The HDM-4 relationships for predicting the time to initiation and progression of all structural cracking are as follows:

a-Initiation of all structural cracking [START_REF] Morosiuk | The highway development and management series Vol.6: Modelling road deterioration and works effects, Version 2[END_REF]:

Its given by the following formulas [START_REF] Heman De Solminihac | Calibration of structural cracking models for asphalt pavements: HDM-4 case[END_REF] In our study, we are taking the following parameters: a0= 13, 20; a1= 0; a2= -20,7. Where: ICA is time to initiation of all structural cracks, in years; Kcia is the cracking initiation factor; SNP is the average annual adjusted structural number of the pavement; YE4 is the annual number of equivalent standard axles, in millions per lane; CRT is the crack retardation time due to maintenance, in years and CDS is the construction defects indicator for bituminous surfacing.

b-Progression of all structural cracking [8]:

The progression of all structural cracking parameter is given by:

(2)

Progression of all cracking commences when δtA> 0 or ACAa > 0. In our study, we are considered the following parameters: a0= 1,76; a1= 0,32 Where dACA is the incremental change in area of all structural cracking during analysis year, in per cent of total carriageway area; Kcpa is the cracking progression factor ; CRP is the retardation of cracking progression due to prenventive treatment, given by CRP = 1-0,12CRT, SCA = min (ACAa, (100-ACAa)) and ACAa is the area of all structural cracking at the start of the analysis year, in per cent.

4-Calibration methodology

To calibrate a pavement performance model, it is necessary to possess a sufficient group of distress data. HDM consists of a series of sub-models that address different aspects of the analysis. Each of these submodels requires certain input data and each produces its own output. In order to apply the model correctly, one needs to ensure that HDM is given the appropriate input data and has been suitably calibrated, [START_REF] Bennett | Guide to calibration and adaptation of HDM-4[END_REF].

The process of calibration then consists of determining the adjustment factors (ki), which will achieve the best agreement between the model's predictions and the field data. In order to implement the HDM-4 model, many kinds of procedures to calibrate road networks, vehicle fleets, economic indices, environmental factors, deterioration models, and unit cost are required for matching local conditions. Among others, a calibration procedure for deterioration models is one of the most important requirements for successful implementation, [START_REF] Han | Section-based multifunctional calibration method for pavement deterioration forecasting model[END_REF].

The "windows" methodology has been used here as it offers the possibility of both relying upon a broader inference space and of integrating data gathered in previous studies with new data. Moreover, this methodology permits section distress measurements to be made in a brief period of time, and consequently a greater number of sections and categories can be evaluated. This is a major advantage of the "windows" method, because the "test sections" calibration methodology makes it necessary to continue measuring data for each selected section for an extended period of time in order to obtain reliable predictive data, [START_REF] Tukaram | Highway Development and Management Model (HDM-4): calibration and adoption for low-volume roads in local conditions[END_REF].

In our study, we simulate the degradation process by integrating Excel equations recommended by the designers of HDM-4, as indicated in Volume 6 of the HDM-4 documentation, in fact, some sections of the simulation test by turning the software gave closer result sets of those data by Excel. While, on one hand we have this procedure saves a lot time instead of running the software requiring enough data that are sometimes unnecessary to calibrate and adapt the model for a well-defined context like ours. Secondly mathematical validation of the procedure will be more relevant and credible.

Experimental study

The planning of maintenance and the importance of investment resulting require mastery of evolution degradation Moroccan roads laws. In this context, the Road Directorate under the tutelage of the Minister for Infrastructure, Transport and Logistics, has established a list of 99 tests sections to follow the real evolution of deterioration depending on their ages. These sections were selected; such test section has 1000m (1Km) of the length, to cover all the diversity of the national network (subgrade, traffic, climate and nature of the surfacing layer).

The approach is to follow these sections in time, requires ten, fifteen years to see in this context the Roads has granted to the National Research Centre for Studies Road NRCSR ensure that mission monitoring during the period from 1996 to 2001 (6 years), including the collection of data relating to different modes of degradation test sections was made using High performance equipment.

Since 1996 a visual inspection of pavement distress has been carried out each year, the aim objective of a visual inspection is to observe pavement cracks, raveling, potholes, and bleeding of binder. The distresses identified were registered in special Road Data Base. But in the present study we will focus only on the degradation on the structural cracking, including its influence on the pavement behavior is noticeable against other types of deterioration.

In the same framework, Pavement deflection on each test section was measured using the Benkelman Beam, and the pavement roughness measured by equipment that records the longitudinal profile by an approximation to a longitudinal profile from a moving vehicle.

4.2-Characteristics of sections used in the development of the cracking model

In our study, the characteristics of sections used in the development of the cracking model are presented in the next table.

4.3-Variables and experiment factorial design

From the analysis of the performance model equations, it is evident that the pavement's evolution over time fundamentally depends on four global variables traffic, pavement age (calculated from the date of construction or most recent maintenance), dominant climatic conditions and structural capacity. These variables help to define the initiation as well as the progression of the distress.

In order to achieve a certain degree of reliability in selecting a group of roads with sufficiently similar characteristics. Thus, the 99 selected tests sections were divided into family structures whose corresponding information is given in the above table 1. The first step consists of defining homogeneous sections according to their most representative variables (structure, traffic, geometry and climate).

The performance data obtained from field observations may then be compared with the data obtained through modelling. For another group of sections or for windows with different characteristics, the data should present different trends, which in turn will modify the k i values obtained when performing the calibration. The details of calibration methodology evaluated in this study are shown in Figure 2.

4.4-Methodology for selection of test sections

Once the factorials of the experiment have been defined, following two simultaneous compatible criteria as has made the selection of representative sections in each cell:

1-Possession of a sufficient number of sections for each cell category in order to be able to make use of a minimum quantity of distress data and with the aim of attaining adequate calibration of the performance prediction models; 2-Ranges sufficiently small in magnitude whenever possible to ensure the greatest similitude and homogeneity among the characteristics of the different pavement types and thus ensure greater reliability with respect to the calibration.

5-Calibration of pavement performance models

The procedure proposed for the statistical calibration of pavement performance models in this study is mainly based on determining the factors that will allow for a more precise or better adjustment of the simulated distress curves with the real performance data. The procedure proposed for calibrating surface distress initiation factors is based on obtaining the coefficient between the observed years of occurrence of the distress to the year of occurrence as predicted by the uncalibrated models. In case of progression factors, the calibration has been done by minimising the squares of the differences between the estimated and observed data or sum of squared differences (SSD). Minimising the SSD will assist in reducing the estimated average error, which in turn will make it possible to locate the calibration factor that ensures the best adjustment of the distress curve and therefore the calibration of the analysed performance model.

The following statistical indicators are used for comparison of models:

, (3) , (4) (5)
AAE: the average absolute error; RMSE : the root mean square error ; R 2 : the goodness-of-fit measure (coefficient of determination) ; n : the number of observations ; O i : the actual value of distress observation i ; O avg : the average value of distress observations ; P i : the predicted value of distress observation.

6-Results and discussion of calibration procedure

6.1-Law of evolution of structural cracking

The determination of this degradation evolution laws will be a tool that will contribute to the evaluation of intervention strategies on the road network eventually leading to the best technical and economic choice of maintenance scenario and maintenance of the road network. Structural cracking is modeled in two discrete phases. In the first, initiation, period the distress has not yet become manifest and the area is zero. After initiation, the area gradually progresses; in the case of cracking this follows a sigmoidal curve as shown in Figure 3. Our Moroccan context, taking into account the 10 families structure and their characteristics, we guess that the initiation phase begins only from an age of 6 in a surface course, and then the phase Trigger increase is up to age 12 almost double the end of the first phase. Therefore, the moderate traffic (heavy weight) remains the main element involved in the delay of the initiation phase. According to summarized data from different surveys conducted for all test sections, we can learn an approach to a law of evolution of structural cracks diagnosed. The evolution of structural cracks is impressive. Was retained a large sample size of the order of n = 73 by making an approximation of this evolution with a polynomial equation of degree 3, with an explanation of structural cracks rate of 86.7%, which high lighted the robustness of the model. This rate of change is explained characterized by the weakest RMSE, which is worth 32,90. It is found that the observed values are adequate with that predicted by the HDM-4 by a rate of variation explained in the order of 85.6% characterized by the weakest RMSE based statistical processing of process data is 1.79. All while keeping a more representative size of the sections selected sample. Based on a representative sample of large size of the order of n=158, reflecting the entire national road network, which make the credible and convincing work covered. Well according to coefficients selected calibration Kcpa and Kcia, can we conclude that structural cracking appears more slowly than predicted HDM-4, meaning that HDM-4 overestimates the occurrence of structural cracks in Moroccan context, and with a moderate rateas well progression of these cracks over time, evolving more slowly than predicted by the HDM-4, then the onset and progression follow a logical evolution taking into account local specificity (traffic, climate structure pavements, etc. ...).

Suggested calibration factors

Otherwise the calibration factor Kcia for the initiation of all structural cracking. By increasing the value of Kcia to 1.27, the time to the initiation of all structural cracking is four thirds, implying that the pavement will last longer before cracks appear than that predicted by default by HDM-4. And decreasing the calibration factor Kcpa for the progression of all structural cracking, to 0.79, implies that the pavement will deteriorate, in terms of the rate of crack progression, three quarter less rapidly as that predicted by default by HDM-4. This means that the explanatory variables would be adequately incorporated into the HDM-4 equations that predict superficial distress, corroborating the model's soundness. Within this framework, there is the reduction of amplitudes guessed between observed and predicted as shown in Figure 4, hence the need for calibration.

7-Conclusion

Since 1996 the National Centre of Studies and Road Research has monitored a factorial of pavement sections to select, improve and calibrate performance prediction models. Data collected from the performance sections indicate that local calibration of models is essential. So, the following conclusions have been made based on this study: ➢ The methodology "windows" allows a more global vision of the distress of the pavement, as it is not restricted to the study of any pavement in particular. Moreover, it makes it possible to evaluate a large number of sections in a short period of time;

➢ The validity of the proposed model was tested to test their effectiveness by comparing distress predictions made by deterioration models calibrated with those actually observed on the pavement sections;

➢ The HDM-4 pavement deterioration models need to be calibrated for the road network in local conditions of Moroccan context, as large variations are found between the suggested calibration factors for initiation and progression of pavement deterioration model in this study and the corresponding default HDM-4 calibration factors as unity.

The calibrated HDM-4 pavement deterioration models in this study can't be used for the prediction of distressed and developing optimal maintenance management strategies for road network of Morocco. Because the full study calibration of HDM-4 requires almost do the same of structural cracking calibration procedure for other Pavement degradation mode such as raveling, potholes, rutting and the resultant is roughness. Well extended following this work, will be a future article scientific as like as perspective.

( 3 )

 3 : Senior Research Engineer (R & D) charge of Research & Development Modeling, Diagnostics and Process Control, France. (4): HDM-4 Consultant, Trainer HDM at Ponts Formation Council of the National School of Bridges and Roads, France.

Figure 1 :

 1 Figure 1: HDM-4, System Architecture [6].

Figure 2 :

 2 Figure2: Calibration methodology adopted in this study,[START_REF] Abou-Rjaili | Development and calibration of network behavioral patterns of rigid pavements MTQ[END_REF]. « Window » technique has been used for calibration of pavement distress models in the present study because it offers the possibility of relying on a broader inference space and the possibility of integrating data gathered in previous studies with the new data for further refinement of pavement distress models. Another reason for suggesting the "window' technique in this study is the absence of long-term historical pavement performance data for Test Sections in Morocco.

Figure 3 :

 3 Figure 3: Law of evolution of structural cracking, Morocco context.

  Different trial calibration factors have been attempted for the test sections and the calibration factor corresponding to the minimum (RMSE) 2 value has been suggested amongst them. The value of suggested calibration factors for initiation and progression structural cracking models are Kcpa = 0,79 and Kcia = 1,27, resulting from the process detailed below:

Figure 4 :

 4 Figure 4: Linear regression line with the equality between the observed crack and that predicted by the HDM-4 Model, eliminating outliers.

Figure 5 :

 5 Significant effect of the calibration on the amplitude variation of structural cracking (a) With calibration where Kcpa=0,79 and Kcia=1,27. (b) Without calibration where Kcpa=1 and Kcia=1.

Table 1 :

 1 Characteristics of sections used in the development of the cracking model.

		Number	Range of	Range of Total	Cumulative	Traffic
	Pavement type	of	Surfacing	surfacing	Trafic loading	Volume
		sections	age (years)	thickness (mm)	(MESA)	(AADT)
	GNT + RS	17	0-7	20	0,004-0,83	69-4191
	BL + PC + RS + GNT + RS	7	1-6	20	0,03-0,81	411-8246
	BL + PC + RS + GBB + EB	10	1-5	40-240	0,33-1,22	1540-17856
	PC + RS + GNT + RS	3	5-9	20	0,1-0,9	330-4191
	BL + RS + GNT + RS	4	5-7	20	0,04-1,30	697-16179
	GNT + RS + EB	1	5	50	0.4-0,5	4502-10162
	BL + PC + RS + EB	4	2-22	40-70	0,4-1	3616-8453
	BL + PC + RS	6	1-7	20	0,02-0,56	478-2350
	GNT + GBB + EB	9	1-6	50-240	0,03-1,8	662-16494
	BL + PC + RS + GNT + EB	5	1-14	50-130	0,57-1,31	3420-17576

BL: Blocking; PC: Broken Stone; GNT: No Treated Gravel; GBB: Asphalt Overlay Gravel; RS: Surfacing; EB: Bituminous mixture.

Table 2 :

 2 Descriptive statistics of correlation between Predicted an Observed cracks.

	Statistics	Predicted cracks	Observed Cracks
	Nb. of observations	159	159
	Minimum	0,000	0,000
	Maximum	49,681	42,000
	Q1	0,000	0,000
	Median	0,000	0,000
	Q3	2,853	0,021
	Average	1,732	0,911
	Variance (n-1)	24,706	19,110
	Standard deviation (n-1)	4,971	4,371

er Congrès International sur les Ingénieries Civile, Mécanique et Electrique pour l'Energie CMEEE

2015-Marrakech