This paper presents an extension of the BIM technology that allows to manage information during the entire lifecycle of an AEC project. Usually, AEC projects and facility management are dissociated. Our Building Information System plays a central role in the improvement of the design and the management process. The building activity generates a great number of data and information of various kinds. The management and the communication of these data by the various participants is complex. Our design and management methods use IFC files to facilitate the sharing process for a better qualification and validation of data.

Abstract:Ontologies nowadays have become widely used for knowledge representation, and are considered as foundation for Semantic Web. However with their wide spread usage, a question of their evaluation increased even more. This paper addresses the issue of finding an efficient ontology evaluation method by presenting the existing ontology evaluation techniques, while discussing their advantages and drawbacks. The presented ontology evaluation techniques can be grouped into four categories: gold standard-based, corpus-based, task-based and criteria based approaches.

Existing literature reveals that unsafe worker movement behaviors are one of the major reasons of construction site fatalities resulting in serious collisions with site objects and machinery. For capturing worker movements in dynamic construction environments which involves moving and changing objects, a solution based on semantic trajectories and Hidden Markov Model (HMM) is presented in the form of four subsystems. First, a real-time data collection and trajectory pre-processing subsystem is constructed for extracting multifaceted trajectory characteristics and stay locations of the workers using spatio-temporal data that will help in recognizing the important regions in the building for categorizing the worker movements. Second, to enable the desired semantic insights for better understanding the underlying meaningful worker movements using the contextual data related to the building environment, an ontologybased STriDE (Semantic Trajectories in Dynamic Environments) model is applied which has an ability to track information about the evolution of moving and changing building objects, and outputs semantic trajectories. The third subsystem uses the Hidden Markov Model (HMM) which is the most preferred probabilistic approach in the literature for describing the object behavior in time. An entire set of trajectories belonging to a stay location (a semantic region) is analyzed by categorizing the worker movements into four states using the HMMs along with the Viterbi algorithm. In the end, the output of the Viterbi algorithm is visualized using a BIM model for identifying the most probable high-risk locations involving sharp worker movements and rotations. The developed system will help safety managers in monitoring and controlling building activities remotely in dynamic environments by better understanding the worker behaviors for an improved safety management in day-to-day building operations as well as by preventing other workforce from accessing such hazardous locations which involve risky movements.

The term "disaster management" comprises both natural and man-made disasters. Highly pervaded with various types of sensors, our environment generates large amounts of data. Thus, big data applications in the field of disaster management should adopt a modular view, going from a component to nation scale. Current research trends mainly aim at integrating component, building, neighborhood and city levels, neglecting the region level for managing disasters. Current research on big data mainly address smart buildings and smart grids, notably in the following areas: energy waste management, prediction and planning of power generation needs, improved comfort, usability and endurance based on the integration of energy consumption data, environmental conditions and levels of occupancy. This paper aims presenting a systematic literature review on the applications of big data in disaster management. The paper will first presents the visual definition of disaster management and describes big data; it will then illustrate the findings and gives future recommendations after a systematic literature review.

Thousands of fatalities are reported from the construction industry every year and a high percentage of them are due to the unsafe worker movements which resulted in falling from heights, transportation accidents, exposure to harmful environments, and striking against or being struck by the moving equipment. To reduce such fatalities, a system is proposed to monitor worker movements on a construction site by collecting their raw spatio-temporal trajectory data and enriching it with the relevant semantic information. To acquire the trajectories, the use of an indoor positioning system (IPS) is considered. Bluetooth beacons are used for collecting spatio-temporal information of the building users. By means of an Android-based mobile application, neighboring beacons' signals are selected, and a geo-localization technique is performed to get the unique pairs of users' location coordinates. After pre-processing this collected data, three semantic enrichment techniques are used to construct semantically enriched trajectories which are as follows: (1) enrichment with the semantic points which maps site location identification to the trajectory points; (2) enrichment with the semantic lines which relies on the speed-based segmentation approach to infer user modes of transportation; (3) enrichment with the semantic region for mapping a complete trajectory on an actual building or a construction site zone. The proposed system will help in extracting multifaceted trajectory characteristics and generates semantic trajectories to enable the desired semantic insights for better understanding of the underlying meaningful worker movements using the contextual data related to the building environment. Generated semantic trajectories will help health and safety (H&S) managers in making improved decisions for monitoring and controlling site activities by visualizing site-zones' density to avoid congestion, proximity analysis to prevent workers collisions, identifying unauthorized access to hazardous areas, and monitoring movements of workers and machinery to reduce transportation accidents.

There is a need for decision-makers to be provided with both an overview of existing knowledge, and information which is as complete and up-to-date as possible on changes in certain features of the biosphere. Another objective is to bring together all the many attempts which have been made over the years at various levels (international, Community, national and regional) to obtain more information on the environment and the way it is changing. As a result, remote sensing tools monitor large amount of land cover informations enabling study of dynamic processes. However the size of the dataset require new tools to identify pattern and extract knowledge. We propose a model to discover knowledge on parcel data allowing analysis of dynamic geospatial phenomena using time, spatial and thematic data. The model is called Land Cover Change Continuum (LC3) and is able to track the evolution of spatial entities along time. Based on semantic web technologies, the model allows users to specify and to query spatio-temporal informations based on semantic definitions. The semantic of spatial relationships are of interest to qualify filiation relationships. The result of this process permit to identify evolutive patterns as a basis for studying the dynamics of the geospatial environment. To this end, we use CORINE datasets to study changes in a specific part of France. In our approach, we consider entities as having several representations during their lifecycle. Each representation includes identity, spatial and descriptives properties that evolve over time.

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Abstract. In the field of civil engineering, the proliferation of stakeholders and the heterogeneity of modeling tools detract from the quality of the design process, construction and building maintenance. In this paper, we present a Web-based platform lets geographically dispersed project participants-from facility managers and architects to electricians to plumbers-directly use and exchange project documents in a centralized virtual environment using a simple Web browser. A 3D visualization lets participants move around in the building being designed and obtain information about the objects that compose it. This approach is based both on a semantic architecture called CDMF and IFC 2x3. Our framework, based on Building Information Modeling features, facilitates data maintenance (data migration, model evolution) during the building lifecycle and reduces the volume of data.