Building Information Modelling (BIM) is now a globally recognised phenomenon, though its adoption remains inconsistent and variable between and within the construction sectors of different countries. BIM technology has enabled a wide range of functional applications, one of which, ‘4D BIM’, involves linking the tasks in a project’s construction schedule to its object-orientated 3D model to improve the logistical decision making and delivery of the project. Ideally, this can be automatically generated but in reality, this is not currently possible, and the process requires considerable manual effort. The level of maturity and expertise in the use of BIM amongst the project participants still varies considerably; adding further obstacles to the ability to derive full benefits from BIM. Reflecting these challenges, two case studies are presented in this paper. The first describes a predominantly manual approach that was used to ameliorate the implementation of 4D BIM on a project in Paris. In fact, there is scope for automating the process: a combination of BIM and Artificial Intelligence (AI) could exploit newly-available data that are increasingly obtainable from smart devices or IoT sensors. A prerequisite for doing so is the development of dedicated ontologies that enable the formalisation of the domain knowledge that is relevant to a particular project typology. Perhaps the most challenging example of this is the case of renovation projects. In the second case study, part of a large European research project, the authors propose such an ontology and demonstrate its application by developing a digital tool for application within the context of deep renovation projects.

Buildings have a significant impact on energy consumption and carbon emissions. Smart buildings are deemed to play a crucial role in improving the energy performance of buildings and cities. Managing a smart building requires the modelling of data concerning smart systems and components. While there is a significant amount of research on optimising building energy using the smart building concept, there is a dearth of studies investigating the modelling and management of smart systems’ data, which is the starting point for establishing the necessary digital environment for representing a smart building. This study aimed to develop and test a solution for modelling and managing smart building information using an industry foundation classes (IFCs)-based BIM process. A conceptual model expressed in the SysML language was proposed to define a smart building. Five BIM approaches were identified as potential ‘prototypes’ for representing and exchanging smart building information. The fidelity of each approach is checked through a BIM-based validation process using an open-source visualisation platform. The different prototypes were also assessed using a multi-criteria comparison method to identify the preferred approach for modelling and managing smart building information. The preferred approach was prototyped and tested in a use case focused on building energy consumption monitoring to evaluate its ability to manage and visualise the smart building data. The use case was applied in a real case study using a full-scale demonstrator, namely, the ‘Nanterre 3’ (N3) smart building located at the CESI campus in Paris-Nanterre. The findings demonstrated that an open BIM format in the form of IFCs could achieve adequate modelling of smart building data without information loss. Future extensions of the proposed approach were finally outlined.

Les notions de robustesse structurale sont intégrées dans les codes de conception européens suite à l'effondrement partiel et progressif de la tour Ronan Point de Canning Town à Londres (Angleterre, 16.05.1968). Le cadre réglementaire des Eurocodes définit la robustesse comme l'aptitude d'une structure à résister à des événements accidentels dits identifiés (incendies, explosions, chocs) ou non identifiés (conséquences d'une erreur humaine, attentats) sans présenter de dégâts disproportionnés par rapport à la cause d'origine. Cette définition incite les ingénieurs à inclure dans les procédures de conception les notions de dommage initial (défaillance locale) et de dommage disproportionné (défaillance globale). L'objectif de ces travaux de thèse est de développer un outil de simulation de la robustesse de structures lorsque des incertitudes de sollicitations (évènement accidentel) et / ou une faute de dimensionnement (conception ou de réalisation) interfèrent avec les dimensionnements réglementaires. La robustesse est évaluée à travers un indice compris entre 0 (structure peu robuste) et 1 (structure très robuste) et calculé à partir des probabilités de défaillance initiale et globale. Cette thèse propose une méthodologie reposant sur la recherche d’arbres complets d'évènements illustrant l'ensemble des cheminements potentiels d'une défaillance initiale localisée jusqu'à la ruine globale. L'approche développée s'applique aux structures hyperstatiques, dans lesquelles la rupture d'un ou plusieurs éléments n'entraine pas systématiquement la ruine de l'ensemble de la structure. En effet, les éléments non endommagés restants peuvent être en mesure de supporter les chargements externes par une redistribution des efforts internes.La procédure est illustrée dans les cas de structures unidimensionnelles hyperstatiques de poutres bi-encastrées et d'un portique référencé dans les normes et classiquement étudié dans la littérature. Le mode local de défaillance de nos simulations est la formation d'une rotule (fragile ou plastique) lorsque le moment sollicitant appliqué atteint la valeur du moment résistant d'une section droite. Deux types de lois probabilistes, Gaussiennes et Log-normales, sont testées par l'approche développée et par des simulations Monte-Carlo. Les variables aléatoires choisies peuvent être indépendantes ou corrélées. Nous présentons les résultats sous forme d’arbres d'évènements comportant l'ensemble des branches exclusives, sans intersection entre branches issues d’un même nœud. Cette spécificité permet de calculer des indices caractérisant la robustesse de la structure selon chaque scénario.L'analyse de l’arbre des évènements et des indices de robustesse permet de mettre en évidence les fragilités potentielles pouvant engendrer une défaillance généralisée d'une structure vis-à-vis d’accidents ou d’actes de malveillance. La méthode développée fournit un outil de simulation et de diagnostic efficace, tant en phase de conception qu'en phase de réhabilitation, permettant d'envisager le renforcement de bâtis existants ou futurs et d'assurer la sécurité des personnes et des ouvrages environnants.

In the context of the energy transition and Senegal’s objective to increase the share of renewable energy to 20%, this study investigates the reliability of an off-grid photovoltaic (PV) power plant located in the village of Badiari, Sédhiou region, in the south of the country. Over a three-month period (June to August), corresponding to the rainy season, the 5 kWp production system was continuously monitored using an intelligent SMA inverter, enabling minute-by-minute data collection. The analysis reveals a clear mismatch between the system’s generation capacity and the users’ energy demand. The lead-acid batteries (1,000 Ah) undergo recurrent deep discharges, occasionally reaching critical levels (21 % state of charge), significantly shortening their operational lifespan. Simultaneously, the backup generator, intended to mitigate these deep discharges, is seldom activated. Frequent overvoltage events, shutdowns due to extreme temperatures (up to 80 °C at the heat sink), and islanding phenomena highlight both system imbalance and structural vulnerability. Reliability metrics were quantified, with a Mean Time Between Failures (MTBF) of 10,907 minutes and a Mean Time To Repair (MTTR) of 43 minutes in June, compared to 8,671 minutes and 329 minutes, respectively, in August. These values underscore the irregularity of energy supply. The findings emphasize the need to reconsider the sizing and maintenance strategies of rural solar installations. Recommended measures include increasing battery bank capacity (up to 2,500 Ah), introducing power limiters for consumers, and improving the thermal management of the technical room (ventilation, insulation). Implementing these strategies is expected to enhance local energy autonomy and improve the long-term sustainability of solar infrastructures.