Building design involves many challenges and requires to take into account the interaction between the building and the users. Different occupant behaviour models implemented with building simulation tools (thermal, air quality, lighting) have been proposed. Among these, models based on the agent approach seem to be the most promising. However, existing models poorly describe human cognition and the social dimension. Moreover, they are often oriented towards a specific use (thermal simulation, waste management) without being transposable to another field, and they require a significant instantiation effort for each new case, making their use difficult. This article proposes an agent-based model called Li-BIM that simulates the behaviour of the occupants in a building and their indoor comfort. Li-BIM model is structured around the numerical modelling of the building-BIM-(with standard exchange format IFC), a highresolution cognitive model, and the coupling with various physical models. Li-BIM simulates the reactive, deliberative and social behaviour of occupants in residential dwellings based on the Belief-Desire-Intention architecture. This model, thanks its ease of use and flexibility, is an operational and relevant tool to support building design process with a human-centred approach. An application of the model is presented, focusing on energy consumption and the inhabitants' comfort. In-situ data obtained from the instrumented house that served as case study have been compared with simulation results from Li-BIM and a standard energy simulation software, demonstrating the reliability of the proposed model.
Life cycle assessment (LCA) has proven its worth in modelling the entire value chain associated with the production of goods and services. However, modelling the consumption system, such as the use phase of a product, remains challenging due to uncertainties in the socioeconomic context. Agent-based models (ABMs) can reduce these uncertainties by improving the consumption system modelling in LCA. So far, no systematic study is available on how ABM can contribute towards a behavior-driven modelling in LCA. This paper aims at filing this gap by reviewing all papers coupling both tools. A focus is carried out on 18 case studies which are analysed according to criteria derived from the four phases of LCA international standards. Criteria specific to agent-based models and the coupling of both tools, such as the type and degree of coupling, have also been selected. The results show that ABMs have been coupled to LCA in order to model foreground systems with too many uncertainties arising from a behaviour-driven use phase, local variabilities, emerging technologies, to explore scenarios and to support consequential modelling. Foreground inventory data have been mainly collected from ABM at the use phase. From this review, we identified the potential benefits from ABM at each LCA phase: (i) scenario exploration, (ii) foreground inventory data collection, (iii) temporal and/or spatial dynamics simulation, and (iv) data interpretation and communication. Besides, methodological guidance is provided on how to choose the type and degree of coupling during the goal and scope phase. Finally, challenging LCA areas of research that could benefit from the agent-based approach to include behaviour-driven dynamics at the inventory and impact assessment phase have been identified.
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