Enhancing building performance: a bayesian network model to support facility management
- Autores: Rafaela Bortolini
- Directores de la Tesis: Núria Forcada Matheu (dir. tes.)
- Lectura: En la Universitat Politècnica de Catalunya (UPC) ( España ) en 2019
- Idioma: español
- Tribunal Calificador de la Tesis: Miquel Casals Casanova (presid.), Eugenio Pellicer Armiñana (secret.), David J. Edwards (voc.)
- Programa de doctorado: Programa de Doctorado en Ingeniería de la Construcción por la Universidad Politécnica de Catalunya
- Materias:
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- Resumen
The performance of existing buildings is receiving increased concern due to the need to renovate the aging building stock and provide better quality of life for end users. The conservation state of buildings and the indoor environment conditions have been related to occupants’ well-being, health, and productivity. At the same time, there is a need for more sustainable buildings with reduced energy consumption.
Most challenges encountered during the analysis of the performance of existing buildings are associated with the complex relationships among the causal factors involved. The performance of a building is influenced by several factors (e.g., environmental agents, occupant behavior, operation, maintenance), which also generate uncertainties when predicting it. Most previous studies that investigate methods to assess a building’s performance do not consider the uncertainty and are often based on linear models.
Although different stakeholders’ requirements regarding building performance coexist, few studies centered on the implications of these requirements. Previous studies tend to be highly specific on indicators related to a particular performance aspect, overlooking potential trade-offs that may occur between them. Therefore, a holistic and integrated approach to manage the performance of existing buildings has not been explored. Facility managers need an efficient approach to deal with uncertainty, to manage risks, and systematically identify, analyze, evaluate and mitigate factors that may impact the building performance.
Taking into account the aforementioned aspects, the aim of this thesis is to devise a Bayesian network (BN) model to holistically manage the operational performance of buildings and support facility management. The proposed model consists of an integrated probabilistic approach to assess the performance of existing buildings, considering three categories: safety and elements working properly, health and comfort, and energy efficiency. The model also provides an understanding of the causality chain between multiple factors and indicators regarding building performance. The understanding of the relationships between building condition, end user comfort and building energy efficiency, supports facility managers to unwind a causal explanation for the performance results in a reasoning process.
The proposed model is tested and validated using sensitivity analysis and data from existing buildings. A set of model applications are discussed, including the assessment of a building’s performance holistically, the identification of causal factors, the prediction of building performance through renovation and retrofit scenarios, and the prioritization of maintenance actions. Case studies also allow to illustrate the applicability of the model for ensuring that its interactions and outcomes are feasible. Scenario analyses provide a basis for a deeper understanding of the potential responses of the model, helping facility managers to optimize operation strategies of buildings in order to enhance its performance.
The results of this thesis also include data collection methods for the inputs of the proposed BN model. A building inspection system is proposed to evaluate the technical performance of buildings, a text-mining approach is developed to analyze maintenance requests of end users, and a questionnaire is formulated to collect end-user satisfaction regarding building comfort. To conclude, this work proposes the use of Building Information Modeling (BIM) to store and access building information, which are typically disperse and not standardized in existing buildings.
