A multi-criteria approach to risk-based sustainability performance scoring: Integrating environmental, structural, and green aspects with fuzzy systems and ensemble learning

Abstract

Current sustainability assessments for buildings often fail to account for the interactions between environmental risks, structural safety, and green performance, resulting in suboptimal prioritization for maintenance and retrofitting. To solve this problem, a novel framework is proposed to evaluate and prioritize the risk-based sustainability performance at the building asset level. The proposed framework integrates three different key aspects, including environmental, structural, and green, due to the multifaceted nature of sustainability. In the presented approach, fuzzy systems are modeled for each of these three aspects to address the uncertainties within the problem. These models introduced three indices: Environmental Risk index (considering factors like fault proximity and vegetation barriers), Structural Safety index (evaluating factors such as material quality and age), and Green index (evaluating factors such as energy efficiency and carbon footprint). The efficiency of the assessment process is upgraded using the results of three ensemble learning models, XGBoost, AdaBoost, and Random Forest, which are trained on a generated database obtained through the fuzzy system evaluations. Due to the complexities existing in the problem under study, a Graphical User Interface (GUI) is also presented based on the final systems, making the use of the computational framework simpler for the user. The framework helps engineers to make more informed decisions regarding maintenance, retrofitting, and redevelopment strategies in a construction project. The successful combination of fuzzy systems with machine learning and providing a user-friendly interface makes this research a significant contribution to advancing sustainable constructions.

Keywords

Structural sustainability / Fuzzy logic / Ensemble learning / Multi-criteria decision making / Risk assessment

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