A Decision Support Tool for Dust Prevention and Control in Construction

Abstract

The prevention and control of construction dust is crucial for minimization of air pollution and the associated health risks posed by ambient particulate matter, representing a critical concern within the realm of sustainable urban and building development. Currently, decisions regarding the prevention and control for construction dust heavily rely on the personal experiences and biases of decision makers, lacking the foundation in rigorous data analysis. This often leads to escalated costs and inefficient utilization of resources, and fails to achieve the desired level of dust control. In our study, we introduce an objective and transparent decision support tool, the Decision Support Tool for Construction Dust Prevention and Control (DST-CDPC), designed to assist decision makers in formulating, monitoring, evaluating, and optimizing construction dust prevention and control (CDPC) schemes. The DST-CDPC is structured around three consecutive modules. The initial scheme module applies a multiobjective optimization model and a multicriteria decision model to identify the most effective CDPC scheme for the entire construction phase at the early stages of a project, with a focus on optimizing dust reduction efficiency and cost. The monitoring and evaluation module involves the deep learning–based and real-time monitoring of on-site dust levels from various perspectives, followed by a comprehensive evaluation based on fuzzy logic and the issuance of dust alerts. The dynamic optimization module offers, in instances where the evaluation outcomes transcend the predetermined threshold, real-time decision-making support to address the unexpected or emergency dust incidents at the alert phase in question. A case study is conducted to demonstrate the practical application of DST-CDPC and highlight its effectiveness. The findings revealed that the DST-CDPC is a robust and efficient tool in the development and optimization of CDPC schemes. This tool can facilitate the advancement of research on dust control and management strategies within complex construction projects.