Wang, M; Yao, G; Yang, Y; Li, R and Deng, R (2025) A decision support tool for dust prevention and control in construction. Journal of Construction Engineering and Management, 151(5): 04025037, ISSN 0733-9364
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | construction dust; decision support; dust prevention and control; environmental management; multiobjective optimization |
| Index terms: | deep learning, fuzzy logic, case study, efficiency, management strategy, environmental management, construction phase, prevention, effectiveness, module, monitoring, particulate, minimization, data analysis, decision support, dust control, air pollution, health risk, construction project, bias, decision-making |
| Subjects: | financial risk, production management, algorithms, architectural elements, sustainability assessment, climate science, environmental engineering, environmental health, decision analysis, probability and distributions, performance management, management, project delivery, data analysis and analytics, data science, artificial intelligence, control systems, data collection methods |
| Topics: | Research Practice, Cost Management, Business Strategy, Design Practice, Digital Applications, Site Management, Project Management, Sustainability, Risk Management, Quality Management |
| Descriptive scope: | 4 PCEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here