Behavioral determinants towards enhancing construction waste management: A Bayesian Network analysis

Amal Bakshan, Issam Srour, Ghassan Chehab, Mutasem El-Fadel, Jalal Karaziwan

    Research output: Contribution to journalArticlepeer-review

    90 Scopus citations


    In this study, a Bayesian Network analysis is used to define causal behavioral determinants towards improving practices in construction waste management (CWM). For this purpose, a structured survey questionnaire was developed and administered to field workers at construction projects. The collected data was used to develop a probabilistic relational model with single- and multi-factor analysis to assess conditional probabilities underlying various determinants. The results indicate that behavior is highly influenced by attitude, past experience, and social pressure with 21, 20, and 10% higher chance of improvement by these factors, respectively. Behavior in CWM appears to be more sensitive to changes in personal factors such as attitude than corporate factors such as training. When simultaneously controlling all factors, the behavior is improved with personal factors by 9% more than with corporate factors. Additionally, it was found that the probability of having effective CWM practices on-site reaches 83% when workers have a positive attitude towards waste management, are well experienced in CWM practices, and are influenced by social pressure. Achieving this result also requires independency at work and availability of training sessions. The model raises the awareness of construction stakeholders about factors influencing workers’ behavior towards CWM and presents quantified strategies that increase the chances of minimizing the generation of construction waste. It can serve as a motivation and a decision support tool for adopting and implementing sustainable CWM practices.

    Original languageBritish English
    Pages (from-to)274-284
    Number of pages11
    JournalResources, Conservation and Recycling
    StatePublished - 1 Feb 2017


    • Bayesian Network
    • Construction Waste Management
    • Sustainability
    • Worker behavior


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