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• W4213344681 abstract "Purpose This study aims to focus on exploring the construction productivity of building projects under the influence of potential factors. The three primary purposes are (1) determining critical factors affecting construction productivity; (2) identifying causal relationship and occurrence probability of these factors to develop a Bayesian network (BN) model; and (3) validating the accuracy of predictions from the proposed BN model via a case study. Design/methodology/approach A conceptual framework that includes three performance stages was used. Twenty-two possible factors were screened from a comprehensive literature review and evaluated through expert opinions. Data were collected using a structured questionnaire-based survey and case-study-based survey. The sampling methods were based on non-probability sampling. Findings Worker characteristic-related factors significantly affect labour productivity for a construction task. Construction productivity is dominated by the working frequency of workers (overtime), complexity of the task, level of technology application and accidents. Labour productivity is defined as nearly 50% of the baseline productivity using the BN model created by the caut 2sal relationship and probability of factors. The prediction error of the BN model was 6.6%, 10.0% and 9.3% for formwork (m 2 /h), reinforcing steel (ton/h) and concrete (m 3 /h), respectively. Research limitations/implications The evaluation or prediction of productivity performance has become a necessary topic for research and practice. Practical implications Managers and practitioners in the construction sector can utilise the outcome of this study to create good productivity management policies for their prospective projects. Originality/value Worker-related characteristics are dominant among critical factors affecting labour productivity for a construction task; the proposed BN-based predictive model is built based on these critical factors. The BN approach is highly accurate for construction productivity prediction. The findings of this study can fill gaps in the construction management body of knowledge when modelling construction productivity under the effects of multiple factors and using a simple probabilistic graphic tool." @default.
• W4213344681 created "2022-02-24" @default.
• W4213344681 creator A5009645239 @default.
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• W4213344681 date "2022-02-15" @default.
• W4213344681 modified "2023-10-03" @default.
• W4213344681 title "Construction productivity prediction through Bayesian networks for building projects: case from Vietnam" @default.
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• W4213344681 doi "https://doi.org/10.1108/ecam-07-2021-0602" @default.
• W4213344681 hasPublicationYear "2022" @default.
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