Khanh, H D; Kim, S Y and Linh, L Q (2023) Construction productivity prediction through Bayesian networks for building projects: Case from Vietnam. Engineering, Construction and Architectural Management, 30(5), pp. 2075-2100. ISSN 0969-9988
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 (m2/h), reinforcing steel (ton/h) and concrete (m3/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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Bayesian networks; building project; construction management; productivity; structural work |
| Index terms: | literature review, accuracy, survey, reinforcing steel, Vietnam, manager, body of knowledge, conceptual framework, construction sector, complexity, methodology, practitioner, questionnaire, formwork, productivity, bayesian network, critical factor, case study, modelling, construction productivity, labour productivity, sampling |
| Subjects: | practitioner, risk assessment, data collection methods, analytical methods, knowledge management, data analysis and analytics, operations management, building materials, professional development, Geography, research methods, management, theoretical framing, systems engineering, industry analysis, probabilistic model, building construction |
| Topics: | Site Management, Digital Applications, Construction Technology, Roles and Professions, Risk Management, Construction Materials, Engineering Principles, Information Management, Project Management, Geographical Context, Research Practice, Business Strategy |
| Descriptive scope: | 5 PCTEA |
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