Wang, P; Wang, K; Huang, Y and Fenn, P (2024) A contingency approach for time-cost trade-off in construction projects based on machine learning techniques. Engineering, Construction and Architectural Management, 31(11), pp. 4677-4695. ISSN 0969-9988
Abstract
Purpose: Time-cost trade-off is normal conduct in construction projects when projects are expectedly late for delivery. Existing research on time-cost trade-off strategic management mostly focused on the technical calculation towards the optimal combination of activities to be accelerated, while the managerial aspects are mostly neglected. This paper aims to understand the managerial efforts necessary to prepare construction projects ready for an upcoming trade-off implementation. Design/methodology/approach: A preliminary list of critical factors was first identified from the literature and verified by a Delphi survey. Quantitative data was then collected by a questionnaire survey to first shortlist the preliminary factors and quantify the predictive model with different machine learning algorithms, i.e. k-nearest neighbours (kNN), radial basis function (RBF), multiplayer perceptron (MLP), multinomial logistic regression (MLR), naïve Bayes classifier (NBC) and Bayesian belief networks (BBNs). Findings: The model's independent variable importance ranking revealed that the top challenges faced were the realism of contractual obligation, contractor planning and control and client management and monitoring. Among the tested machine learning algorithms, multilayer perceptron was demonstrated to be the most suitable in this case. This model accuracy reached 96.5% with the training dataset and 95.6% with an independent test dataset and could be used as the contingency approach for time-cost trade-offs. Originality/value: The identified factor list contributed to the theoretical explanation of the failed implementation in general and practical managerial improvement to better avoid such failure. In addition, the established predictive model provided an ad-hoc early warning and diagnostic tool to better ensure time-cost implementation success.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | novel model; project management; risk management; questionnaire survey |
| Index terms: | dataset, machine learning, multinomial, construction project, strategic management, methodology, questionnaire, model accuracy, contingency approach, bayesian belief network, survey, early warning, realism, implementation, time-cost trade-off, project management, monitoring, risk management, independent variable, multilayer, critical factor, Delphi survey, logistic regression |
| Subjects: | contractual arrangements, analytical methods, organization, research methods, production management, financial risk, artificial intelligence, data collection methods, risk assessment, control systems, data management, probabilistic model, statistical analysis, management, education and knowledge transfer, quantity surveying, specialized materials and systems, project management theory and practice |
| Topics: | Project Management, Engineering Principles, Procurement, Risk Management, Digital Applications, Site Management, Organizational Design, Research Practice, Construction Materials, Cost Management, 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