Selvam, G; Kamalanandhini, M; Velpandian, M and Shah, S (2025) Duration and resource constraint prediction models for construction projects using regression machine learning method. Engineering, Construction and Architectural Management, 32(9), pp. 5743-5763. ISSN 0969-9988
Abstract
Purpose - The construction projects are highly subjected to uncertainties, which result in overruns in time and cost. Realistic estimates of workforce and duration are imperative for construction projects to attain their intended objectives. The aim of this study is to provide accurate labor and duration estimates for the construction projects, considering actual uncertainties. Design/methodology/approach - The dataset was formulated from the information collected from 186 construction projects through direct interviews, group discussions and questionnaire methods. The actual uncertainties and exposure conditions of construction activities were recorded. The data were verified with the standard guideline to remove the outliers. The prediction model was developed using support vector regression (SVR), a machine learning (ML) method. The performance was evaluated using the widely adopted regression metrics. Further, the cross validation was made with the visualization of residuals and predicted errors, ridge regression with transformed target distribution and a Gaussian Naive Bayes (NB) regressor. Findings - The prediction models predicted the duration and labor requirements with the consideration of actual uncertainties. The residual plot indicated the appropriate use of SVR to develop the prediction model. The duration (DC) and resource constraint (RC) prediction models obtained 80 and 82% accuracy, respectively. Besides, the developed model obtained better accuracy for the training and test scores than the Gaussian NB regressor. Further, the range of the explained variance score and R2 was from 0.95 to 0.97, indicating better efficiency compared with other prediction models. Research limitations/implications - The researchers will utilize the research findings to estimate the duration and labor requirements under uncertain conditions and further improve the construction project management practices. Practical implications - The research findings will enable industry practitioners to accurately estimate the duration and labor requirements, considering historical uncertain conditions. A precise estimation of resources will ensure the attainment of the intended project outcomes. Social implications - Delays in construction projects will be reduced by implementing the research findings, which significantly ensures the effective utilization of resources and attainment of other economic benefits. The policymakers will develop a guideline to develop a database to collect the uncertainties of the construction projects and relatively estimate the resource requirements. Originality/value - This is the first study to consider the actual uncertainties of construction projects to develop RC and DC prediction models. The developed prediction models accurately estimate the duration and labor requirements with minimal computational time. The industry practitioners will be able to accurately estimate the duration and labor requirements using the developed models.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | duration constraint; machine learning; prediction; resource constraint; uncertainties |
| Index terms: | prediction model, overrun, accuracy, interview, exposure, dataset, machine learning, visualization, resource constraint, questionnaire, validation, practitioner, construction project, methodology, estimation, project outcome, database, construction activity, efficiency, variance, labour requirement, estimate, duration, construction project management |
| Subjects: | project controls, data management, measurement and scaling, management, design practice, performance management, public and environmental health, project management theory and practice, professional development, artificial intelligence, prediction and forecasting, financial and cost management, data collection methods, research methods, production management, operations management, construction operations, project completion, practitioner |
| Topics: | Site Management, Time Control, Design Practice, Digital Applications, Roles and Professions, Cost Management, Research Practice, Information Management, Quality Management, Health and Safety, Project Management |
| 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