Aghajamali, K; Metvaei, S; Suliman, A; Lei, Z and Chen, Q (2025) Development of a prefabricated construction productivity estimation model through BIM and data augmentation processes. Construction Management and Economics, 43(5), pp. 340-359. ISSN 0144-6193
Abstract
The accuracy of productivity estimates remains a significant challenge due to limited data availability. This research addresses the need for precise productivity estimation in construction by integrating data augmentation techniques, onsite time study data, and Building Information Modeling (BIM) for automated quantity take-offs and design complexity analysis of steel connections. By examining design complexity, the method provides productivity estimates for project zones, sequences, and individual components, improving overall production management. Four data augmentation techniques—normal noise, interpolation, clustering, and Bayesian Linear Regression—were evaluated to enhance time study data. The augmented dataset was used to train an Artificial Neural Network, validated through case studies. The study identified the normal noise method as the most effective, significantly improving time estimation accuracy. Specifically, the proposed approach yielded a 58%–71% enhancement over current industry estimates and a 2.1%–31.1% improvement compared to models without data augmentation. This research enables managers to optimize resource allocation and reduce potential project delays.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Bayesian linear regression; building information modeling; data augmentation; offsite construction and prefabrication; productivity forecasting |
| Index terms: | estimate, construction productivity, prefabrication, clustering, resource allocation, onsite, building information modelling, case study, forecasting, productivity, estimation, dataset, time estimation, project delay, complexity, manager, take-off, production management, accuracy, artificial neural network |
| Subjects: | building construction, practitioner, operations management, operations research, management, quantity surveying, professional development, project controls, data management, information systems, systems engineering, data collection methods, modelling and simulation, financial and cost management, project delivery, prediction and forecasting, resource management, data science |
| Topics: | Engineering Principles, Project Management, Business Strategy, Cost Management, Information Management, Research Practice, Roles and Professions, Construction Technology, Digital Applications, Time Control, Site Management |
| Descriptive scope: | 4 PCEA |
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