Abbasnejad, B; Nasirian, A; Duan, S; Diro, A; Prasad Nepal, M and Song, Y (2024) Measuring BIM implementation: A mathematical modeling and artificial neural network approach. Journal of Construction Engineering and Management, 150(5): 04024032, ISSN 0733-9364
Abstract
Evaluating the level of Building Information Modeling (BIM) implementation in construction firms is critical yet challenging in the absence of a quantitative method. This study addresses this gap. The study begins with a literature review that identified 27 BIM implementation enablers, followed by interviews with three firms to score their performance on each enabler. A mathematical model was developed to score a firm's BIM implementation based on each enabler's score. For each firm, 1 million random scenarios are generated to simulate alternative ways by which a firm's enablers' score can be improved. Subsequently, in each simulated scenario, the firm's BIM implementation score is calculated. The simulation results are incorporated into a feature-pairing neural network that has been designed specifically to provide a customized best course of action for each firm's further BIM adoption. The first contribution of this research is providing a comprehensive analysis of the dynamics and interconnectedness of factors influencing BIM adoption in AEC firms, offering insights into effective BIM adoption. The second contribution is proposing a novel quantitative approach for measuring the current level of BIM implementation and providing data-driven advice for steering the BIM implementation process. This research offers a practical contribution by providing companies with a tool to compute their BIM implementation score, allowing comparisons and benchmarking against competitors.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial neural network; building information modeling implementation; mathematical modeling; predictive modeling |
| Index terms: | building information modelling, dynamics, mathematical modelling, implementation, interview, mathematical model, predictive modelling, neural network, literature review, artificial neural network, construction firm, quantitative method, benchmarking |
| Subjects: | data collection methods, mathematical modelling, organization, modelling and simulation, artificial intelligence, data analysis and analytics, contractual arrangements, analytical methods, prediction and forecasting, performance measurement, information systems, systems engineering |
| Topics: | Procurement, Research Practice, Engineering Principles, Business Strategy, Quality Management, Digital Applications |
| 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