Jafary, P.; Shojaei, D.; Rajabifard, A. and Ngo, T. (2026) AI-augmented construction cost estimation: An ensemble natural language processing (NLP) model to align quantity take-offs with cost indexes. International Journal of Construction Management, 26(8), pp. 1508-1526. ISSN 1562-3599
Abstract
Accurate construction cost estimation is crucial for the financial success of construction projects. Effective financial management in construction heavily relies on a robust and reliable estimation system. Building Information Modeling (BIM) has emerged as a powerful tool, providing precise quantities for various building elements through Quantity Take-Offs (QTOs). Traditionally, Quantity Surveyors (QSs) match these QTOs with cost indexes, a task that is both labor-intensive and prone to errors due to subjectivity and inconsistencies in classification systems and software structures. This paper presents an ensemble Natural Language Processing (NLP)-based method designed to automatically align QTOs with corresponding cost indexes across different building classifications and works. Our proposed method leverages advanced NLP techniques to analyze and align textual descriptions in QTOs with corresponding items in cost indexes. The system was rigorously tested on a high-rise residential building project, where it demonstrated an 82.96% agreement rate with QS-based estimations, highlighting its semantic alignment accuracy. The model also achieved only −2.05% total cost deviation from QS estimates. The proposed Artificial Intelligence (AI)-driven system complements QSs by serving as a notification tool, highlighting discrepancies between human and system-generated estimates. This approach aids QSs in refining their cost assessments, enhancing accuracy and mitigating potential financial risks.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial intelligence; building information modeling; construction cost estimation; ensemble learning; natural language processing; quantity take-off |
| Index terms: | building information modelling, quantity surveying, financial management, cost deviation, construction cost, cost index, estimation, high-rise residential building, estimate, artificial intelligence, take-off, subjectivity, accuracy, construction project |
| Subjects: | production management, construction type, human factors and perception, economic analysis, quantity surveying, professional development, information systems, profession, financial and cost management, artificial intelligence |
| Topics: | Project Management, Digital Applications, Construction Technology, Roles and Professions, Research Practice, Information Management, Cost Management, Business Strategy |
| Descriptive scope: | 2 PC |
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