AI-augmented construction cost estimation: an ensemble Natural Language Processing (NLP) model to align quantity take-offs with cost indexes

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 ali..