ITcon Vol. 28, pg. 266-285, http://www.itcon.org/2023/13

Leveraging Natural Language Processing for Automated Information Inquiry from Building Information Models

DOI:10.36680/j.itcon.2023.013
submitted:February 2023
revised:March 2023
published:April 2023
editor(s):Robert Amor
authors:Armin Nabavi
Graduate Student, Department of Civil Engineering,
K.N. Toosi University of Technology, No. 1346, Valiasr Street, Mirdamad Intersection, Tehran, Iran
E-Mail: armin.nabavi@email.kntu.ac.ir

Issa Ramaji
Associate Professor, School of Engineering, Computing, and Construction Management,
Roger Williams University, One Old Ferry Road, Bristol, RI 02809
E-Mail: iramaj@rwu.edu

Naimeh Sadeghi
Assistant Professor, Department of Civil Engineering,
K.N. Toosi University of Technology, No. 1346, Valiasr Street, Mirdamad Intersection, Tehran, Iran
E-Mail: sadeghi@kntu.ac.ir

Anne Anderson
Associate Professor, School of Engineering, Computing, and Construction Management,
Roger Williams University, One Old Ferry Road, Bristol, RI 02809
E-Mail: akanderson@rwu.edu
summary:Building Information Modeling (BIM) is a trending technology in the building industry that can increase efficiency throughout construction. Various practical information can be obtained from BIM models during the project life cycle. However, accessing this information could be tedious and time-consuming for non-technical users, who might have limited or no knowledge of working with BIM software. Automating the information inquiry process can potentially address this need. This research proposes an Artificial Intelligence-based framework to facilitate accessing information in BIM models. First, the framework uses a support vector machine (SVM) algorithm to determine the user's question type. Simultaneously, it employs natural language processing (NLP) for syntactic analysis to find the main keywords of the user's question. Then it utilizes an ontology database such as IfcOWL and an NLP method (latent semantic analysis (LSA)) for a semantic understanding of the question. The keywords are expanded through the semantic relationship in the ontologies, and eventually, a final query is formed based on keywords and their expanded concepts. A Navisworks API is developed that employs the identified question type and its parameters to extract the results from BIM and display them to the users. The proposed platform also includes a speech recognition module for a more user-friendly interface. The results show that the speed of answering the questions on the platform is up to 5 times faster than the manual use by experts while maintaining high accuracy.
keywords:Building Information Modeling (BIM), Natural Language Processing (NLP), Ontology, Support Vector Machine (SVM), Question Answering platform
full text: (PDF file, 0.773 MB)
citation:Nabavi A, Ramaji I, Sadeghi N, Anderson A (2023). Leveraging Natural Language Processing for Automated Information Inquiry from Building Information Models, ITcon Vol. 28, pg. 266-285, https://doi.org/10.36680/j.itcon.2023.013
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