Automated detection of contractual risk clauses from construction specifications using bidirectional encoder representations from transformers (BERT).

Detecting contractual risk information from construction specifications is crucial to succeeding in construction projects. This paper describes clause classification using the Bidirectional Encoder Representations from Transformers (BERT) method in natural language processing. Seven risk categories are determined from a literature review, including payment, temporal, procedure, safety, role and responsibility, definition, and reference. Using 2807 clauses from 56 construction specifications, the BERT-based clause classification model returns noticeable performances with 0.889 accuracy for validation and a 0.934 F1 score on testing. The model is evaluated by comparing the clause classification performance with other machine learning methods, including the support vector machine and a simple deep neural network, and shows dominant performance on every risk category. Practitioners in the construction industry are the primary beneficiaries of the research as the model will contribute to improving the construction specification review process and risk management during construction projects. • The BERT-based clause classification model achieved 0.934 of F1 score. • The model detected seven risk contractual categories which are vulnerable to disputes. • The model made the contractual risk detection to be efficient, accurate, and scalable. • First attempt to apply a pre-trained NLP model to construction document review [ABSTRACT FROM AUTHOR]