4.7 Article

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

期刊

AUTOMATION IN CONSTRUCTION
卷 142, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.autcon.2022.104465

关键词

Contractual risk; Automated specification review; Natural language processing; BERT

资金

  1. National R&D Project for Smart Construction Technology - Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport [22SMIP-A158708-03]
  2. Korea Authority of Land & Infrastructure Safety [C-202202-013]
  3. BK21 PLUS research program of the National Research Foundation of Korea

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This paper presents a clause classification model based on the BERT method for construction specifications, which demonstrates excellent performance in various risk categories. It contributes to improving the review process and risk management in the construction industry.
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.

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