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.

从施工规范中检测合同风险信息对于建筑项目的成功至关重要。本文描述了在自然语言处理中使用来自 Transformers (BERT) 方法的双向编码器表示的子句分类。从文献回顾中确定了七个风险类别，包括付款、时间、程序、安全、角色和责任、定义和参考。使用来自 56 个构造规范的 2807 个子句，基于 BERT 的子句分类模型返回显着的性能，验证准确率为 0.889，测试 F1 分数为 0.934。通过将子句分类性能与其他机器学习方法（包括支持向量机和简单的深度神经网络）进行比较来评估该模型，并在每个风险类别上都表现出领先的表现。建筑行业的从业人员是该研究的主要受益者，因为该模型将有助于改善建筑项目期间的建筑规范审查过程和风险管理。