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The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

The number of construction dispute cases has surged in recent years. The effective exploration and management of risks associated with construction contracts helps to directly enhance the overall project performance. The existing approaches to identify the risks associated with construction project contracts have a heavy reliance on manual review techniques, which are inefficient and highly restricted by personnel experience. The existing intelligent approaches only work for the contract query and storage. Hence, it is necessary to improve the intelligence level for contract risk management. This study aims to propose a novel method for the intelligent identification of risks in construction contract clauses based on natural language processing.

This proposed method can formalize the linguistic logic and semantic information of contract clauses into multiple triples and transform the structural processing results of general clauses in a construction contract into rights and interests rules for risk review. In addition, the core semantic information of special clauses in a construction contract, rights and interests rules are used for semantic conflict detection. Finally, this study achieves the intelligent risk identification of construction contract clauses.

The method is verified by selecting several construction contracts that had been applied in engineering contracting as a corpus. The results showed a high level of accuracy and applicability of the proposed method.

This novel method can identify the risks in contract clauses with complex syntactic structures and realize rule extension according to the semantic relation network of the ontology. It can support efficient contract review and assist the decision-making process in contract risk management.