Effective Risk Positioning through Automated Identification of Missing Contract Conditions from the Contractor's Perspective Based on FIDIC Contract Cases

Abstract

Defining, measuring, and dealing with contractual risks are crucial for successful construction projects because the contractual risks can lead to serious claims and disputes. In general, construction participants make a stipulation regarding their roles and responsibilities by contracting in order to prevent such claims and disputes. A common practice for preparing construction contracts is to modify the standard contract forms to reflect the interests of the given project from the owner's perspective. In this process, however, favorable clauses that may be beneficial to the contractor are often modified or even removed, causing significant potential risks to the contractor. Therefore, an in-depth review of contract terms and conditions is required to avoid future risks. This study presents a new proactive risk assessment model to identify missing contractor-friendly clauses in the owner's modified contract conditions from the contractor's point of view. A case study is used to demonstrate the proposed framework, and real-world project cases were analyzed to understand what type of contractor-friendly clauses would likely be omitted in the owner's modified contract. In this study, the developed model builds on rule-based natural-language processing (NLP) to analyze unstructured text data through preprocessing, syntactic analysis, and semantic analysis. The proposed data-driven risk assessment model is expected to reduce the extent of human errors by (1) identifying potential contractual risks that could arise disputes; and (2) supporting to develop an appropriate response strategy for the given risks.