Developing a Conceptual Model to Predict Disputes in Construction Projects Using Artificial Intelligence Technologies
Year: 2025
Type: conference-paper
Abstract: The construction industry is a complex and competitive environment and disputes become more prevalent. The construction disputes negatively effect the cost, time and quality of the project. Further, disputes could result in the abandonment of projects and even the financial collapse of construction companies. Many researchers have attempted to mitigate disputes by addressing the causes of disputes, dispute avoidance techniques, and resolution methods from different perspectives. However, recent literature has evidenced that disputes remain a persistent threat in construction projects. The construction industry is attempting to transform into a technology-driven industry through the adoption of innovative concepts and technologies. In the digital era, intelligent systems have received widespread attention in many industries. This trend offers a new approach to integrating digital technology with established dispute management methods, aiming to automate processes and effectively mitigate disputes. Artificial intelligence offers a robust and sophisticated approach to prediction and pattern recognition in the construction industry by utilising advanced algorithms and data analysis techniques. Ensemble models using predictive analysis and natural language processing could enable forecasting disputes in construction projects. Thus, this study develops a conceptual model to predict disputes in construction projects using artificial intelligence techniques. This model combines traditional machine learning technique and natural language processing technique from realtime text-based communications. This research outcome will pave the way for the development of a dispute prediction digital system, offering a potential next-generation dispute mitigation strategy for the construction industry through the implementation of the intelligent system.
Source: CIB Conferences
Institution: Western Sydney University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4411472615