Machine Learning–Based Predictive Model for Dispute Occurrence and Resolution Strategies in Pipeline Projects

Abstract

Pipeline projects present complex legal challenges due to their extensive scope and multifaceted impacts. These challenges often lead to land use conflicts, environmental compliance issues, regulatory hurdles, and internal disputes, resulting in costly delays and legal complications. To address these issues, this study analyzes data from sixty ongoing or completed pipeline projects and evaluates the performance of five machine learning models in predicting both the occurrence of disputes and appropriate resolution mechanisms. Among these, the random forest algorithm demonstrated superior performance, achieving high accuracy and F1 scores, and providing insights through feature importance analysis. Key findings from the feature importance analysis emphasize the critical role of environmental and stakeholder-related variables, such as community engagement and stakeholder count, in shaping disputes and their resolutions, while technical factors like budgeted cost and project complexity were less significant. By developing a data-driven framework for dispute prediction and resolution strategy analysis, this study explores the role of machine learning in supporting risk assessment and resolution strategies in pipeline projects. The findings provide practical value for policymakers, project managers, and regulatory bodies, aiding proactive decision-making and dispute mitigation efforts in infrastructure development. The findings of this study demonstrate how machine learning can support dispute management in pipeline projects. The predictive models help identify and assess key dispute factors early in project planning, with environmental concerns, regulatory compliance, and stakeholder engagement emerging as critical areas that influence both dispute occurrence and resolution. The analysis reveals how predictive modeling can enhance existing project management practices by providing data-driven perspectives on dispute dynamics. This approach proves particularly valuable in regions with stringent oversight, where understanding these complex interactions can strengthen compliance efforts and dispute resolution strategies. Integrating predictive analytics into project decision-making provides quantitative insights into potential dispute factors and their interconnections, providing a complementary data-driven perspective to existing management practices. By evaluating the effectiveness of these models in the context of pipeline projects, this research contributes to ongoing discussions on the role of predictive methods in infrastructure planning while reinforcing the importance of regulatory and social responsibility in project execution.