AI models for predicting construction disputes in Sri Lanka

Abstract

Construction disputes pose persistent challenges in Sri Lanka's construction industry, leading to project delays, cost overruns, and strained professional relations. This research seeks to alleviate these issues by introducing an AI-powered predictive model designed to identify and analyse dispute risks at the project's outset. By offering proactive insights, the AI model aims to enhance decision-making and facilitate the implementation of dispute prevention strategies, thereby improving overall project outcomes. Employing a mixed-methods approach, the study comprehensively examined project features contributing to disputes within the Sri Lankan context. Quantitative data on project characteristics and their correlation with dispute occurrence were gathered through structured questionnaires, while qualitative insights into dispute causes and stakeholder challenges were obtained via in-depth interviews with industry experts. Through meticulous analysis of this combined data, key predictors of construction disputes were identified, including contract ambiguities, unrealistic timelines, payment delays, poor communication, and unforeseen site conditions. These findings drove the development of a machine learning-based predictive model trained to recognise patterns, predict dispute likelihoods, and suggest their nature based on identified risk factors. This innovative AI tool has the potential to revolutionise dispute management practices in Sri Lanka's construction industry. By providing stakeholders with early warnings of potential disputes, the model enables proactive mitigation strategies, such as enhanced contract drafting, optimised communication, and timely alternative dispute resolution. The long-term impact of this research extends to fostering a more collaborative and sustainable construction industry, ultimately contributing to the successful delivery of projects across Sri Lanka.