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dc.contributor.author Kiridana, YMWHMRRLJB
dc.contributor.author Abeynayake, MDTE
dc.contributor.author Eranga, BAI
dc.contributor.editor Sandanayake, YG
dc.contributor.editor Waidyasekara, KGAS
dc.contributor.editor Ranadewa, KATO
dc.contributor.editor Chandanie, H
dc.date.accessioned 2024-09-03T05:17:10Z
dc.date.available 2024-09-03T05:17:10Z
dc.date.issued 2024
dc.identifier.citation Kiridana, Y.M.W.H.M.R.R.L.J.B., Abeynayake, M.D.T.E., & Eranga, B.A.I. (2024). AI models for predicting construction disputes in Sri Lanka. In Y.G. Sandanayake, K.G.A.S. Waidyasekara, K.A.T.O. Ranadewa, & H. Chandanie (Eds.), World Construction Symposium – 2024 : 12th World Construction Symposium (pp. 132-145). Department of Building Economics, University of Moratuwa. https://doi.org/10.31705/WCS.2024.11
dc.identifier.uri http://dl.lib.uom.lk/handle/123/22792
dc.description.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. en_US
dc.language.iso en en_US
dc.publisher Department of Building Economics en_US
dc.subject Artificial Intelligence en_US
dc.subject Causes of Construction Dispute en_US
dc.subject Construction Dispute en_US
dc.subject Construction Industry en_US
dc.subject Machine Learning en_US
dc.title AI models for predicting construction disputes in Sri Lanka en_US
dc.type Conference-Full-text en_US
dc.identifier.faculty Architecture en_US
dc.identifier.department Department of Building Economics en_US
dc.identifier.year 2024 en_US
dc.identifier.conference World Construction Symposium - 2024 en_US
dc.identifier.place Colombo en_US
dc.identifier.pgnos pp. 132-145 en_US
dc.identifier.proceeding 12th World Construction Symposium - 2024 en_US
dc.identifier.email kiridanaymwhmrrljb.19@uom.lk en_US
dc.identifier.email mabeynayake@uom.lk en_US
dc.identifier.email isurue@uom.lk en_US
dc.identifier.doi https://doi.org/10.31705/WCS.2024.11 en_US


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  • WCS - 2024 [87]
    Proceedings of The 12th World Construction Symposium 2024

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