Development of Prediction Models for Claim Cause Analyses in Highway Projects

• Published in: Journal of legal affairs and dispute resolution in engineering and construction Vol. 11; no. 4
• Main Authors: Parikh, Dhaval, Joshi, G. J, Patel, D. A
• Format: Journal Article
• Language: English
• Published: Reston American Society of Civil Engineers 01-11-2019
• Subjects: Analytic hierarchy process; Artificial neural networks; Construction contracts; Construction costs; Contractors; Contracts; Highway construction; Principal components analysis; Regression analysis; Roads; Roads & highways; Scholarly Paper; Scholarly Papers; India
• ISSN: 1943-4162; 1943-4170
• DOI: 10.1061/(ASCE)LA.1943-4170.0000303

AbstractIndia has the second largest road network in the world at 4.5 million km, and still, the government plans to develop a further 66,117 km of roads in the near future. Recently, out of 1,263 road infrastructure projects, 297 and 350 reported a time or cost overrun, respectively, and 103 projects faced both time and cost overruns. Thus, the majority of highway construction contracts lead to claims and disputes among stakeholders. It is necessary to understand, assess, and take appropriate actions for increasing the predictability of these claims and disputes in order to control them. This study attempts to analyze the patterns of claim and dispute occurrences between contractors and clients of highway construction projects in India. Data on disputes and claims for 77 contracts containing 573 claims and dispute cases under highway construction projects implemented over a period of 10 years in India were collected. These claim causes are grouped by using principle component analysis. A dispute occurrence index is developed through the analytical hierarchy process (AHP) to examine the relative potential of occurrence of dispute and claim events. Further, prediction models for time overrun and cost overrun for highway projects using multiple linear regression and an artificial neural network are developed to understand and predict the cause-effect impact. This study can enhance knowledge in claims management.