Rai, H; Jagannathan, M and Venkata Santosh Kumar, D (2021) Claim tenability assessment in Indian real estate projects using ann and decision tree models. Built Environment Project and Asset Management, 11(3), pp. 468-487. ISSN 2044-124X
Abstract
Claims have become an inseparable part of construction projects across the world. Construction claims often tend to result not only in time and cost overruns but in case of a dispute arising from the claim, it may result in erosion of the brand value and the working relationship between the parties. Thus, construction claim prediction is important but is complicated because of a large number of dependent factors and the complex inter-relations between them. With the aid of machine learning techniques, claim tenability assessment for real estate projects in India is attempted in this paper. In this research, artificial neural network (ANN) and decision tree models are used for assessment of claims in the Indian real estate sector using project and claims data from 275 real estate projects. The developed ANN model assesses the claim tenability in a project with a high degree of accuracy. Both ANN and decision tree models identify that "inconsistency between drawings and specification" as the most influencing factor in claim tenability assessment. Notwithstanding the claim tenability assessment, the model, in its current form, cannot be used to predict the "extent of claim" in the real estate projects. Claim tenability assessment in real estate projects, especially in India, is scantily discussed in literature. This research, by adding to the body of knowledge, helps in both claim assessment and identification of factors that need to be controlled to reduce the claim tenability in real estate construction projects in India.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | claim prediction; artificial intelligence; artificial neural networks; construction costs; neural networks; process controls; cost estimates; learning theory; drawings; algorithms; decision trees; machine learning; India |
| Index terms: | real estate, construction project, learning theory, process control, decision tree, claim prediction, cost estimate, machine learning, construction claim, body of knowledge, India, artificial intelligence, influencing factor, specification, drawing, construction cost, dispute, neural network, cost overrun, accuracy, artificial neural network |
| Subjects: | professional development, financial risk, production management, Geography, contractual condition, decision analysis, payment, learning theory, risk assessment, dispute resolution, control systems, technical documentation, real estate economics, modelling and simulation, financial and cost management, artificial intelligence, knowledge management |
| Topics: | Cost Management, Information Management, Engineering Principles, Research Practice, Geographical Context, Project Management, Risk Management, Urban Studies, Digital Applications, Legal Issues, Design Practice, Contract Administration |
| Descriptive scope: | 2 PC |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here