Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools

Hubert Anysz,Beata Grzyl,Magdalena Apollo

doi:10.3390/sym13050744

Hubert Anysz, Beata Grzyl + Show 1 more

Open Access

https://doi.org/10.3390/sym13050744

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Abstract

A high monetary value of the construction projects is one of the reasons of frequent disputes between a general contractor (GC) and a client. A construction site is a unique, one-time, and single-product factory with many parties involved and dependent on each other. The organizational dependencies and their complexity make any fault or mistake propagate and influence the final result (delays, cost overruns). The constant will of the parties involved results in completing a construction object. The cost increase, over the expected level, may cause settlements between parties difficult and lead to disputes that often finish in a court. Such decision of taking a client to a court may influence the future relations with a client, the trademark of the GC, as well as, its finance. To ascertain the correctness of the decision of this kind, the machine learning tools as decision trees (DT) and artificial neural networks (ANN) are applied to predict the result of a dispute. The dataset of about 10 projects completed by an undisclosed contractor is analyzed. Based on that, a much bigger database is simulated for automated classifications onto the following two classes: a dispute won or lost. The accuracy of over 93% is achieved, and the reasoning based on results from DT and ANN is presented and analyzed. The novelty of the article is the usage of in-company data as the independent variables what makes the model tailored for a specific GC. Secondly, the calculation of the risk of wrong decisions based on machine learning tools predictions is introduced and discussed.

Highlights

An important condition accompanying smooth and consensual implementation of a construction project is a proportional and transparent division of risks between the parties of the contract, inter alia, the consequences of any disruptions arisen from an increase in the scope of work and extension of the completion time [1]
The factors causing serious disturbances in the contractor’s operations include the necessity to introduce changes and revisions in the scope of works, lack of access to the construction site at the planned date, the necessity to suspend works and re-mobilize, logistic problems related to supplies, organization and coordination of works conducted by several subcontractors, adverse weather conditions [4]
To interpret the results obtained by the tools applied, it is necessary to analyze the confusion matrix and the corresponding indicators to assess the diagnostic value of the classification

Summary

Introduction

An important condition accompanying smooth and consensual implementation of a construction project is a proportional and transparent division of risks between the parties of the contract, inter alia, the consequences of any disruptions arisen from an increase in the scope of work and extension of the completion time [1]. In this context, properly structured legal and contractual solutions are crucial, as they significantly reduce the risk of conflicts between cooperating parties and, in many cases, offer the chance to solve the aforementioned problems without any court involvement [2,3]. One can speculate that the negative effects of a continuing pandemic, in the long run, will lead to numerous conflicts between construction investment parties and increased litigation

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