Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools

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.

Deterministic and Probabilistic Risk Management Approaches in Construction Projects: A Systematic Literature Review and Comparative Analysis

“…Limited amount of data Able to deal with missing values Numerical, categorical, and linguistic data [103,104] High amount of data Partial ability to deal with missing values [24] Probability and dependencies' role Embrace probability in assessments Considering variables interdependencies with each other and final output [105,106] Does not embrace probability in assessments Considering variables interdependencies on final output [87,107] Prediction precision Mid-high [108] Very high [25] Application scope Subjective and uncertain problems with limited data [109] Objective and complex problems with abundant data [83] Application in RM processes…”

7-Score Function for Assessing the Strength of Association Rules Applied for Construction Risk Quantifying

“…precipitation prediction [33]; -insurance risk assessment [34]; -traffic safety analysis [35][36][37]; -assessment of construction project risk [27]; -assessment of risk in construction disputes [38]; -a variety of problems in biology [39][40][41]; -preferences' discovering in social sciences [42]; -collusion detection in tender procedures [43]; -quality management problem-solving in production [44].…”

The Influence of Threat Development on the Failure of the System’s Symmetry

“…Security assessment is the work of many authors, who mainly focus on individual methods and procedures by which they assess the level of risks or threats to selected objects. [7][8][9]. In the field of security, the method of fuzzy logic (fuzzy model) is well known for its probabilistic character, which can be applied to estimate the future development of risks [10][11][12][13][14][15][16].…”