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Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools
Authors:
- Hubert Jerzy Anysz,
- Magdalena Apollo,
- Beata Grzyl
- Record ID
- WUTb09c337ff1d3403593fb00162b3c2025
- Author
- Journal series
- Symmetry-Basel, ISSN 2073-8994
- Issue year
- 2021
- Vol
- 13
- No
- 5
- Pages
- 1-30
- Article number
- 744
- Keywords in English
- artificial neural networks; association analysis; construction project; decision-supporting tools; decision trees; disputes in construction industry; risk in decision-making
- ASJC Classification
- ; ; ;
- Abstract in original language
- 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.
- DOI
- DOI:10.3390/sym13050744 Opening in a new tab
- Language
- eng (en) English
- File
-
- File: 1
- symmetry-13-00744.pdf
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- Score (nominal)
- 70
- Score source
- journalList
- Score
- = 70.0, 10-05-2021, ArticleFromJournal
- Publication indicators
- : 2016 = 0.640; : 2018 = 2.143 (2) - 2018=2.041 (5)
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- Uniform Resource Identifier
- https://repo.pw.edu.pl/info/article/WUTb09c337ff1d3403593fb00162b3c2025/
- URN
urn:pw-repo:WUTb09c337ff1d3403593fb00162b3c2025
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or PerishOpening in a new tab system.