Search
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
Citations
-
8
CrossRef
-
0
Web of Science
-
7
Scopus
Cite as
Full text
- Publication version
- Accepted or Published Version
- License
-
open in new tab
Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
Symmetry-Basel
no. 13,
ISSN: 2073-8994 - Language:
- English
- Publication year:
- 2021
- Bibliographic description:
- Anysz H., Apollo M., Grzyl B.: Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools// Symmetry-Basel -Vol. 13,iss. 5 (2021),
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/sym13050744
- Verified by:
- Gdańsk University of Technology
seen 97 times