Predicting the Outcome of Construction Change Disputes Using Machine-Learning Algorithms-Reference-Cited by-同舟云学术

Predicting the Outcome of Construction Change Disputes Using Machine-Learning Algorithms

Published:2024-02 Issue:1 Volume:16 Page:
ISSN:1943-4162
Container-title:Journal of Legal Affairs and Dispute Resolution in Engineering and Construction
language:en
Short-container-title:J. Leg. Aff. Dispute Resolut. Eng. Constr.

Author:

Alqaisi Aaraf Shukur¹^ORCID,Ataei Hossein²,Seyrfar Abolfazl³^ORCID,Al Omari Mohammad⁴^ORCID

Affiliation:

1. Ph.D. Student, Dept. of Civil, Materials, and Environmental Engineering, Univ. of Illinois at Chicago, Chicago, IL 60607 (corresponding author). ORCID: .

2. Clinical Associate Professor, Dept. of Civil, Materials, and Environmental Engineering, Univ. of Illinois at Chicago, Chicago, IL 60607.

3. Data Scientist, Dept. of Civil, Materials, and Environmental Engineering, Univ. of Illinois at Chicago, Chicago, IL 60607. ORCID: .

4. Ph.D. Student, Dept. of Civil, Materials, and Environmental Engineering, Univ. of Illinois at Chicago, Chicago, IL 60607. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Law,Engineering (miscellaneous),Safety, Risk, Reliability and Quality,Civil and Structural Engineering,Building and Construction

Link

https://ascelibrary.org/doi/pdf/10.1061/JLADAH.LADR-1051

Reference36 articles.

1. Alpaydin, E. 2014. Introduction to machine learning. London: MIT Press.

2. Alqaisi A. S. 2022. “Outcome prediction of construction change disputes using machine learning.” Master’s thesis Dept. of Civil Materials and Environmental Engineering Univ. of Illinois at Chicago.

3. Arcadis. 2022. “2022 global construction disputes report.” Accessed September 28 2022. https://www.arcadis.com/en-gb/knowledge-hub/perspectives/global/global-construction-disputes-report.

4. Predicting the Outcome of Construction Litigation Using Neural Networks

5. Predicting the Outcome of Construction Litigation Using Boosted Decision Trees