Predicting Business Failure of Construction Contractors Using Long Short-Term Memory Recurrent Neural Network-Reference-Cited by-同舟云学术

Predicting Business Failure of Construction Contractors Using Long Short-Term Memory Recurrent Neural Network

Published:2019-11 Issue:11 Volume:145 Page:04019067
ISSN:0733-9364
Container-title:Journal of Construction Engineering and Management
language:en
Short-container-title:J. Constr. Eng. Manage.

Author:

Jang Youjin¹^ORCID,Jeong In-Bae¹^ORCID,Cho Yong K.²^ORCID,Ahn Yonghan³

Affiliation:

1. Postdoctoral Research Associate, School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355. ORCID: .

2. Associate Professor, School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355 (corresponding author). ORCID: .

3. Associate Professor, School of Architecture and Architectural Engineering, Hanyang Univ., Ansan-si 15588, Republic of Korea.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Strategy and Management,Industrial relations,Building and Construction,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CO.1943-7862.0001709

Reference33 articles.

1. A methodology for predicting company failure in the construction industry

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3. Predicting the risk of contractor default in Saudi Arabia utilizing artificial neural network (ANN) and genetic algorithm (GA) techniques

4. Business failures in the construction industry

5. Entropy for business failure prediction: An improved prediction model for the construction industry;Bal J.;Adv. Decis. Sci.,2013

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