Predicting Business Failure of Construction Contractors Using Long Short-Term Memory Recurrent Neural Network
Publication: Journal of Construction Engineering and Management
Volume 145, Issue 11
Abstract
Predicting business failure of construction contractors is critical for both contractors and other stakeholders such as project owners, surety underwriters, investors, and government entities. To identify a new model with better prediction of business failure of the construction contractors, this study utilized long short-term memory (LSTM) recurrent neural network (RNN). The financial ratios of the construction contractors in the United States were collected, and synthetic minority oversampling technique (SMOTE) and Tomek links were employed to obtain a balanced data set. The proposed LSTM RNN model was evaluated by comparing its accuracy and F1-score with feedforward neural network (FNN) and support vector machine (SVM) models for the optimized parameters selected from a grid search with five-fold cross-validation. The results successfully demonstrate that the prediction performance of the proposed LSTM RNN model outperforms FNN and SVM models for both test and original data set. Therefore, the proposed LSTM RNN model is a promising alternative to assist managers, investors, auditors, and government entities in predicting business failure of construction contractors, and can also be adapted to other industry cases.
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Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author by request. Information about the Journal’s data-sharing policy can be found here: http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
Acknowledgments
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT, and Future Planning (No. 2015R1A5A1037548).
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Information & Authors
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Published In
Journal of Construction Engineering and Management
Volume 145 • Issue 11 • November 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Aug 18, 2018
Accepted: Mar 19, 2019
Published online: Aug 27, 2019
Published in print: Nov 1, 2019
Discussion open until: Jan 27, 2020
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