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Abouhamad, M and Zayed, T (2019) Risk-Based Asset Management Framework for Subway Systems. Journal of Construction Engineering and Management, 145(11).

Ahn, C R, Lee, S, Sun, C, Jebelli, H, Yang, K and Choi, B (2019) Wearable Sensing Technology Applications in Construction Safety and Health. Journal of Construction Engineering and Management, 145(11).

Alruwaythi, O and Goodrum, P (2019) A Difference in Perspective: Impact of Different Formats of Engineering Information and Spatial Cognition on Craft-Worker Eye-Gaze Patterns. Journal of Construction Engineering and Management, 145(11).

Cheng, M, Chang, Y and Korir, D (2019) Novel Approach to Estimating Schedule to Completion in Construction Projects Using Sequence and Nonsequence Learning. Journal of Construction Engineering and Management, 145(11).

Demirel, H &, Volker, L, Leendertse, W and Hertogh, M (2019) Dealing with Contract Variations in PPPs: Social Mechanisms and Contract Management in Infrastructure Projects. Journal of Construction Engineering and Management, 145(11).

Esmaeeli, A N and Heravi, G (2019) Real Options Approach versus Conventional Approaches to Valuing Highway Projects under Uncertainty. Journal of Construction Engineering and Management, 145(11).

Feng, Y and Trinh, M T (2019) Developing Resilient Safety Culture for Construction Projects. Journal of Construction Engineering and Management, 145(11).

Franco-Duran, D M and Garza, J M d l (2019) Review of Resource-Constrained Scheduling Algorithms. Journal of Construction Engineering and Management, 145(11).

Hamzeh, F R, El Samad, G and Emdanat, S (2019) Advanced Metrics for Construction Planning. Journal of Construction Engineering and Management, 145(11).

Jang, Y, Jeong, I, Cho, Y K and Ahn, Y (2019) Predicting Business Failure of Construction Contractors Using Long Short-Term Memory Recurrent Neural Network. Journal of Construction Engineering and Management, 145(11).

  • Type: Journal Article
  • Keywords: Business failure; Construction contractors; Prediction model; Long short-term memory (LSTM); Recurrent neural network (RNN);
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001709
  • 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.

Khalafallah, A and Shalaby, Y (2019) Change Orders: Automating Comparative Data Analysis and Controlling Impacts in Public Projects. Journal of Construction Engineering and Management, 145(11).

Li, K and Cheung, S O (2019) Unveiling Cognitive Biases in Construction Project Dispute Resolution through the Lenses of Third-Party Neutrals. Journal of Construction Engineering and Management, 145(11).

Prakash, A and Phadtare, M (2019) Exploration of Logic in Project Marketing Using Interpretive Structural Modeling. Journal of Construction Engineering and Management, 145(11).