A risk assessment framework for construction project using artificial neural network

Authors

  • Le Hong Ha Building and Industrial Faculty, National University of Civil Engineering, 55 Giai Phong road, Hai Ba Trung district, Hanoi, Vietnam
  • Le Hung Building and Industrial Faculty, National University of Civil Engineering, 55 Giai Phong road, Hai Ba Trung district, Hanoi, Vietnam
  • Le Quang Trung Building and Industrial Faculty, National University of Civil Engineering, 55 Giai Phong road, Hai Ba Trung district, Hanoi, Vietnam

DOI:

https://doi.org/10.31814/stce.nuce2018-12(5)-06

Abstract

The current trend of increasing construction project size and complexity results in higher level of project risk. As a result, risk management is a crucial determinant of the success of a project. It seems necessary for construction companies to integrate a risk management system into their organizational structure. The main aim of this paper is to propose a risk assessment framework using Artificial Neural Network (ANN) technique. Three main phases of the proposed framework are risk management phase, ANN training phase and framework application phase. Thereby, Risk Factors are identified and analysed using Failure Mode and Effect Analysis (FMEA) technique. ANN model is created and trained to evaluate the impact of Risk Factors on Project Risk which is represented through the ratio of contractor’s profit to project costs. As a result, the framework with successful model is used as a tool to support the construction company in assessing risk and evaluate their impact on the project’s profit for new projects.

Keywords: risk management; risk assessment; Artificial Neural Network (ANN); Failure Mode and Effect Analysis (FMEA); construction project.

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Published

2018-08-30

How to Cite

Ha, L. H., Hung, L., & Trung, L. Q. (2018). A risk assessment framework for construction project using artificial neural network. Journal of Science and Technology in Civil Engineering (JSTCE) - HUCE, 12(5), 51–62. https://doi.org/10.31814/stce.nuce2018-12(5)-06

Issue

Vol. 12 No. 5 (2018)

Section

Research Papers

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