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. 2020 Nov 13;17(22):8395.
doi: 10.3390/ijerph17228395.

Developing an Ensemble Predictive Safety Risk Assessment Model: Case of Malaysian Construction Projects

Haleh Sadeghi  1 , Saeed Reza Mohandes  1 , M Reza Hosseini  2 , Saeed Banihashemi  3 , Amir Mahdiyar  4 , Arham Abdullah  5
Affiliations

Developing an Ensemble Predictive Safety Risk Assessment Model: Case of Malaysian Construction Projects

Haleh Sadeghi et al. Int J Environ Res Public Health. .

Abstract

Occupational Health and Safety (OHS)-related injuries are vexing problems for construction projects in developing countries, mostly due to poor managerial-, governmental-, and technical safety-related issues. Though some studies have been conducted on OHS-associated issues in developing countries, research on this topic remains scarce. A review of the literature shows that presenting a predictive assessment framework through machine learning techniques can add much to the field. As for Malaysia, despite the ongoing growth of the construction sector, there has not been any study focused on OHS assessment of workers involved in construction activities. To fill these gaps, an Ensemble Predictive Safety Risk Assessment Model (EPSRAM) is developed in this paper as an effective tool to assess the OHS risks related to workers on construction sites. The developed EPSRAM is based on the integration of neural networks with fuzzy inference systems. To show the effectiveness of the EPSRAM developed, it is applied to several Malaysian construction case projects. This paper contributes to the field in several ways, through: (1) identifying major potential safety risks, (2) determining crucial factors that affect the safety assessment for construction workers, (3) predicting the magnitude of identified safety risks accurately, and (4) predicting the evaluation strategies applicable to the identified risks. It is demonstrated how EPSRAM can provide safety professionals and inspectors concerned with well-being of workers with valuable information, leading to improving the working environment of construction crew members.

Keywords: ANFIS; Malaysia; construction hazard; data mining; fuzzy inference system; neural network; safety risk management; site management.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The developed EPRAM.
Figure 2
Figure 2
The breakdown of interviewed experts.
Figure 3
Figure 3
Triangular fuzzy set membership function.
Figure 4
Figure 4
The ANFIS architecture with two inputs (z, y), two rules, and one output (f).
Figure 5
Figure 5
Structure of used ANFIS model.
Figure 6
Figure 6
Inputs and output in ANFIS.
Figure 7
Figure 7
Triangular membership functions with membership values between 0–1.
Figure 8
Figure 8
Performance of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model on (a) training, and (b) test data.
Figure 9
Figure 9
Rule viewer created for the training datasets.
Figure 10
Figure 10
The constructed rules by ANFIS Sugeno.
Figure 11
Figure 11
The comparison between actual versus predicted risk magnitudes of testing data sets.
Figure 12
Figure 12
Results of comparative analysis.
See this image and copyright information in PMC

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