Predicting the Level of Safety Performance Using an Artificial Neural Network

被引:12
作者
Boateng, Emmanuel Bannor [1 ]
Pillay, Manikam [1 ]
Davis, Peter [2 ]
机构
[1] Univ Newcastle, Sch Hlth Sci, Callaghan, NSW 2308, Australia
[2] Univ Newcastle, Sch Architecture & Built Environm, Callaghan, NSW 2308, Australia
来源
HUMAN SYSTEMS ENGINEERING AND DESIGN, IHSED2018 | 2019年 / 876卷
关键词
Artificial neural network; Construction industry; Experimental study; Safety performance; Safety management; Prediction;
D O I
10.1007/978-3-030-02053-8_107
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this study, an artificial neural network model is developed to predict the level of safety performance on construction sites. Adopting an experimental research design, the model employs safety behaviour, near misses, incidents, fatalities, and the safety risk levels as the inputs, while the safety performance level acted as the output. 339 datasets were generated based on expert intuition and professional experiences. A 5-4-1 Multi-Layer Perceptron with back-propagation was sufficient in building the model that has been trained and validated. The results are promising and show good predictive ability. The developed model could help construction and consultancy firms to assess, forecast, and monitor the level of safety performance of construction projects.
引用
收藏
页码:705 / 710
页数:6
相关论文
共 9 条
[1]   Acquaintance to Artificial Neural Networks and use of artificial intelligence as a diagnostic tool for tuberculosis: A review [J].
Dande, Payal ;
Samant, Purva .
TUBERCULOSIS, 2018, 108 :1-9
[2]   Applications of artificial neural networks for adsorption removal of dyes from aqueous solution: A review [J].
Ghaedi, Abdol Mohammad ;
Vafaei, Azam .
ADVANCES IN COLLOID AND INTERFACE SCIENCE, 2017, 245 :20-39
[3]   Application of ANN technique to predict the performance of solar collector systems - A review [J].
Ghritlahre, Harish Kumar ;
Prasad, Radha Krishna .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2018, 84 :75-88
[4]   Cognitive Factors Influencing Safety Behavior at Height: A Multimethod Exploratory Study [J].
Goh, Yang Miang ;
Sa'adon, Nur Faddilah Binte .
JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT, 2015, 141 (06)
[5]   Application of artificial neural networks, fuzzy logic and wavelet transform in fault diagnosis via vibration signal analysis: A review [J].
Jayaswal, P. ;
Wadhwani, A. K. .
AUSTRALIAN JOURNAL OF MECHANICAL ENGINEERING, 2009, 7 (02) :157-171
[6]   Integration of case-based forecasting, neural network, and discriminant analysis for bankruptcy prediction [J].
Jo, H ;
Han, I .
EXPERT SYSTEMS WITH APPLICATIONS, 1996, 11 (04) :415-422
[7]   Safety climate in construction site environments [J].
Mohamed, S .
JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT, 2002, 128 (05) :375-384
[8]   Neural Network Model for the Prediction of Safe Work Behavior in Construction Projects [J].
Patel, D. A. ;
Jha, K. N. .
JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT, 2015, 141 (01)
[9]   Overview and analysis of safety management studies in the construction industry [J].
Zhou, Zhipeng ;
Goh, Yang Miang ;
Li, Qiming .
SAFETY SCIENCE, 2015, 72 :337-350