An adaptive neuro-fuzzy inference system (anfis) model for assessing occupational risk in the shipbuilding industry

被引:50
作者
Fragiadakis, N. G. [1 ]
Tsoukalas, V. D. [2 ]
Papazoglou, V. J. [1 ]
机构
[1] Natl Tech Univ Athens, Sch Naval Architecture & Marine Engn, Shipbldg Technol Lab, Zografos 15780, Greece
[2] Athens Merchant Marine Acad, Dept Marine Engn, Aspropyrgos 19300, Greece
关键词
Risk assessment; Occupational accident; Shipbuilding industry; Fuzzy inference; Artificial neural networks; INJURIES; CLASSIFICATION; REGRESSION; ACCIDENTS; WORKERS; SAFETY;
D O I
10.1016/j.ssci.2013.11.013
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In this research an adaptive neuro-fuzzy inference system (ANFIS) has been applied to study the effect of working conditions on occupational injury using data of professional accidents assembled by ship repair yards. The data were statistically processed in order to select the most important parameters. These parameters were day and time, specialty, type of incident, dangerous situations and dangerous actions involved in the incident. The selected parameters proved, due to statistical processing, to be correlated to the observed frequency of four injury categories, namely negligible wounding, slight wounding, severe wounding and death. For each parameter a Gravity Factor (GF) was calculated based on the percentage of injury categories resulting to the incident each of the above mentioned parameter was involved. These GF values and the resulting risk value based on the accident data were used as input values to train the ANFIS model. Trapezoidal and Gauss membership functions were used for the training of the data. The model combined the modeling function of fuzzy inference with the learning ability of artificial neural networks. A set of rules has been generated directly from the statistically processed reported data. The model's predictions were compared with a number of recorded data for verifying the approach. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码:226 / 235
页数:10
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