000			02089nam a22002537a 4500
003			ZW-GwMSU
005			20240604140406.0
008			240604b |||||||| |||| 00| 0 eng d
022			_a01446193
040			_aMSU _bEnglish _cMSU _erda
050	0	0	_aHD9715.A1 CON
100	1		_aGoh, Yang Miang _eauthor
245	1	0	_aNeural network analysis of construction safety management systems: _ba case study in Singapore _ccreated by Yang Miang Goh and David Chua
264	1		_aAbingdon: _bTaylor and Francis, _c2013
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_aConstruction Management and Economics _vVolume 31, number 4-6
520	3		_aA neural network analysis was conducted on a quantitative occupational safety and health management system (OSHMS) audit with accident data obtained from the Singapore construction industry. The analysis is meant to investigate, through a case study, how neural network methodology can be used to understand the relationship between OSHMS elements and safety performance, and identify the critical OSHMS elements that have significant influence on the occurrence and severity of accidents in Singapore. Based on the analysis, the model may be used to predict the severity of accidents with adequate accuracy. More importantly, it was identified that the three most significant OSHMS elements in the case study are: incident investigation and analysis, emergency preparedness, and group meetings. The findings imply that learning from incidents, having well-prepared consequence mitigation strategies and open communication can reduce the severity and likelihood of accidents on construction worksites in Singapore. It was also demonstrated that a neural network approach is feasible for analysing empirical OSHMS data to derive meaningful insights on how to improve safety performance
650			_aAccident _vManagement system _xNeural network _zSingapore
700	1		_aChua, David _eco-author
856			_uhttps://doi.org/10.1080/01446193.2013.797095
942			_2lcc _cJA
999			_c165894 _d165894