TY - JOUR
T1 - Data-driven safety enhancing strategies for risk networks in construction engineering
AU - Chen, Fangyu
AU - Wang, Hongwei
AU - Xu, Gangyan
AU - Ji, Hongchang
AU - Ding, Shanlei
AU - Wei, Yongchang
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China [grant numbers 71701213 , 71401181 , 71821001 ]; and the MOE (Ministry of Education in China) Project of Humanities and Social Sciences [grant numbers 15YJC630008 , 14YJC630136 ].
Funding Information:
This work was supported by the National Natural Science Foundation of China [grant numbers 71701213, 71401181, 71821001]; and the MOE (Ministry of Education in China) Project of Humanities and Social Sciences [grant numbers 15YJC630008, 14YJC630136].
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/5
Y1 - 2020/5
N2 - Risk management is crucial and indispensable to the success of projects, while identifying critical risks is the fundamental step in devising the corresponding safety measures. To fully exploit the value of richly accumulated accidental cases, this paper presents a data-driven research framework for proposing effective safety enhancing strategies based on risk networks in construction engineering, spanning the whole process from extracting accident chains from accidents to construct a risk network to devising safety measures. Aiming at the weighted heterogeneity of the risk network, both the performance metrics at network level and critical-risk identification metrics at node level are deliberately designed. These metrics then enable the proposing of a series of safety-enhancing strategies. In the case study, based on the accident-related data in China's bridge-and-tunnel hybrid projects, different safety-enhancing strategies are compared through simulation experiments and analyzed to verify their effectiveness on optimizing costs and improving safety. Finally, based on results from simulations, relevant managerial suggestions are proposed.
AB - Risk management is crucial and indispensable to the success of projects, while identifying critical risks is the fundamental step in devising the corresponding safety measures. To fully exploit the value of richly accumulated accidental cases, this paper presents a data-driven research framework for proposing effective safety enhancing strategies based on risk networks in construction engineering, spanning the whole process from extracting accident chains from accidents to construct a risk network to devising safety measures. Aiming at the weighted heterogeneity of the risk network, both the performance metrics at network level and critical-risk identification metrics at node level are deliberately designed. These metrics then enable the proposing of a series of safety-enhancing strategies. In the case study, based on the accident-related data in China's bridge-and-tunnel hybrid projects, different safety-enhancing strategies are compared through simulation experiments and analyzed to verify their effectiveness on optimizing costs and improving safety. Finally, based on results from simulations, relevant managerial suggestions are proposed.
KW - Construction engineering
KW - Data-driven
KW - Risk network
KW - Safety enhancing strategies
UR - http://www.scopus.com/inward/record.url?scp=85078133750&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2020.106806
DO - 10.1016/j.ress.2020.106806
M3 - Journal article
AN - SCOPUS:85078133750
SN - 0951-8320
VL - 197
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
M1 - 106806
ER -