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author:

Liu, Chun-Lin (Liu, Chun-Lin.) [1] | Fan, Jun-Yu (Fan, Jun-Yu.) [2] | Chen, Zhao-Hui (Chen, Zhao-Hui.) [3]

Indexed by:

EI PKU CSCD

Abstract:

To provide economic decision support for structural protection against blasting terror attacks, and to achieve the optimal balance between protection performance and cost, an economic decision model for blast-resistant protection measures of structures was established in terms of the relationship between cost of land and blast protection measures and the standoff distance. Based on practical engineering data of protection costs for anti-blasting, key parameters of the prediction model were determined by the trust-region optimization algorithm. The effectiveness of the established model was verified with novel project data. The results show that the prediction model can be used in the economic analysis for the blast resistant protection measures in a simple and effective manner, which provides evidences for blast security risk assessment and management decision. © 2018, Engineering Mechanics Press. All right reserved.

Keyword:

Blasting Blast resistance Cost engineering Decision support systems Economic analysis Risk assessment

Community:

  • [ 1 ] [Liu, Chun-Lin]School of Civil Engineering, Tsinghua University, Beijing; 100084, China
  • [ 2 ] [Liu, Chun-Lin]K&C Protective Technologies Pte Ltd, Singapore; 311125, Singapore
  • [ 3 ] [Fan, Jun-Yu]College of Defense Engineering, Army Engineering University, Nanjing; 210007, China
  • [ 4 ] [Chen, Zhao-Hui]Collegeof Civil Engineering, Fuzhou University, Fuzhou; 350116, China

Reprint 's Address:

  • [liu, chun-lin]k&c protective technologies pte ltd, singapore; 311125, singapore;;[liu, chun-lin]school of civil engineering, tsinghua university, beijing; 100084, china

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Source :

Engineering Mechanics

ISSN: 1000-4750

Year: 2018

Issue: 11

Volume: 35

Page: 99-105

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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