Journal
NATURAL HAZARDS
Volume 65, Issue 1, Pages 799-817Publisher
SPRINGER
DOI: 10.1007/s11069-012-0394-z
Keywords
Optimal reinforcement strategy; System failure probability; Life quality index; Life loss upon dam failure; Dangerous dams
Funding
- National Natural Science Foundation of China [SN: 51179066, 51139001]
- Jiangsu Natural Science Foundation [SN: BK2012036]
- Program for New Century Excellent Talents in University [SN: NCET-10-0359]
- Special Fund of State Key Laboratory of China [SN: 2009586912]
- Fundamental Research Funds for the Central Universities [2012B06614]
- Jiangsu Province 333 High-Level Personnel Training Project [BRA2011179]
- Priority Academic Program Development of Jiangsu Higher Education Institutions [SN: YS11001]
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Lifetime-oriented multi-objective optimization for structural reinforcement based on series-system reliability and benefit-cost analysis has already been proposed. It is still needed, however, to incorporate the life quality index (LQI) in the lifetime reinforcement optimization process of complex massive infrastructure engineering with correlated series-parallel failure modes (e.g., dams). An improved technique combining overall system failure probability with benefit-cost analysis based on the LQI is developed. An approach to obtain time-average system failure probability with correlated series-parallel failure modes is proposed to measure the structural performance. Then, the concept for benefit-cost ratio based on LQI including failure consequence and life quality objective is introduced. As an application of the methodology, the optimal reinforcement strategy for an existing earth dam is shown. Three types of reinforcement strategies, that is, preventive reinforcement, essential reinforcement and that lies between them, are selected. The results show that the preventive reinforcement strategy is the most beneficial for a dam, whose failure loss involving human life is tremendous. The advantage of the proposed approach is its ability to harmonize overall structural safety with reinforcement cost and can be extended to optimization of reinforcement strategies for other massive infrastructure engineering projects.
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