A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Multi-objective decision-making supporting system of maintenance strategies for deteriorating reinforced concrete buildings
Abstract This work presents an integrated system that can help engineers search for the optimal maintenance strategy for deteriorating RC buildings via multi-objective optimization. This system applies PSO and the Pareto optimal solution to achieve the optimization of the multiple objectives of minimal LCCs (economy), minimal failure probability of the building (safety), minimal spalling probability of concrete cover (serviceability), maximum rationality, and minimal maintenance times. Additionally, to enhance computing efficiency in optimization, probabilistic effect assessment models for setting repair and retrofitting strategies are proposed. The effects of maintenance strategies on the cumulative density function of the structural seismic damage and spalling probability of concrete cover are assessed directly instead of reanalyzing deterioration using the finite difference method, while considering the modified structural state created by implementation of the maintenance strategies. The proposed system has four main modules: (1) Deterioration analysis; (2) Seismic performance assessment; (3) Setting maintenance strategies; and (4) Multi-objective optimization. These four modules are integrated into the multi-objective decision-making support system for maintenance strategies for deteriorating reinforced concrete buildings (MDMS-RCB). Finally, a case study is conducted to demonstrate application of the proposed system.
Highlights An integrated system for optimal maintenance strategies for RC buildings is built. PSO and Pareto solution are applied in the multi-objective optimization. Multi-objective includes economy, safety, serviceability, and rationality. Probabilistic effect assessment models for maintenance strategies are proposed.
Multi-objective decision-making supporting system of maintenance strategies for deteriorating reinforced concrete buildings
Abstract This work presents an integrated system that can help engineers search for the optimal maintenance strategy for deteriorating RC buildings via multi-objective optimization. This system applies PSO and the Pareto optimal solution to achieve the optimization of the multiple objectives of minimal LCCs (economy), minimal failure probability of the building (safety), minimal spalling probability of concrete cover (serviceability), maximum rationality, and minimal maintenance times. Additionally, to enhance computing efficiency in optimization, probabilistic effect assessment models for setting repair and retrofitting strategies are proposed. The effects of maintenance strategies on the cumulative density function of the structural seismic damage and spalling probability of concrete cover are assessed directly instead of reanalyzing deterioration using the finite difference method, while considering the modified structural state created by implementation of the maintenance strategies. The proposed system has four main modules: (1) Deterioration analysis; (2) Seismic performance assessment; (3) Setting maintenance strategies; and (4) Multi-objective optimization. These four modules are integrated into the multi-objective decision-making support system for maintenance strategies for deteriorating reinforced concrete buildings (MDMS-RCB). Finally, a case study is conducted to demonstrate application of the proposed system.
Highlights An integrated system for optimal maintenance strategies for RC buildings is built. PSO and Pareto solution are applied in the multi-objective optimization. Multi-objective includes economy, safety, serviceability, and rationality. Probabilistic effect assessment models for maintenance strategies are proposed.
Multi-objective decision-making supporting system of maintenance strategies for deteriorating reinforced concrete buildings
Chiu, Chen-Kuo (author) / Lin, Yi-Fong (author)
Automation in Construction ; 39 ; 15-31
2013-11-23
17 pages
Article (Journal)
Electronic Resource
English
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