Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Efficient multi-objective optimisation of probabilistic service life management
The inspection and maintenance plans to ensure the structural safety and extend the service life of deteriorating structures can be established effectively through an optimisation process. When several objectives are required for inspection and maintenance strategies, a multi-objective optimisation process needs to be used in order to consider all objectives simultaneously and to rationally select a well-balanced solution. However, as the number of objectives increases, additional computational efforts are required to obtain the Pareto solutions, for decision-making to select well-balanced solutions, and for visualisation of the solutions. This paper presents a novel approach to multi-objective optimisation process of probabilistic service life management with four objectives: minimising the damage detection delay, minimising the probability of failure, maximising the extended service life and minimising the expected total life-cycle cost. With these four objectives, the single, bi-, tri- and quad-objective optimisation processes are investigated using the weighted sum method and genetic algorithms. The objective reduction approach with the Pareto optimal solutions is applied to estimate the degree of conflict among the objectives, and to identify the redundant objectives and minimum essential objective set. As a result, the efficiency in decision-making and visualisation for service life management can be improved by removing the redundant objectives.
Efficient multi-objective optimisation of probabilistic service life management
The inspection and maintenance plans to ensure the structural safety and extend the service life of deteriorating structures can be established effectively through an optimisation process. When several objectives are required for inspection and maintenance strategies, a multi-objective optimisation process needs to be used in order to consider all objectives simultaneously and to rationally select a well-balanced solution. However, as the number of objectives increases, additional computational efforts are required to obtain the Pareto solutions, for decision-making to select well-balanced solutions, and for visualisation of the solutions. This paper presents a novel approach to multi-objective optimisation process of probabilistic service life management with four objectives: minimising the damage detection delay, minimising the probability of failure, maximising the extended service life and minimising the expected total life-cycle cost. With these four objectives, the single, bi-, tri- and quad-objective optimisation processes are investigated using the weighted sum method and genetic algorithms. The objective reduction approach with the Pareto optimal solutions is applied to estimate the degree of conflict among the objectives, and to identify the redundant objectives and minimum essential objective set. As a result, the efficiency in decision-making and visualisation for service life management can be improved by removing the redundant objectives.
Efficient multi-objective optimisation of probabilistic service life management
Kim, Sunyong (Autor:in) / Frangopol, Dan M
2017
Aufsatz (Zeitschrift)
Englisch
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