A new computational method for structural reliability based on big data is proposed in this paper. Firstly, the big data is collected
via structural monitoring and is analyzed. The big data is then classified into different groups according to the regularities of
distribution of the data. In this paper, the stress responses of a suspension bridge due to different types of vehicle are obtained.
Secondly, structural reliability prediction model is established using the stress-strength interference theory under the repeated
loads after the stress responses and structural strength have been comprehensively considered. In addition, structural reliability
index is calculated using the first order second moment method under vehicle loads that are obeying the normal distribution. The
minimum reliability among various types of stress responses is chosen as the structural reliability. Finally, the proposed method
has been validated for its feasibility and effectiveness by an example.
Abstract. For the conventional computational methods for structural reliability analysis, the common limitations are long computational time, large number of iteration and low accuracy. Thus, a new novel method for structural reliability analysis has been proposed in this paper based on response surface method incorporated with an improved genetic algorithm. The genetic algorithm is first improved from the conventional genetic algorithm. Then, it is used to produce the response surface and the structural reliability is finally computed using the proposed method. The proposed method can be used to compute structural reliability easily whether the limit state function is explicit or implicit. It has been verified by two practical engineering cases that the algorithm is simple, robust, high accuracy and fast computation.
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