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Structural Engineering and Mechanics
  Volume 71, Number 4, August25 2019, pages 329-339

An evolutionary approach for structural reliability
Alireza Garakaninezhad and Morteza Bastami

    Assessment of failure probability, especially for a complex structure, requires a considerable number of calls to the numerical model. Reliability methods have been developed to decrease the computational time. In this approach, the original numerical model is replaced by a surrogate model which is usually explicit and much faster to evaluate. The current paper proposed an efficient reliability method based on Monte Carlo simulation (MCS) and multi-gene genetic programming (MGGP) as a robust variant of genetic programming (GP). GP has been applied in different fields; however, its application to structural reliability has not been tested. The current study investigated the performance of MGGP as a surrogate model in structural reliability problems and compares it with other surrogate models. An adaptive Metropolis algorithm is utilized to obtain the training data with which to build the MGGP model. The failure probability is estimated by combining MCS and MGGP. The efficiency and accuracy of the proposed method were investigated with the help of five numerical examples.
Key Words
    reliability; genetic programming; surrogate model; metropolis algorithm; failure probability; Monte Carlo
Alireza Garakaninezhad: Iranian Academic Center for Education, Culture and Research, Kerman 7616914111, Iran
Morteza Bastami: International Institute of Earthquake Engineering and Seismology (IIEES), No. 26, Arghavan St., North Dibajee, Farmanieh,
P.O. Box: 19395/3913, Tehran, Iran

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