Reliability Analysis via an Optimal Covariance Matrix Adaptation Evolution Strategy: Emphasis on Applications in Civil Engineering

  • Ali Kaveh
    Affiliation

    Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of Science and Technology, Narmak, University St., Hengam St., Resalat Square, 13114-16846, Tehran, Iran

  • Seyed Mohammad Javadi
    Affiliation

    Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of Science and Technology, Narmak, University St., Hengam St., Resalat Square, 13114-16846, Tehran, Iran

  • Roya Mahdipour Moghanni
    Affiliation

    Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of Science and Technology, Narmak, University St., Hengam St., Resalat Square, 13114-16846, Tehran, Iran

Abstract

In this paper, a reliability-based optimization approach is applied using a recently proposed CMA-ES with optimal covariance update and storage complexity. Cholesky-CMA-ES gives a significant increase in optimization speed and reduces the runtime complexity of the standard CMA-ES. The reliability index is the shortest distance between the surface of Limit-State Function (LSF) and the origin of the standard normal space. Hence, finding the reliability index can be expressed as a constrained optimization problem. To verify the concept and test the feasibility of this algorithm, several numerical examples consisting of mathematical and highly nonlinear civil engineering problems are investigated. The reliability indexes obtained agree reasonably well with reported values from some existing approximation methods and Monte Carlo simulation.

Keywords: reliability analysis, FORM, optimal CMA-ES, computational efficiency, uncertainty
Published online
2020-04-17
How to Cite
Kaveh, A., Javadi, S. M., Mahdipour Moghanni, R. “Reliability Analysis via an Optimal Covariance Matrix Adaptation Evolution Strategy: Emphasis on Applications in Civil Engineering”, Periodica Polytechnica Civil Engineering, 64(2), pp. 579-588, 2020. https://doi.org/10.3311/PPci.15793
Section
Research Article