A novel hybrid adaptive framework for support vector machine-based reliability analysis: A comparative study

Abstract

This study presents an innovative hybrid Adaptive Support Vector Machine - Monte Carlo Simulation (ASVM-MCS) framework for reliability analysis in complex engineering structures. These structures often involve highly nonlinear implicit functions, making traditional gradient-based first or second order reliability algorithms and Monte Carlo Simulation (MCS) time-consuming. The application of surrogate models has proven effective in addressing computational challenges associated with a large number of simulations. Support Vector Machine (SVM), as an emerging machine learning method suitable for small-sample scenarios, offers a well-established theoretical foundation and presents an effective model substitution approach for reliability analysis in engineering structures. However, the existing literature lacks a comprehensive and thorough comparative analysis of SVM's hybrid adaptive modeling approach, encompassing initial sampling methods and learning functions, with regards to both computational efficiency and accuracy. Additionally, there is a gap in adaptive modeling methods capable of accommodating diverse types of input uncertainty, the nonlinearity of limit state functions, and various application scenarios. In response to these gaps, this article introduces the ASVM-MCS framework, which addresses these challenges by considering different types of input variables and various failure modes. Moreover, this study provides a comprehensive evaluation of the ASVM-MCS framework's performance, including its initial sampling methods and learning functions, across a range of application scenarios, such as scenarios involving only random variables, mixed variables, and the reliability of series-parallel systems.