An optimization-based method of calibrating load and resistance factors: Application to slope and breakwaters’ foundation stability

Monte Carlo simulation (MCS)-based calibrations can accurately determine probabilistic load and resistance factors (LRFs) needed in the limit state designs. However, most of the computing time and effort of the calibrations is for evaluating performance functions if they are defined implicitly, as in the case of slope stabilities. This study proposes a robust framework that combines the advantages of adaptive artificial neural networks (ANNs) in approximating implicit performance functions with an optimization process to establish the LRFs quickly and automatically. Furthermore, experiment data obtained in the preceding iterations of the optimizations are accumulated and reused in the subsequent trial. For illustration, three implicit problems, including a slope and two cases of breakwater foundation stability, are examined to demonstrate the efficiency and accuracy of the proposed procedure. The investigations show that the proposed framework can be accurately completed within 1 h instead of lasting for weeks of calculation when using basic MCSs. Remarkably, reusing experiment data helps decrease the necessary data by two-thirds compared to only using the adaptive ANN, facilitating a faster calibration process. Thus, this work contributes a practical method for calibrating LRFs needed in limit state designs of geotechnical engineering fields wherein limit state equations are defined in implicit fashions. •An optimization-based calibration of load and resistance factors is proposed.•Limit state data beneficially enables adaptive metamodels for reliability analyses.•Reusing calibration data can reduce computing time by two-thirds.•The framework yields result in 1 h, far faster than basic MCSs taking weeks.