Abstract :
In this study, a reliability evaluation method, integrated with artificial neural network (ANN), and first-order reliability method (FORM), or Monte-Carlo simulation (MCS), is explored. By performing a case study on the reliability of deep excavation within soft ground, an analysis procedure for reliability analysis is proposed. The evaluation model of ANN-based FORM or ANN-based MCS is superior to traditional reliability method, such as first-order second-moment method (FOSM), in view of many aspects, such as system modeling, computational efficiency, and analysis precision. Based on these methods, the reliability of different serviceability performance or limit states for braced excavation problems can be assessed easily, efficiently and accurately. The effectiveness of ground modification measures, once needed, against excavation failure can also be evaluated quantitatively. Hence, it will supply the deep excavations engineering with a powerful tool for safety evaluation when using the established reliability-based risk analysis and design method in this research. And also, excavations safety in metropolis will be guaranteed definitely.
Keywords :
Monte Carlo methods; civil engineering computing; neural nets; reliability; Monte-Carlo simulation; artificial neural network; deep excavation; first-order reliability method; reliability analysis; reliability evaluation method; Artificial neural networks; Computational efficiency; Design engineering; Modeling; Performance analysis; Power engineering and energy; Power system reliability; Reliability engineering; Risk analysis; Safety; Monte-Carlo simulation; artificial neural network; first-order reliability method; reliability-based design;
