Reliability-based stability analysis of large rock slopes with different failure mechanisms using response surface methodology - Kumar, Akshay; Pandit, Bhardwaj; Tiwari, Gaurav (Environmental earth sciences, 2022)
This paper presents the stability analysis of three real large rock slopes prone to failure with different mechanisms to investigate the applicability of the response surface methodology (RSM) for reliability-based rock slope problems. Initially, a detailed review of studies was performed to identify the gaps and common types of reliability-based rock slope problems. The applicability of RSMs based reliability methods was then investigated for three types of identified rock slope reliability problems via three case studies–type (1) Chenab rock slope prone to stress-controlled failures neglecting spatial variability, type (2) Deccan gold mine slope prone to stress-controlled failures considering spatial variability, and type (3) Rishikesh-Badrinath Highway slope prone to structurally controlled failures neglecting spatial variability. Analysis was performed by coupling MATLAB coded RSMs with advanced numerical tools and results were compared with those of direct Monte-Carlo Simulations (MCSs) based reliability method. Further, a detailed comparative study was carried out to evaluate the effect of the use of different response surfaces, i.e., polynomial based, Radial Basis Functions (RBFs) based, Support Vector Machine (SVM) based, kriging, Moving Least Square (MLS) and Gaussian Process Regression (GPR) on the accuracy and efficiency of RSMs based reliability analysis. Accuracy was evaluated using the Nash–Sutcliffe Efficiency (NSE) and Relative Difference in Reliability Index (RDRI), while computational efficiency was evaluated via the computational time required to evaluate the reliability with acceptable accuracy. RSMs based methods are observed to be highly accurate and efficient for the reliability analysis of large rock slopes. Further, the accuracy and efficiency are observed to be dependent upon the employed response surfaces and type of the problem. Suggestive guidelines are provided for selecting most suitable response surfaces for different problem types– a) for type 1 problem, Kriging and Least Square (LS)-SVM, b) for type 2 problem, some RBFs based and LS-SVM, and c) for type 3 problem, some RBFs based and LS-SVM are the most suitable RSMs.