The use of probabilistic analysis (PA) of slopes as an effective method for evaluating the uncertainty that is so pervasive in variables has become increasingly common in recent years. This study presents a case study which was conducted to demonstrate the efficiency of an embankment which consists of an 11.693 m-high soil slope, placing emphasis on PA and reliability evaluation. The investigation employs Monte Carlo simulation (MCS) and subset simulation (SS) techniques, considering seismic coefficients (kh) of 0.12 for Zone-III and 0.14 for Zone-IV, along with varying pore water pressure ratios (ru=0.0, [Formula: see text], and [Formula: see text]). MCS with 10,000 samples was used to test the probabilistic response of the proposed embankment. This work also discusses the results of SS, in which 1,400 samples are generated from UPSS 3.0 Excel add-ins, which permits rapid PA in such a way that they progressively shift toward the failure zone in successive stages. The study delves into the impact of uncertainty on the probability of failure [Formula: see text]. Findings reveal an increased [Formula: see text] with rising coefficients of variation, [Formula: see text], and kh values, underscoring the sensitivity to soil parameter variations. SS outperforms MCS in simulating low probabilities, demanding smaller sample sizes and less computational time. Furthermore, the machine learning technique has been used to optimize the worst [Formula: see text] condition. In this current research, three neural network-based models, namely recurrent neural network, long short-term memory (LSTM), and Bayesian neural network, have been used. Based on the performance of the models, the three neural network-based models were compared in the testing phase, and the proposed LSTM outperformed the other neural networks ( R2 = 0.9962 and root mean square error = 0.0051).
Machine Learning-Aided Monte Carlo Simulation and Subset Simulation
Transportation Research Record: Journal of the Transportation Research Board
2024-06-06
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Learning About Ares I from Monte Carlo Simulation
AIAA | 2008
|Learning About Ares I from Monte Carlo Simulation
NTIS | 2008
|Learning About Ares I from Monte Carlo Simulation
NTRS | 2008
|Distributional Direct Simulation Monte Carlo Methods
AIAA | 2010
|