A novel probabilistic simulation approach for forecasting the safety factor of slopes: a case study

Mojtahedi, S. Farid F.; Tabatabaee, Sanaz; Ghoroqi, Mahyar; Tehrani, Mehran Soltani; Gordan, Behrouz; Ghoroqi, Milad

doi:10.1007/s00366-018-0623-5

Cited by 20 publications

(9 citation statements)

References 48 publications

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“…Zhou et al [17] used reliability coefficients with cohesion and internal friction angle as components, following other studies of sensitivity analysis for embankment slope parameters [34][35][36][37]. However, fluctuating water levels coupled with the static train loads found a sensitivity analysis of railway embankment slopes complicated.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Railway Embankment Slope Hydromechanical Properties under Bidirectional Water Level Fluctuations

Aliyu,

Xu,

Bello

et al. 2024

Applied Sciences

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Railway embankment slopes are exposed to natural hazards such as excess rainfall, floods, earthquakes, and lake water/groundwater level variations. These are generally considered during the design, construction, and maintenance periods of the embankment. In this study, combined laboratory test methods and a computational approach were applied to assess the effect of groundwater level changes on the railway embankment. The Plackett–Burman (PBD), Box–Behnken design response surface methodology (BBD-RSM), and an artificial neural network (ANN) were used to predict the behavior of the embankment soil hydromechanical properties to determine the integrity of the embankment as water level fluctuates under varied seasonal conditions. The results show that the seepage line is concave during the rising water level (RWL) period, and the railway slope’s static stability factor surges and then stabilizes. Further analysis found that the slope’s stability is largely affected by some of the hydromechanical properties of the soil embankment material, such as the internal friction angle (ϕ), soil density (ρs), and cohesion (c). The second-order interaction factors c x s, x s, and s2 also affect the stability factor. It was observed that the four most sensitive parameters under both falling water level (FWL) and RWL conditions are ϕ, ρs, c, and rate of fall/rise in water level (H). The statistical evaluation of the RSM model produced R2 values of 0.99(99) and 0.99, with MREs of 0.01 and 0.24 under both RWL and FWL conditions, respectively, while for ANN, they produced R2 values of 0.99(99) and 0.99(98), with MRE values of 0.02 and 0.21, respectively. This study demonstrates that RSM and ANN performed well under these conditions and enhanced accuracy, efficiency, iterations, trial times, and cost-effectiveness compared to full laboratory experimental procedures.

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Section: Introductionmentioning

confidence: 99%

Prediction of Railway Embankment Slope Hydromechanical Properties under Bidirectional Water Level Fluctuations

Aliyu,

Xu,

Bello

et al. 2024

Applied Sciences

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“…In the past few decades, many researchers have contributed to slope stability prediction and significant progress has been achieved in landslide disaster prevention (e.g., Sakellariou and Ferentinou, 2005;Gordan et al, 2016;Mahdiyar et al, 2017;Mojtahedi et al, 2019;Bui et al, 2020;Wang et al, 2020cZeng et al, 2021;Zhuang and Xing, 2021). For example, Sakellariou and Ferentinou (2005) introduced neural networks to predict slope stability.…”

Section: Introductionmentioning

confidence: 99%

“…It was observed that the PSO-ANN model was superior to the remaining three hybrid intelligent models in predicting the FS of slopes. Mojtahedi et al (2019) proposed an MC-based probabilistic approach for forecasting the FS of slopes and found that the internal friction angle was the most influential factor among the four inputs through conducting sensitivity analysis. Zhou et al (2019) applied a gradient boosting machine (GBM) approach to predict the stability status of slopes based on an updated database that records a total of 221 historical cases gathered from the literature.…”

Section: Introductionmentioning

confidence: 99%

Efficient Seismic Stability Analysis of Embankment Slopes Subjected to Water Level Changes Using Gradient Boosting Algorithms

Zhang

Wang

et al. 2021

Front. Earth Sci.

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Embankments are widespread throughout the world and their safety under seismic conditions is a primary concern in the geotechnical engineering community since the failure events may lead to disastrous consequences. This study proposes an efficient seismic slope stability analysis approach by introducing advanced gradient boosting algorithms, namely Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost). A database consisting of 600 datasets is prepared for model calibration and evaluation, where the factor of safety (FS) is regarded as the output and four influential factors are selected as the inputs. For each dataset, the FS corresponding to the four inputs is evaluated using the commercial geotechnical software of Slide2. As an illustration, the proposed approach is applied to the seismic stability analysis of a hypothetical embankment example subjected to water level changes. For comparison, the predictive performance of CatBoost, LightGBM, and XGBoost is investigated. Moreover, the Shapley additive explanations (SHAP) method is used in this study to explore the relative importance of the four features. Results show that all the three gradient boosting algorithms (i.e., CatBoost, LightGBM, and XGBoost) perform well in the prediction of FS for both the training dataset and testing dataset. Among the four influencing factors, the friction angle φ is the most important feature variable, followed by horizontal seismic coefficient Kh, cohesion c, and saturated permeability ks.

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“…For the existing studies on reservoir landslide, researches mainly adopted theoretical, experimental, and numerical simulation methods. For example, with respect to theoretical research, a novel probabilistic simulation approach was been proposed by Mojtahedi et al (2018) to forecast the safety factor of slopes, and the sensitivity of the safety factor on the effective parameters was identified. Su et al (2021a) used the HBP rheological model and Drucker-Prager yield criterion to simulate the dynamic catastrophic process of landslide.…”

Section: Introductionmentioning

confidence: 99%

Deterministic and Uncertainty Analysis of a Reservoir Slope Stability and Failure Mechanism Under Combined Action of Multi-hazards

Wang

Zhang

2021

Geotech Geol Eng

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Influenced by continuous rainfall and water level fluctuation, a large number of landslide disasters occur in the reservoir areas in China every year, causing serious property losses and casualties. Based on the unsaturated seepage theory, the deterministic method and Monte-Carlo method are used to simulate the safety state of a reservoir bank slope under combined action of continuous rainfall and fluctuation of water level. Besides, the variation trend of safety factor, failure probability and reliability index of the slope under different working conditions are also obtained. By revealing the instantaneous variation characteristics of the equivalent plastic zone, the progressive failure mechanism of the slope is analyzed. The most dangerous condition is the time when the water level drops by 3 m/d and the rainfall changes at 30 mm/d simultaneously. This study provides a reference for risk analysis and emergency management of reservoir slope under extreme conditions.

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A novel probabilistic simulation approach for forecasting the safety factor of slopes: a case study

Cited by 20 publications

References 48 publications

Prediction of Railway Embankment Slope Hydromechanical Properties under Bidirectional Water Level Fluctuations

Prediction of Railway Embankment Slope Hydromechanical Properties under Bidirectional Water Level Fluctuations

Efficient Seismic Stability Analysis of Embankment Slopes Subjected to Water Level Changes Using Gradient Boosting Algorithms

Deterministic and Uncertainty Analysis of a Reservoir Slope Stability and Failure Mechanism Under Combined Action of Multi-hazards

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