Risk assessment of slope failure considering the variability in soil properties

Cheng, Hongzhan; Chen, Jian; Chen, Renpeng; Chen, Guoliang; Yu, Zhong

doi:10.1016/j.compgeo.2018.07.006

Cited by 77 publications

(24 citation statements)

References 26 publications

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“…In addition, in geotechnical engineering, some computing software is used to build geological models to analyze and evaluate the safety and stability of engineering geological bodies [18].…”

Section: D Geological Modelmentioning

confidence: 99%

Development of Data Integration and Sharing for Geotechnical Engineering Information Modeling Based on IFC

Chen

et al. 2021

Advances in Civil Engineering

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With the rapid development of infrastructure construction, geotechnical engineering has always been worthy of attention due to its complexity and diversity. Accelerating the informatization of geotechnical engineering will contribute to the project management, but the information contained in geotechnical engineering cannot be well integrated because of the lack of unified data standards. Building Information Modeling (BIM) has been considered as an effective technology to manage information, and Industry Foundation Classes (IFC) in BIM serves as a neutral and open standard for the exchange of information. However, it was found that BIM cannot express the information of some structure objects and geological objects well during the construction process of geotechnical engineering. Combined with the characteristics of geotechnical engineering, taking advantage of the good extensibility of IFC, this paper proposes a “Built-In Generation Schema” for geotechnical structure models and a “Plug-In Extension Schema” for three-dimensional (3D) geological models, ultimately forming the basic data system of geotechnical engineering information models based on IFC. Applying extended IFC to the modeling process, the BIM-based modeling method of geotechnical models is proposed. In addition, an IFC-based platform is developed to integrate geological models and structure models for further displaying and analyzing of geotechnical engineering models. The work in this paper provides a feasible way and technical support for promoting the integration and sharing of geotechnical engineering information and enhancing the multiprofessional collaborative work.

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Section: D Geological Modelmentioning

confidence: 99%

Development of Data Integration and Sharing for Geotechnical Engineering Information Modeling Based on IFC

Chen

et al. 2021

Advances in Civil Engineering

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“…Graphs of SiEF and SqEF (for unit correlation length) for 1‐D and 2‐D variables are shown in Figure 1. The surface of SqEF is isotropic and smooth, and SqEF is differentiable near the origin 38,39 . However, there is no analytical solution to the integral equation when performing the spectral decomposition of SqEF during RF generation, as discussed in the following sections.…”

Section: Simulation Of the Spatially Varied Soil Strength Via Rfmentioning

confidence: 99%

Efficient reliability analysis of slopes integrating the random field method and a Gaussian process regression‐based surrogate model

Zhu

Hiraishi

Pei

et al. 2020

Num Anal Meth Geomechanics

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Efficient evaluation of slope stability is a frontier in geo‐disaster prevention fields. While many slope stability evaluation methods, ranging from deterministic to probabilistic, have been proposed, reliability methods are particularly advantageous as they can account for uncertainties during slope stability evaluation. To address the problem of the low efficiency of direct Monte Carlo simulations and overcome the defects of the traditional response surface method, in this work, a novel probabilistic procedure that integrates a Gaussian process regression‐based surrogate model and the random limit equilibrium method for slope stability evaluation while accounting for the spatial variability of soil strength is proposed. The novel Gaussian process regression‐based surrogate model is efficiently used in the Monte Carlo simulation to reduce the number of calls for direct stability analysis of a slope with spatially varied soil strength. To verify the accuracy and efficiency of the proposed procedure, it was applied to three case studies: the first one is a slope in saturated clay under undrained conditions; the second one is a slope for which the friction angle and cohesion values are cross‐correlated; and the third one is a real slope with multiple soil layers. The results obtained from the comparisons with other methods confirmed the precision and feasibility of the proposed procedure.

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“…Random Field Generation Method. With reference to studies by other researchers [30][31][32], the generation of random fields in the present study is based on covariance matrix decomposition. e finite difference software FLAC3D is employed in this research.…”

Section: Numerical Characteristics Of Random Fieldsmentioning

confidence: 99%

A Prediction Method Based on Monte Carlo Simulations for Finite Element Analysis of Soil Medium considering Spatial Variability in Soil Parameters

Tang

Wang

2020

Advances in Materials Science and Engineering

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With the Stochastic Finite Element Method (SFEM), the spatial variability of soil properties can be incorporated into the analysis of geotechnical structures. Although this method is significantly superior in principle to the homogeneous analysis of soil parameters, generalizing the method in engineering practice is difficult due to its computational inefficiency. In this paper, we propose a new method for the fast calculation of convergence results. The proposed method introduces a distance space to the Monte Carlo Method (MCM) random field instances and, considering the importance of a safety margin in structures, uses selected spatial interpolation to predict the MCM instances to be solved. Two case study simulations are presented. The results show that compared to the full Monte Carlo Simulation, the fast calculation method proposed in this paper can achieve very accurate convergence results while substantially reducing the computational cost, and the simulation errors for the structure are on the safer side.

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Risk assessment of slope failure considering the variability in soil properties

Cited by 77 publications

References 26 publications

Development of Data Integration and Sharing for Geotechnical Engineering Information Modeling Based on IFC

Development of Data Integration and Sharing for Geotechnical Engineering Information Modeling Based on IFC

Efficient reliability analysis of slopes integrating the random field method and a Gaussian process regression‐based surrogate model

A Prediction Method Based on Monte Carlo Simulations for Finite Element Analysis of Soil Medium considering Spatial Variability in Soil Parameters

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