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Sustainability
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Slope Stability Monitoring and Evaluation
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Slope Stability Monitoring and Evaluation

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 6723

Special Issue Editors

Dr. Shuihua Jiang

E-Mail Website
Guest Editor
School of Infrastructure Engineering, Nanchang University, Xuefu Road 999, Nanchang 330031, China
Interests: modelling of spatial variability of geomaterials; geotechnical reliability and risk assessment; bayesian inverse analysis and reliability updating; probabilistic site characterization
Special Issues, Collections and Topics in MDPI journals
Dr. Zezhou Wang

E-Mail Website
Guest Editor
Department of Civil & Environmental Engineering, National University of Singapore, Singapore 117576, Singapore
Interests: geotechnical reliability and risk analysis; probabilistic back analysis of geotechnical systems; machine-learning/deep-learning in geotechnical analysis
Dr. Faming Huang

E-Mail Website
Guest Editor
School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, China
Interests: failure mechanism analysis of engineering and natural hazards; slope stability and reliability analysis; landslide susceptibility, hazard and risk mapping; machine learning and numerical simulation in slope engineering; remote sensing and geographic information system
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jinsong Huang

E-Mail Website
Guest Editor
ARC Centre of Excellence for Geotechnical Science and Engineering, University of Newcastle, Callaghan, NSW, Australia
Interests: risk assessment in geotechnical engineering; computational geomechanics; modelling of spatial variability; stress integration techniques for elastoplastic models; contact dynamics of granular media; analysis of hydraulic fracturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As a result of the ever-changing climate and frequent engineering activities, slope stability has become an increasingly prominent ecological environment issue that impacts societal development and human safety in recent years. Given the increasing intensity and frequency of extreme weather events and earthquakes, catastrophic natural and engineering disasters (e.g., landslide, rock fall, debris flow) may occur, resulting in huge casualties, economic loss, and ecological damage. Hence, it is crucial to understand issues such as slope deformation processes, slope instability mechanisms, and the influence of climate change and spatial variability on slope stability. Methods that monitor and evaluate slope stability can be either qualitative or quantitative. Site investigation, GPS, InSAR, theoretical analysis, experimental modeling, numerical simulation, probability statistics, machine learning, and deep learning techniques are among many examples of such methods.

In general, slope monitoring and evaluation are effective in mitigating slope instability. Slope monitoring can provide information on slope deformation, subsurface slope processes, rainfall, groundwater levels, internal structure, and mechanical properties of slopes. Recent advancements in equipment, data analytics, and field survey techniques significantly enhance the use of slope monitoring to reduce the risk of slope instability. Slope evaluation plays an equally significant role in the control, management, and mitigation of slope instability. In this regard, various quantitative approaches have been developed to predict (i) “where” (e.g., spatial prediction), (ii) “when” (e.g., temporal prediction), and (iii) “how” (e.g., number, size, impact, and destructiveness prediction). While some approaches focus on understanding regional characteristics such as susceptibility, hazard, risk, and vulnerability, there are also approaches that focus on the local aspects of slope behavior (e.g., slope stability, runout prediction, and change-detection mapping).

This Special Issue aims to promote research on slope stability monitoring and evaluation. We welcome submissions from various disciplines, including but not limited to the following topics:

  • Failure modes and mechanisms of slopes.
  • Impact of climate on slope stability.
  • Landslide monitoring and early warning systems.
  • Probabilistic slope stability assessment.
  • Influence of spatial variability on slopes.
  • Landslide susceptibility, hazard, and risk evaluation.
  • Prevention and control of geological disasters.

Dr. Shuihua Jiang
Dr. Zezhou Wang
Dr. Faming Huang
Prof. Dr. Jinsong Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • slope stability
  • climate change
  • failure mode and mechanism
  • spatial variability of slopes
  • landslide susceptibility, hazard and risk evaluation
  • monitoring and early warning
  • machine learning and deep learning
  • 3S technology

Published Papers (4 papers)

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Research

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17 pages, 3360 KiB  
Article
Sensitivity of EPA of Ground Motion to Soil Slope Dynamic Response
by Jiangwei Zhang, Yan Shen, Tao Lu, Ying Yuan and Chengda Zhang
Sustainability 2022, 14(24), 16985; https://doi.org/10.3390/su142416985 - 18 Dec 2022
Viewed by 1031
Abstract
To study the influence law of effective peak acceleration (EPA) on the seismic response of soil slope, the finite element method was used to simulate the slope response under earthquake action with 100 actual seismic records were selected, the influence law of the [...] Read more.
To study the influence law of effective peak acceleration (EPA) on the seismic response of soil slope, the finite element method was used to simulate the slope response under earthquake action with 100 actual seismic records were selected, the influence law of the EPA under four different definitions commonly used in domestic and foreign codes on the soil slope seismic response was discussed, and which was compared with the influence law of the peak acceleration (PGA). The results showed that the deformation and the maximum principal stress of soil slope both increased with the EPA and PGA, which had an obvious linear relationship, but the correlation degree were different with the parameters of PGA and EPA by the different definitions. EPA1 by the first definition has the highest correlation with the soil slope seismic response, followed by PGA, which was close to EPA1. Other parameters in order of correlation coefficient were EPA2, EPA3 and EPA4. In this example, EPA1 and PGA could better describe the response degree of soil slope in earthquake. The results are expected to provide a basis for the selection of seismic parameters in soil slope seismic stability evaluation. Full article
(This article belongs to the Special Issue Slope Stability Monitoring and Evaluation)
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17 pages, 16669 KiB  
Article
Landslide Susceptibility Prediction: Improving the Quality of Landslide Samples by Isolation Forests
by Qinghua Zhang, Zhu Liang, Wei Liu, Weiping Peng, Houzan Huang, Shouwen Zhang, Lingwei Chen, Kaihua Jiang and Lixing Liu
Sustainability 2022, 14(24), 16692; https://doi.org/10.3390/su142416692 - 13 Dec 2022
Cited by 4 | Viewed by 1387
Abstract
Landslide susceptibility prediction (LSP) is the first step to ease landslide disasters with the application of various machine learning methods. A complete landslide inventory, which is essential but difficult to obtain, should include high-quality landslide and non-landslide samples. The insufficient number of landslide [...] Read more.
Landslide susceptibility prediction (LSP) is the first step to ease landslide disasters with the application of various machine learning methods. A complete landslide inventory, which is essential but difficult to obtain, should include high-quality landslide and non-landslide samples. The insufficient number of landslide samples and the low purity of non-landslide samples limit the performance of the machine learning models. In response, this study aims to explore the effectiveness of isolated forest (IF) to solve the problem of insufficient landslide samples. IF belongs to unsupervised learning, and only a small share of landslide samples in the study area were required for modeling, while the remaining samples were used for testing. Its performance was compared to another advanced integration model, adaptive boosting integrated with decision tree (Ada-DT), which belongs to two-class classifiers (TCC) and needs a sufficient number of samples. Huangpu District, Guangzhou City, Guangdong Province in China, was selected as the study area, and 13 predisposing factors were prepared for the modeling. Results showed that the IF proved its effectiveness with an AUC value of 0.875, although the Ada-DT model performed better (AUC = 0.921). IF outperformed the Ada-DT model in terms of recognizing landslides, and the sensitivity values of IF and the Ada-DT model were 90.00% and 86.67%, respectively, while the Ada-DT model performed better in terms of specificity. Two susceptibility maps obtained by the models were basically consistent with the field investigation, while the areas predicted by IF tended to be conservative as higher risk areas were presented, and the Ada-DT model was likely to be risky. It is suggested to select non-landslide samples from the very low susceptibility areas predicted by the IF model to form a more reliable sample set for Ada-DT modeling. The conclusion confirms the practicality and advancement of the idea of anomaly detection in LSP and improves the application potential of machine learning algorithms for geohazards. Full article
(This article belongs to the Special Issue Slope Stability Monitoring and Evaluation)
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17 pages, 3798 KiB  
Article
Study on the Influence of Seismic Wave Parameters on the Dynamic Response of Anti-Dip Bedding Rock Slopes under Three-Dimensional Conditions
by Zhanghao Ren, Congxin Chen, Yun Zheng, Chaoyi Sun and Jiahao Yuan
Sustainability 2022, 14(18), 11321; https://doi.org/10.3390/su141811321 - 9 Sep 2022
Cited by 1 | Viewed by 1150
Abstract
As a result of earthquakes, the deformation and failure caused by anti-dip bedding rock slopes are large, and their seismic dynamic response law is complex. Using the three-dimensional discrete element numerical analysis software 3DEC, the influence of seismic wave parameters on the dynamic [...] Read more.
As a result of earthquakes, the deformation and failure caused by anti-dip bedding rock slopes are large, and their seismic dynamic response law is complex. Using the three-dimensional discrete element numerical analysis software 3DEC, the influence of seismic wave parameters on the dynamic response of anti-dip bedding rock slopes was systematically studied, with special focus on the influence of the angle between seismic wave incidence direction and slope trend on the dynamic response of anti-dip bedding rock slopes under three-dimensional conditions. The orthogonal test was designed to conduct sensitivity analysis of five seismic parameters, including seismic wave amplitude, incidence angle of the S-wave, frequency, duration, and the time difference between the P-wave’s and the S-wave’s peak. The results revealed that the S-wave’s amplitude As and the holding time T of the seismic wave are positively correlated with the acceleration amplification factor of the slope, and the incident direction γ of the S-wave is negatively correlated with the acceleration amplification factor of the slope. The increase of seismic wave frequency f and the time difference Δt between the P-wave’s and the S-wave’s peak lead to the first increase and then decrease of the Y-directional displacement of the slope. The sensitivity of each seismic wave parameter to the Y-directional acceleration amplification factor at the shoulder of anti-dip bedding rock slopes in earthquake conditions is ordered as follows: S-wave’s amplitude As > frequency f > S-wave’s incidence angle γ > the time difference Δt > holding time T. the study results provide reference and basis for stability evaluation and engineering design of anti-dip bedding rock slopes in areas with high seismic intensity. Full article
(This article belongs to the Special Issue Slope Stability Monitoring and Evaluation)
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Review

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15 pages, 656 KiB  
Review
Morphological Assessment of River Stability: Review of the Most Influential Parameters
by Nor Azidawati Haron, Badronnisa Yusuf, Mohd Sofiyan Sulaiman, Mohd Shahrizal Ab Razak and Siti Nurhidayu
Sustainability 2022, 14(16), 10025; https://doi.org/10.3390/su141610025 - 12 Aug 2022
Cited by 3 | Viewed by 1740
Abstract
River health assessments in the form of morphological approaches are crucial to determining the stability of a river system. Human interference in the natural river landscapes has altered the regime of river flows in the past. The catastrophes arising from the regime alteration [...] Read more.
River health assessments in the form of morphological approaches are crucial to determining the stability of a river system. Human interference in the natural river landscapes has altered the regime of river flows in the past. The catastrophes arising from the regime alteration are varied: excessive erosion and sedimentation, low carrying capacity, depletion of water yield, and many more. Past researchers have formulated numerous assessments to examine the stability of a river system. Still, arguments are prevalent due to the opinionated nature of the evaluation and a lack of parameters about river equilibrium. This paper reviews the past approaches to assessing channel stability by revisiting the most influential parameters adopted in the assessment process. An Analytical Hierarchy Process (AHP) was employed to find the prioritization of the selected parameters. This study found that a field survey is the most preferred method of river assessment instead of the other techniques such as remote sensing, modeling, and rapid field assessment. The most influential parameters (top 5) that determine the stability of a river system are (1) channel forms, (2) channel dimensions, (3) channel substrates, (4) channel pattern, and (5) bank profile. Those parameterizations are crucial to determining the stability of a river system. Full article
(This article belongs to the Special Issue Slope Stability Monitoring and Evaluation)
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