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Flowing Mechanism of Debris Flow and Engineering Mitigation
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Flowing Mechanism of Debris Flow and Engineering Mitigation

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydraulics and Hydrodynamics".

Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 6329

Special Issue Editor

Prof. Dr. Weijie Zhang

E-Mail Website
Guest Editor
College of Civil and Transportation Engineering, Hohai University, Nanjing, China
Interests: extreme rainfall; water–soil coupling; debris flow; numerical simulation; impact force; machine learning; artificial neural network; risk assessment; mitigation measure

Special Issue Information

Dear Colleagues,

With the development of the economy and society, the impact of human activities on the climate has become increasingly intense. As a consequence, extreme rainfall events frequently occur all over the world, triggering geohazards that bring great suffering to humans and lead to the loss to their belongings. Debris flow, as a water-induced geohazard, has attracted increasing attention worldwide, and research has been carried out for the purposes of preventing disasters and reducing damages.

Therefore, this Special Issue aims to present original research and review articles that discuss slope stability under rainfall, the water–soil coupling mechanism in flow, numerical modeling and artificial intelligence prediction of debris flow, influence range evaluation and risk assessment, engineering mitigation measures, etc.

Potential topics include, but are not limited to, the following:

  1. Stability and failure mechanism of slope under a rainfall event;
  2. Water–soil coupling in debris flow;
  3. Numerical modeling of debris flow;
  4. Artificial intelligence in predicting debris flow;
  5. Influence range evaluation and risk assessment;
  6. Engineering measures to prevent and mitigate the disaster of debris flow.

We look forward to receiving your contributions.

Prof. Dr. Weijie Zhang
Guest Editor

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. Water 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 2600 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

  • extreme rainfall
  • water–soil coupling
  • debris flow
  • numerical simulation
  • impact force
  • machine learning
  • artificial neural network
  • risk assessment
  • engineering mitigation

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Published Papers (6 papers)

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Research

15 pages, 16024 KiB  
Article
Numerical Simulations of a Permeability Test on Non-Cohesive Soil Under an Increasing Water Level
by Weijie Zhang, Hongxin Chen, Lei Xiong and Liang Chen
Water 2024, 16(20), 2992; https://doi.org/10.3390/w16202992 - 20 Oct 2024
Viewed by 652
Abstract
With the intensification of global climate change, extreme rainfall events are occurring more frequently. Continuous rainfall causes the debris flow gully to collect a large amount of rainwater. Under the continuous increase in the water level, the water flow has enough power to [...] Read more.
With the intensification of global climate change, extreme rainfall events are occurring more frequently. Continuous rainfall causes the debris flow gully to collect a large amount of rainwater. Under the continuous increase in the water level, the water flow has enough power to carry plenty of loose solids, thus causing debris flow disasters. The intensity of the soil is reduced with the infiltration of rainwater, which is one of the key causes of the disaster. The rise in the water level affects the infiltration behavior. There have been few previous studies on infiltration under variable head. In order to understand the infiltration behavior of soils under the action of water level rises, this paper conducted an indoor permeability test on non-cohesive soil under the condition of an increasing water level. A numerical model was established using the finite element analysis software, Abaqus 6.14, and the pore pressure was increased intermittently to simulate the intermittent increase in the water level. Thereafter, the permeability coefficient and seepage length were changed to interpret the changes in the flow velocity and rate in the permeability test of the non-cohesive soil. The results showed that the finite element numerical simulation method could not reflect the particle movement process in the soil. The test could better reflect the through passage and void plugging phenomenon in soil; when the permeability coefficient alone changed, the velocity of the measuring point with higher velocity changed more violently with the permeability coefficient; when the length of soil seepage diameter was uniformly shortened, the velocity of water flow increased faster and faster. Full article
(This article belongs to the Special Issue Flowing Mechanism of Debris Flow and Engineering Mitigation)
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15 pages, 8233 KiB  
Article
Investigation of the Deformation Behavior of Baffle Structures Impacted by Debris Flow Based on Physical Modelling
by Weizhi Chen, Bei Zhang, Na Xu and Yu Huang
Water 2024, 16(14), 2046; https://doi.org/10.3390/w16142046 - 19 Jul 2024
Viewed by 731
Abstract
The utilization of baffle structures as a highly effective strategy for mitigating debris flow has attracted significant scholarly attention in recent years. Although the predominant focus of existing research has been on augmenting the energy dissipation capabilities of baffle structures, their deformation behavior [...] Read more.
The utilization of baffle structures as a highly effective strategy for mitigating debris flow has attracted significant scholarly attention in recent years. Although the predominant focus of existing research has been on augmenting the energy dissipation capabilities of baffle structures, their deformation behavior under impact load has not been extensively investigated. Addressing this research gap, the current study systematically designs a series of physical model experiments, incorporating variables such as baffle height, shape, and various combinations of baffle types to comprehensively analyze the deformation characteristics of baffles subjected to debris flow impact. The experimental results reveal that the deformation of baffle group structures demonstrates a marked non-uniform spatial distribution and exhibits a latency effect. Additionally, distinct baffle configurations show considerable variations in peak strain, suggesting that combining different baffle shapes can not only optimize energy dissipation but also enhance resistance to deformation. Moreover, the relationship between baffle height and the development of deformation in relation to energy dissipation capacity is inconsistent, indicating that deformation must be a key consideration in the design of baffle structures. Consequently, this paper advocates for the formulation of a deformation-based design strategy for baffle structures, with the findings presented herein providing a foundational reference for future studies. Full article
(This article belongs to the Special Issue Flowing Mechanism of Debris Flow and Engineering Mitigation)
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14 pages, 24885 KiB  
Article
A Case Study and Numerical Modeling of Post-Wildfire Debris Flows in Montecito, California
by Diwakar K. C., Mohammad Wasif Naqvi and Liangbo Hu
Water 2024, 16(9), 1285; https://doi.org/10.3390/w16091285 - 30 Apr 2024
Cited by 1 | Viewed by 1250
Abstract
Wildfires and their long-term impacts on the environment have become a major concern in the last few decades, in which climate change and enhanced anthropogenic activities have gradually led to increasingly frequent events of such hazards or disasters. Geological materials appear to become [...] Read more.
Wildfires and their long-term impacts on the environment have become a major concern in the last few decades, in which climate change and enhanced anthropogenic activities have gradually led to increasingly frequent events of such hazards or disasters. Geological materials appear to become more vulnerable to hazards including erosion, floods, landslides and debris flows. In the present study, the well-known 2017 wildfire and subsequent 2018 debris flows in the Montecito area of California are examined. It is found that the post-wildfire debris flows were initiated from erosion and entrainment processes and triggered by intense rainfall. The significant debris deposition in four major creeks in this area is investigated. Numerical modeling of the post-wildfire debris flows is performed by employing a multi-phase mass flow model to simulate the growth in the debris flows and eventual debris deposition. The debris-flow-affected areas estimated from the numerical simulations fairly represent those observed in the field. Overall, the simulated debris deposits are within 7% error of those estimated based on field observations. A similar simulation of the pre-wildfire scenario indicates that the debris would be much less significant. The present study shows that proper numerical simulations can be a promising tool for estimating post-wildfire erosion and the debris-affected areas for hazard assessment and mitigation. Full article
(This article belongs to the Special Issue Flowing Mechanism of Debris Flow and Engineering Mitigation)
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15 pages, 1695 KiB  
Article
Monitoring and Early Warning Method of Debris Flow Expansion Behavior Based on Improved Genetic Algorithm and Bayesian Network
by Jun Li, Javed Iqbal Tanoli, Miao Zhou and Filip Gurkalo
Water 2024, 16(6), 908; https://doi.org/10.3390/w16060908 - 21 Mar 2024
Viewed by 1085
Abstract
Based on an improved genetic algorithm and debris flow disaster monitoring network, this study examines the monitoring and early warning method of debris flow expansion behavior, divides the risk of debris flow disaster, and provides a scientific basis for emergency rescue and post-disaster [...] Read more.
Based on an improved genetic algorithm and debris flow disaster monitoring network, this study examines the monitoring and early warning method of debris flow expansion behavior, divides the risk of debris flow disaster, and provides a scientific basis for emergency rescue and post-disaster recovery. The function of the debris flow disaster monitoring network of the spreading behavior disaster chain is constructed. According to the causal reasoning of debris flow disaster monitoring information, the influence factors of debris flow, such as rainfall intensity and duration, are selected as the inputs of the Bayesian network, and the probability of a debris flow disaster is obtained. The probability is compared with the historical data threshold to complete the monitoring and early warning of debris flow spreading behavior. Innovatively, by introducing niche technology to improve traditional genetic algorithms by learning Bayesian networks, the optimization efficiency and convergence speed of genetic algorithms are improved, and the robustness of debris flow monitoring and warning is enhanced. The experimental results show that this method divides debris flow disasters into the following five categories based on their danger: low-risk area, medium-risk area, high-risk area, higher-risk area, and Very high-risk area. It accurately monitors the expansion of debris flows and completes early warning. The disaster management department can develop emergency rescue and post-disaster recovery strategies based on early warning results, thus providing a scientific basis for debris flow disasters. The improved genetic algorithm has a higher learning efficiency, a higher accuracy, a faster convergence speed, and higher advantages in learning time and accuracy of the Bayesian network structure. Full article
(This article belongs to the Special Issue Flowing Mechanism of Debris Flow and Engineering Mitigation)
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16 pages, 2074 KiB  
Article
Multi-Index Fusion Debris Flow Early Warning Model Based on Spatial Interpolation and Support Vector Machine
by Rui Jin, Shaoqi Wang and Jianfei Liu
Water 2024, 16(5), 724; https://doi.org/10.3390/w16050724 - 28 Feb 2024
Viewed by 950
Abstract
Debris flow early warning is an effective method to prevent major disasters, so a multi-index fusion debris flow early warning model based on spatial interpolation and a support vector machine is designed. Aiming at the discrete rainfall data in the study area, the [...] Read more.
Debris flow early warning is an effective method to prevent major disasters, so a multi-index fusion debris flow early warning model based on spatial interpolation and a support vector machine is designed. Aiming at the discrete rainfall data in the study area, the collaborative Kriging spatial interpolation method based on Kriging spatial interpolation is adopted to process the rainfall data into multi-index fused surface data. The rainfall data after spatial interpolation are used as the input sample data of the support vector machine early warning model, and the optimal parameters of the support vector machine are calculated by the sea squirt algorithm, and then the debris flow early warning results are output. After experimental analysis, the model can obtain rainfall surface data. After calculation by the model, the accuracy of the early warning probability of debris flow is improved, and the early warning result is consistent with the actual result of debris flow. Full article
(This article belongs to the Special Issue Flowing Mechanism of Debris Flow and Engineering Mitigation)
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17 pages, 2169 KiB  
Article
Risk Zoning Method of Potential Sudden Debris Flow Based on Deep Neural Network
by Qinglun Xiao, Shaoqi Wang, Na He and Filip Gurkalo
Water 2024, 16(4), 518; https://doi.org/10.3390/w16040518 - 6 Feb 2024
Viewed by 1059
Abstract
With the continuous increase in global climate change and human activities, the risk of sudden debris flow disasters is becoming increasingly severe. In order to effectively evaluate and zone the potential hazards of debris flows, this paper proposes a method for zoning the [...] Read more.
With the continuous increase in global climate change and human activities, the risk of sudden debris flow disasters is becoming increasingly severe. In order to effectively evaluate and zone the potential hazards of debris flows, this paper proposes a method for zoning the potential sudden hazards of debris flows based on deep neural networks. According to hazard identification, ten risk indicators of potential sudden debris flows are determined. The risk indicators of a potential sudden debris flow in each region were used as the input factors of a deep trust network (DBN) composed of a back propagation (BP) neural network and a restricted Boltzmann machine (RBM). The DBN is pre-trained using the contrast divergence method to obtain the optimal value of the parameter set of the DBN model, and a BP network is set at the last layer of the DBN for fine-tuning to make the network optimal. Using the DBN model with the best parameters, the risk probability of debris flows corresponding to each region is taken as an output. The risk grade is divided, the risk degree of potential sudden debris flow in each region is analyzed, and the potential sudden debris flow risk in each region is divided individually. The results show that this method can effectively complete the risk zoning of sudden debris flow. Moreover, the cumulative contribution of the indicators selected by this method is significant, and the correlation of indicators is not significant, which can play a role in the risk assessment of potential sudden debris flow. This study not only provides new ideas and methods for risk assessment of sudden debris flow disasters, but also fills a gap in the field of geological hazard susceptibility mapping. Full article
(This article belongs to the Special Issue Flowing Mechanism of Debris Flow and Engineering Mitigation)
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