Safety assessment method of steel protective structure against large-scale debris flow

Kokuryo, Hiroshi; Horiguchi, Toshiyuki; Ishikawa, Nobuyoshi

doi:10.1177/20414196211059552

Cited by 3 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to hazard identification, the risk index of a potential sudden debris flow is determined. The risk of debris flow refers to the possibility of a disaster-causing geological process [11], and the core is the activity degree of such a process. Based on the qualitative analysis, the higher the activity degree of geological processes causing disasters, the greater the danger, resulting in more serious disaster losses.…”

Section: Risk Assessment Index System Of Potential Sudden Debris Flowmentioning

confidence: 99%

Risk Zoning Method of Potential Sudden Debris Flow Based on Deep Neural Network

Xiao,

Wang,

et al. 2024

Water

View full text Add to dashboard Cite

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.

show abstract

Section: Risk Assessment Index System Of Potential Sudden Debris Flowmentioning

confidence: 99%

Risk Zoning Method of Potential Sudden Debris Flow Based on Deep Neural Network

Xiao,

Wang,

et al. 2024

Water

View full text Add to dashboard Cite

show abstract

“…Baselt et al [9] focused on the study of runoff distance, fluidity and inundated area based on the experiment on massive stony debris flow. Kokuryo et al [10] studied the safety evaluation methods of steel protection structure against massive debris flow.…”

Section: Introductionmentioning

confidence: 99%

A study of control scheme of debris flows and geological disasters in the Shiwei river basin

2024

Archives of Civil Engineering

View full text Add to dashboard Cite

The basic characteristics of debris flows in the Shiwei river basin are summarized through the field investigation on debris flows in the Shiwei river basin and analysis on formation conditions of debris flows from three aspects, i.e. geological environment, geological structure and neotectonic movement, as well as seismic action. Based on this, the stability of landslide in the Shiwei river basin is analyzed and calculated, and the stability coefficient of landslide is obtained. The debris flows in the Shiwei river basin will directly damage and threaten the county town, while other geological disasters such as landslide, collapse, slope sliding & collapse and potentially unstable slopes will indirectly damage and threaten the county town. The landslide form is clear, and the landslide stability calculation shows that the landslide body is generally stable – basically stable, but partially unstable – less stable. The “blocking + discharging” comprehensive control scheme is proposed according to the formation conditions and development characteristics of debris flows in the Shiwei river basin, and the study findings can be used as a reference for similar projects.

show abstract

Evaluating the Performance of Protective Barriers against Debris Flows Using Coupled Eulerian Lagrangian and Finite Element Analyses

Sha,

Dyson,

Kefayati

et al. 2024

Sustainability

View full text Add to dashboard Cite

Protective structures are critical in mitigating the dangers posed by debris flows. However, evaluating their performance remains a challenge, especially considering boulder transport in complex 3D terrains. This study introduces a comprehensive methodology to appraise the effectiveness of protective structures under the impact of debris flows for real-world conditions along the Hobart Rivulet in Tasmania, Australia. The validation of the Coupled Eulerian-Lagrangian (CEL) model against experimental data demonstrates its high accuracy in predicting flow dynamics and impact forces, whereby flow velocities are estimated for subsequent Finite Element (FE) analyses. By simulating boulder-barrier interactions, weak points in I-beam post barriers are identified, with a broad investigation of the effects on the barrier performance under various conditions. The establishment of a 3D CEL model to assess the interactions between debris flow, boulders, and I-beam post barriers in a complex rivulet terrain is of particular significance. Through CEL and FE analyses, various aspects of debris flow-structure interactions are presented, including structural failure, impact force, and boulder velocity. The findings provide insights into the suitability of various numerical methods to assess the performance of protective measures in real-world scenarios.

show abstract

Safety assessment method of steel protective structure against large-scale debris flow

Cited by 3 publications

References 13 publications

Risk Zoning Method of Potential Sudden Debris Flow Based on Deep Neural Network

Risk Zoning Method of Potential Sudden Debris Flow Based on Deep Neural Network

A study of control scheme of debris flows and geological disasters in the Shiwei river basin

Evaluating the Performance of Protective Barriers against Debris Flows Using Coupled Eulerian Lagrangian and Finite Element Analyses

Contact Info

Product

Resources

About