Scientific challenges in disaster risk reduction for the Sichuan–Tibet Railway

Cui, Peng; Ge, Yong; Li, Shaojun; Li, Zhenhong; Xu, Xiwei; Zhou, Guomei; Chen, Hong‐Yuan; Wang, Hao; Lei, Yu; Zhou, Libo; Yi, Shujian; Wu, Chunhao; Guo, Junbo; Wang, Qi; Lan, Hengxing; Ding, Mingtao; Ren, Junjie; Zeng, Longfei; Jiang, Yuanjun; Wang, Yan

doi:10.1016/j.enggeo.2022.106837

Cited by 97 publications

(25 citation statements)

References 156 publications

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“…It should be noted that the hydraulic gradients utilized in this study might be slightly higher than those typically observed in general natural groundwater systems. Nevertheless, the hydraulic gradient levels applied in this study may be plausible in cold alpine environments (e.g., QTP) and under extreme contrasts in hydraulic conductivity (Cui et al., 2022; Meyer et al., 2014; Neupane et al., 2019). Moreover, hydraulic gradients exceeding 1 are not uncommon in laboratory experiments (Deng et al., 2023; Okeke & Wang, 2016; Prasomsri & Takahashi, 2021).…”

Section: Methodsmentioning

confidence: 94%

Characterization of Thermal‐Hydraulic Coupling Behavior for Moraine Soil With Ice Inclusions in a Warming Environment

Zhou,

Zhu,

et al. 2023

Water Resources Research

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A warming environment is promoting the thawing process in ice‐rich moraine soils in cold alpine regions, thereby accelerating the formation/development of geohazards in these regions. The mechanism of these geohazards is closely related to the thermal‐hydraulic (TH) coupling behavior of moraine soils, which has rarely been investigated. Here, we conducted a series of bench‐scale TH coupling simulations of moraine soils with ice inclusions in a warming environment. The numerical model was conceptualized from a field survey on the Qinghai‐Tibet Plateau, which consists of a porous medium embedded with multilayer ice cubes with warmer water continuously flowing through it. A TH coupling framework considering ice‐water phase change was established to implement this coupled simulation with different initial ice contents and hydraulic gradients. The simulated results showed phased and nonlinear evolutionary features of temperature, ice content, and permeability for the moraine soil system. Further analyses showed that these complex evolutionary features can be reproduced by empirical formulas, with concise expressions following dimensional consistency and some coefficients having potential physical meanings. The discriminant models of two equilibrium states were finally developed to predict the critical times of structure stabilization and thermal equilibrium of the moraine soil system. This study presents the complex evolution of TH coupling properties of moraine soils, and simultaneously demonstrates that these properties can be predicted and characterized.

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Section: Methodsmentioning

confidence: 94%

Characterization of Thermal‐Hydraulic Coupling Behavior for Moraine Soil With Ice Inclusions in a Warming Environment

Zhou,

Zhu,

et al. 2023

Water Resources Research

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“…Guided Machine Learning Algorithms in Rock Strength Prediction: A Novel Method for Evaluating Dynamic Compressive Strength of Rocks Under Freeze-Thaw Cycles by the national grand strategy, transportation construction is progressively advancing into high-altitude and high-latitude cold regions [3][4][5] . Typical projects in these cold regions include major national construction projects such as the Sichuan Tibet Railway, Yunnan Tibet Railway, and New Tibet Railway [6][7][8] , which are the pillars of human activities in remote areas [9][10][11] . However, in high-altitude and cold regions, rock masses often contain a large number of initial defects, such as joints and cracks [12,13] .…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms in Rock Strength Prediction: A Novel Method for Evaluating Dynamic Compressive Strength of Rocks Under Freeze-Thaw Cycles

Lv,

Zhang,

Zhang

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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The combined impact of freeze-thaw cycles and dynamic loads significantly influences the long-term durability of rock engineering in high-cold regions. Consequently, investigating the dynamic compressive strength (DCS) of rocks subjected to freeze-thaw cycles has emerged as a crucial area of scientific research to advance rock engineering construction in cold regions. Presently, the determination of the DCS of rocks under freeze-thaw cycles primarily relies on indoor experiments. However, this approach has faced criticism due to its drawbacks, including prolonged duration, high costs, and reliance on rock samples. To address these limitations, the exploration of using artificial intelligence technology to develop more accurate and convenient DCS prediction models for rocks under freeze-thaw cycles is a promising attempt. In this context, this paper introduces a DCS prediction model for rocks under freeze-thaw cycles, which integrates the Sparrow Search Algorithm (SSA) with Random Forest (RF). Firstly, employing a dataset of 216 samples, Principal Component Analysis (PCA) is utilized to reduce the dimensionality of ten influential factors. Subsequently, five optimization algorithms are employed to optimize the hyperparameters of both the BP and RF algorithms. Finally, a comprehensive evaluation and comparative analysis are carried out to assess the predictive performance of the optimized model, using evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2).The research findings demonstrate that the SSA-RF model exhibits the best predictive performance, surpassing the other nine models in terms of generalization. The prediction model proposed in this study has good applicability for predicting DCS of freeze-thaw rock in cold regions, and also provides new ideas for the combination of machine learning and rock mass engineering in cold regions.

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“…Under the superimposed amplification effect of extreme rainfall and earthquake, the slopes may slip and form landslides. The normal operation of HSR is significantly impacted by these landslides, which pose a serious threat to the life and property along lines (Cui et al, 2022; Liu et al, 2018). However, relatively little attention has been given to the early detection and monitoring of potential landslides along HSR lines.…”

Section: Introductionmentioning

confidence: 99%

Early detection of potential landslides along high‐speed railway lines: A pressing issue

Zhu,

Qiu,

Cui

et al. 2023

Earth Surf Processes Landf

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Early detection of landslides is important for prevention and mitigation of landslide disasters. Especially, accurately identifying potential landslides along high‐speed railway is becoming a pressing issue for operation safety of high‐speed railway. Here, 161 Sentinel‐1A satellite images from March 2017 to September 2022 were acquired to detect potential landslides along the Lanzhou‐Urumqi high‐speed railway (LUHR) in Qinghai. Results show that rainfall is the main cause for the transition from secondary creep (steady state) to tertiary creep (accelerated) at the Jiujiawan landslide. The hot spot analysis method was used to cluster the average line‐of‐sight deformation velocity along the LUHR, effectively extracting five deformation zones that may pose a threat. Optical satellite image analysis revealed four potential landslides and one unstable slope. Furthermore, the rescaled range analysis documented that the Hurst exponent is greater than 0.8 for all four potential landslide instability points, implying that the identified potential landslide areas are still in motion and may pose a threat to the operation of the LUHR. On this basis, we propose a framework for early identification and monitoring of landslides along the high‐speed railway lines that supports landslide hazard managing with the goal to reduce landslide risk.

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Scientific challenges in disaster risk reduction for the Sichuan–Tibet Railway

Cited by 97 publications

References 156 publications

Characterization of Thermal‐Hydraulic Coupling Behavior for Moraine Soil With Ice Inclusions in a Warming Environment

Characterization of Thermal‐Hydraulic Coupling Behavior for Moraine Soil With Ice Inclusions in a Warming Environment

Machine Learning Algorithms in Rock Strength Prediction: A Novel Method for Evaluating Dynamic Compressive Strength of Rocks Under Freeze-Thaw Cycles

Early detection of potential landslides along high‐speed railway lines: A pressing issue

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