Engineering Geology最新文献

Noise-resistant automatic seismic framework for monitoring rockslide slope 用于岩滑边坡监测的抗噪自动地震框架

IF 8.4 1区工程技术 Q1 ENGINEERING, GEOLOGICAL

Engineering Geology

Pub Date : 2026-03-26 Epub Date: 2026-02-01 DOI: 10.1016/j.enggeo.2026.108574

Chung-Ray Chu , Chiao-Yin Lu , Guan-Wei Lin , En-Jui Lee , Che-Hsin Liu , Chih-Hsin Chang , Hsiao-Yu Huang

National seismic networks provide high-quality data for monitoring large-scale landslides within tens of kilometers, but are difficult to detect small-scale slope failures due to signal attenuation. Denser local networks can enhance monitoring capabilities focusing on prone slopes that potentially cause fatalities and economic losses, but being close to human settlements introduces significant noise interference. This study developed a noise-resistant automatic algorithm including three stages: detection, noise elimination, and classification, for a local seismic network deployed near villages to monitor an active rockslide slope. The main concept is to effectively filter diverse and abundant surrounding noise and purify the dataset before feeding it into the machine learning classifier. During a one-year examination period, 98.6% of non-target sections, including numerous calm ambiences and random noise, were filtered out by STA/LTA, signal-to-noise ratio, and cross-correlation in the detection and noise elimination stages. As a result, the remaining dataset primarily consisted of earthquake and rockslide signals in approximately a 5:1 ratio, with only a few vehicle passages and random noise. This denoised dataset was subsequently used to train a Random Forest classifier with two attribute clusters, achieving good recall rates of 78% for rockslides and 99% for earthquakes. However, approximately 20% of manually labeled rockslides were misclassified as earthquakes due to their overlapping attribute ranges that cause certain distinctive attributes to resemble earthquake characteristics. This study establishes an applicable framework for monitoring slope hazards near vulnerable villages, demonstrating that effective noise filtering can significantly improve the reliability of classification in seismic monitoring implemented in high-noise environments.

国家地震台网为监测几十公里范围内的大规模滑坡提供了高质量的数据，但由于信号衰减，难以检测到小规模的边坡破坏。密集的本地网络可以增强监测能力，重点关注可能造成死亡和经济损失的倾斜斜坡，但靠近人类住区会带来严重的噪音干扰。本研究开发了一种抗噪自动算法，包括三个阶段：检测、噪声消除和分类，用于部署在村庄附近的当地地震网监测活动的岩石滑坡。其主要概念是在将数据集输入机器学习分类器之前，有效地过滤各种丰富的周围噪声，并对数据集进行净化。在为期一年的检测期间，在检测和消噪阶段，通过STA/LTA、信噪比和相互关系过滤掉了98.6%的非目标剖面，包括大量的平静环境和随机噪声。因此，剩下的数据集主要由地震和岩石滑动信号组成，比例约为5:1，只有少数车辆通道和随机噪声。该去噪数据集随后用于训练具有两个属性聚类的随机森林分类器，对滑坡和地震的召回率分别达到78%和99%。然而，大约20%的人工标记的滑坡被错误地分类为地震，因为它们的属性范围重叠，导致某些独特的属性类似于地震特征。本研究建立了易损村庄附近边坡灾害监测的适用框架，表明在高噪声环境下进行有效的噪声滤波可以显著提高地震监测分类的可靠性。

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引用次数: 0

STGCN-based inversion of landslide creep parameters using GNSS displacement time series 基于stgcn的GNSS位移时间序列滑坡蠕变参数反演

IF 8.4 1区工程技术 Q1 ENGINEERING, GEOLOGICAL

Engineering Geology

Pub Date : 2026-03-26 Epub Date: 2026-01-30 DOI: 10.1016/j.enggeo.2026.108602

Duo Wang , Qin Zhang , Guanwen Huang , Yuan Du

The highly nonlinear and spatiotemporal nature of landslide deformation poses significant challenges to the accurate estimation of landslide creep parameters. This study proposes a novel GNSS-based method to invert landslide creep parameters, integrating both spatial and temporal characteristics. First, the Burgers creep constitutive model is employed to describe the time-dependent deformation behavior of the landslide. Next, an orthogonal experimental design is used to conduct numerical creep simulations and generate synthetic displacement time series for model training. Based on these data, a Spatiotemporal Graph Convolutional Network (STGCN) is constructed to capture both spatial correlations and temporal dynamics. Finally, the inverted parameters are validated through forward numerical simulations. The case study results indicate that the Burgers creep constitutive model effectively reproduces nonlinear creep behavior and captures the spatial evolution of deformation. The simulated results show close agreement with the monitored displacements, yielding an average Mean Absolute Error (MAE) of 0.010 m. Compared with the traditional back-propagation neural network (BPNN), the STGCN reduces the MAE by 54.5%, thereby confirming the reliability of the proposed method. The results demonstrate that this approach provides a powerful tool for simulating the spatiotemporal evolution of landslides.

滑坡变形的高度非线性和时空性对滑坡蠕变参数的准确估计提出了重大挑战。本文提出了一种基于gnss的综合时空特征的滑坡蠕变参数反演方法。首先，采用Burgers蠕变本构模型描述滑坡随时间变化的变形行为。其次，采用正交试验设计进行蠕变数值模拟，生成合成位移时间序列进行模型训练。基于这些数据，构建了一个时空图卷积网络（STGCN）来捕捉空间相关性和时间动态。最后，通过正演数值模拟对反演参数进行了验证。算例研究结果表明，Burgers蠕变本构模型能有效地再现非线性蠕变行为，并能捕捉变形的空间演化。模拟结果与实测位移接近，平均绝对误差（MAE）为0.010 m。与传统的反向传播神经网络（BPNN）相比，STGCN将MAE降低了54.5%，从而验证了所提方法的可靠性。结果表明，该方法为模拟滑坡时空演化提供了有力的工具。

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引用次数: 0

An innovative transform mapping and visualization of fracture persistence from borehole-group image analysis: MFPbia 基于井眼群图像分析的裂缝持续性创新变换映射与可视化：MFPbia

IF 8.4 1区工程技术 Q1 ENGINEERING, GEOLOGICAL

Engineering Geology

Pub Date : 2026-03-26 Epub Date: 2026-01-23 DOI: 10.1016/j.enggeo.2026.108583

Zhenhao Xu , Yihui Li , Dongdong Pan , Shengzhe Zhao

Fracture persistence is a key parameter for evaluating the geological stability in unexcavated underground sections. It governs potential slurry migration pathways and strongly affects the overall efficiency of grouting. We propose an automated workflow for mapping fracture persistence. First, fracture plane attributes—dip azimuth, dip angle, spatial location, and dispersion—are digitally quantified from borehole data to construct a standardized database. Large-scale fracture occurrences are extracted using these persistence criteria. This provides crucial data on the maximum chord length of the fracture and the corresponding convex polygonal area. Furthermore, intelligent algorithms for persistence judgment and feature extraction are developed, enabling efficient analysis of borehole-induced fractures in tunnels. Numerical simulations spanning diverse borehole and fracture configurations confirm feasibility and demonstrate utility for 3-D visualization and fracture modeling. Additionally, the proposed method has been successfully applied in an oil depot project. This showcases its ability to swiftly and accurately determine the persistence of multiple fracture surfaces. The large-scale fracture information derived from this method offers valuable insights for ensuring the safety of tunnel construction.

裂缝持续性是评价地下未开挖段地质稳定性的关键参数。它控制着潜在的浆液迁移路径，强烈影响注浆的整体效率。我们提出了一种自动绘制裂缝持续性的工作流程。首先，从井眼数据中对裂缝平面属性（倾角、倾角、空间位置和分散度）进行数字化量化，构建标准化数据库。使用这些持续性标准提取大规模裂缝发生。这为骨折的最大弦长和相应的凸多边形面积提供了关键数据。此外，还开发了用于持续性判断和特征提取的智能算法，实现了对隧道井眼裂缝的有效分析。跨越不同井眼和裂缝配置的数值模拟证实了三维可视化和裂缝建模的可行性和实用性。该方法已成功应用于某油库工程。这显示了它能够快速准确地确定多个裂缝表面的持久性。该方法获得的大尺度裂缝信息为确保隧道施工安全提供了有价值的见解。

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引用次数: 0

Strain localization in rock: From multi-scale measurement to AI-driven prediction 岩石应变局部化：从多尺度测量到人工智能驱动预测

IF 8.4 1区工程技术 Q1 ENGINEERING, GEOLOGICAL

Engineering Geology

Pub Date : 2026-03-26 Epub Date: 2026-01-27 DOI: 10.1016/j.enggeo.2026.108594

Shijiao Yang , Qing Du , Jianchang Yan , Wenhua Liu , Jiancheng Huang , Danli Li

Strain localization, spanning from microscopic mineral fabrics to crustal-scale fault zones, fundamentally controls failure modes in natural geological systems and rock engineering. While individual measurement and modeling techniques have advanced significantly, an integrated framework bridging these approaches remains lacking. This review systematically synthesizes multi-scale measurement technologies, numerical simulation methods, and AI-driven prediction approaches for rock strain localization. Contact-based techniques including strain gauges, LVDT, distributed optical fiber sensing, and acoustic emission are examined alongside non-contact optical methods such as digital image correlation and X-ray computed tomography. Continuum and discontinuum numerical frameworks are compared, and AI methodologies from conventional machine learning to physics-informed neural networks are evaluated, with adaptability analysis for different monitoring data types. Three critical insights emerge: (1) multi-source data fusion is essential under geological heterogeneity; (2) physics-based constraints ensure data-driven model reliability; and (3) a gap persists between post-failure analysis and predictive capability. These findings inform rockburst warning, tunnel support design, slope stability assessment, and reservoir management. This review provides a framework for advancing from phenomenological description to mechanistic prediction and from laboratory understanding to engineering geological application.

应变局部化，从微观矿物组构到地壳尺度的断裂带，从根本上控制着自然地质系统和岩石工程的破坏模式。虽然单独的测量和建模技术有了显著的进步，但仍然缺乏一个连接这些方法的集成框架。本文系统地综合了岩石应变局部化的多尺度测量技术、数值模拟方法和人工智能驱动的预测方法。基于接触的技术，包括应变计、LVDT、分布式光纤传感和声发射，以及非接触光学方法，如数字图像相关和x射线计算机断层扫描。对连续统和非连续统数值框架进行了比较，并评估了从传统机器学习到物理信息神经网络的人工智能方法，并对不同监测数据类型进行了适应性分析。主要有三个方面：(1)在地质非均质性条件下，多源数据融合至关重要；(2)基于物理的约束保证了数据驱动模型的可靠性；(3)故障后分析与预测能力之间存在差距。这些发现为岩爆预警、隧道支护设计、边坡稳定性评估和水库管理提供了依据。这一综述为从现象学描述到机理预测，从实验室认识到工程地质应用提供了框架。

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引用次数: 0

Scale-dependent connectivity behavior in multi-clustered fracture systems 多簇裂缝系统中尺度相关的连通性

IF 8.4 1区工程技术 Q1 ENGINEERING, GEOLOGICAL

Engineering Geology

Pub Date : 2026-03-26 Epub Date: 2026-01-29 DOI: 10.1016/j.enggeo.2026.108597

Weiwei Zhu , Shengwen Qi , Xupeng He , Bowen Zheng , Songfeng Guo , Yu Zou , Wenhai Lei , Wang Zhang , Hussein Hoteit , Moran Wang , Manchao He , Wenjiao Xiao

Fracture network connectivity fundamentally controls subsurface fluid flow and rock mass behavior across spatial scales, yet determining the representative elementary volume (REV) remains a core challenge in geological system characterization. This study investigates scale-dependent connectivity through systematic analysis of natural outcrop data and artificial discrete fracture networks (DFNs). We implement a novel connectivity metric,

C_{t}

, integrating both intra-cluster connectivity and inter-cluster interactions, and propose the Standard Deviation Stability Criterion (SDSC) for objective REV determination using second-order statistical measures. Analysis of 63 natural outcrop maps and various artificial DFN configurations reveals several key findings. First, fracture network connectivity exhibits pronounced scale-dependence with REV values approaching the same order of magnitude as the investigated systems, with mean REV values of 0.586 for natural outcrops and exceeding 0.2 for artificial networks. Second, preferential orientations increase REV requirements, particularly under stress conditions where only critically stressed fractures remain permeable, with fracture clustering further amplifying this effect. Third, in-situ stress conditions substantially increase REV requirements, with values nearly doubling when only critically stressed fractures remain active. Complete sealing creates the most challenging REV determination due to orientation selectivity, while partial sealing provides intermediate behavior by preserving orientation diversity. These findings demonstrate that obtaining representative volumes through conventional sampling presents fundamental limitations and provide critical insights for enhancing predictive models in subsurface engineering and environmental applications.

裂缝网络连通性从根本上控制着地下流体流动和岩体在空间尺度上的行为，但确定代表性基本体积（REV）仍然是地质系统表征的核心挑战。本研究通过系统分析天然露头数据和人工离散裂缝网络（DFNs）来研究尺度相关连通性。我们实现了一个新的连通性度量，Ct，整合了集群内连通性和集群间的相互作用，并提出了标准差稳定性准则（SDSC），用于使用二阶统计度量客观确定REV。对63张自然露头图和各种人工DFN配置的分析揭示了几个关键发现。首先，裂缝网络连通性表现出明显的尺度依赖性，REV值与所研究的系统接近同一数量级，自然露头的平均REV值为0.586，人工网络的平均REV值超过0.2。其次，优先定向会增加REV要求，特别是在应力条件下，只有临界应力裂缝仍然具有渗透性，而裂缝聚集进一步放大了这种影响。第三，地应力条件大大增加了REV要求，当只有临界应力裂缝仍然活跃时，REV要求几乎翻了一番。由于定向选择性，完全密封是最具挑战性的REV测定方法，而部分密封通过保持定向多样性提供了中间行为。这些发现表明，通过常规采样获得具有代表性的体积存在根本性的局限性，并为增强地下工程和环境应用中的预测模型提供了关键见解。

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引用次数: 0

Symbolic regression-based prediction of coefficient of permeability for granular soils 基于符号回归的颗粒土渗透系数预测

IF 8.4 1区工程技术 Q1 ENGINEERING, GEOLOGICAL

Engineering Geology

Pub Date : 2026-03-26 Epub Date: 2026-01-27 DOI: 10.1016/j.enggeo.2026.108593

Yerim Yang , Hangseok Choi , Younseo Kim , Kibeom Kwon

Predicting the coefficient of permeability in granular soils is critical for effective groundwater flow analysis. However, existing predictive models are often constrained by limited datasets and a lack of interpretable formulations. This study developed a predictive formula for the coefficient of permeability in saturated granular soils using symbolic regression applied to a large-scale global database (CG/KSAT/7/1278) comprising 1278 samples. Exploratory data analysis identified both individual and combined effects of grain size and volumetric state parameters on soil permeability, guiding the selection of key predictors. Symbolic regression systematically explored functional forms and optimized coefficients, resulting in a closed-form expression based solely on grain size parameters. Compared with ten existing models, the proposed formula achieved superior predictive performance, including the lowest mean absolute error of 0.419. Its predictive stability was further demonstrated by minimal and balanced over- and under-predictions across the entire permeability range. External validation using an independent dataset and laboratory permeability tests confirmed its generalizability. In conclusion, this study presents a generalized and interpretable formula that advances the understanding of flow behavior and improves practical permeability estimation in granular soils.

预测颗粒土的渗透系数是有效分析地下水流的关键。然而，现有的预测模型往往受到有限的数据集和缺乏可解释的公式的限制。本研究将符号回归应用于包含1278个样本的大型全球数据库（CG/KSAT/7/1278），建立了饱和颗粒土渗透系数的预测公式。探索性数据分析确定了粒度和体积状态参数对土壤渗透性的单独和联合影响，指导了关键预测因子的选择。符号回归系统地探索了函数形式，优化了系数，得到了一个仅基于粒度参数的封闭式表达式。与已有的10个模型相比，本文提出的模型具有较好的预测性能，其中平均绝对误差最低，为0.419。在整个渗透率范围内，其预测的稳定性进一步得到了最小和平衡的高估和低估。使用独立数据集和实验室渗透率测试的外部验证证实了其普遍性。总之，本研究提出了一个广义和可解释的公式，促进了对颗粒土流动特性的理解，并改善了实际渗透率估算。

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引用次数: 0

Seismic response and failure mechanism of pile foundations at different relative positions and rock-socketed depths on deep deposit slopes 深埋边坡不同相对位置和嵌岩深度桩基地震反应及破坏机理

IF 8.4 1区工程技术 Q1 ENGINEERING, GEOLOGICAL

Engineering Geology

Pub Date : 2026-03-26 Epub Date: 2026-02-02 DOI: 10.1016/j.enggeo.2026.108604

Zhongping Yang , Shunbo Zhang , Hua Liu , Miao Liu , Yonghua Li , Qingqiang Guan

In southwestern China, rock-socketed piles are extensively utilised on deep deposit slopes (DDPs), where numerous deposit landslides and the deformation and failure of pile foundations have occurred following earthquakes. This research conducted a series of shaking table tests to assess the impact of seismic action on the seismic response and failure mechanisms of pile foundations at different relative positions (λ) and rock-socketed depths (RSDs) on DDPs. The findings of this study indicate that the nonlinear characteristics of the deposits significantly impact the dynamic properties and acceleration response of both the deposits and the pile foundations, resulting in fluctuations. Pile foundations and deposits near the slope surface experience vibration phase differences and relative motion, resulting in a failure mode characterised by shallow sliding, which notably increases the strain and bending moments in the upper sections of the pile foundations. Both λ and RSD influence the seismic response, deformation, and forces of pile foundations via inertial effects and pile-deposit interaction. The seismic response of the pile (λ = 0) is the most pronounced, whereas the pile-deposit interaction (λ = 1) is the most intense (particularly at depths ranging from 2D to 5D). Contrastingly, the RSD can effectively mitigate these effects on both deposits and pile foundations, and this inhibitory effect is particularly significant for pile foundations with depths exceeding 10D. Based on these findings, this research provides several recommendations concerning the seismic design of pile foundations at different λ and RSDs on DDPs.

在中国西南地区，嵌岩桩被广泛应用于深部沉积物边坡（ddp），这些边坡在地震后发生了大量的沉积物滑坡和桩基变形破坏。本研究通过一系列振动台试验，评估地震作用对不同相对位置（λ）和嵌岩深度（rsd）桩基地震反应的影响及破坏机制。研究结果表明，沉积物的非线性特性对沉积物和桩基的动力特性和加速度响应都有显著影响，产生波动。靠近坡面的桩基和沉积物经历振动相位差和相对运动，形成以浅滑动为特征的破坏模式，显著增加了桩基上部的应变和弯矩。λ和RSD均通过惯性效应和桩-沉积物相互作用影响桩基的地震反应、变形和力。桩的地震反应（λ = 0）最为明显，而桩-沉积物相互作用（λ = 1）最为强烈（特别是在2D至5D深度范围内）。相比之下，RSD对沉积物和桩基均能有效缓解这些影响，且对于深度超过10D的桩基，这种抑制作用尤为显著。基于这些发现，本研究对ddp上不同λ和rsd的桩基抗震设计提出了几点建议。

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引用次数: 0

Effects of grain size on landslide–forest interaction 粒径对滑坡-森林相互作用的影响

IF 8.4 1区工程技术 Q1 ENGINEERING, GEOLOGICAL

Engineering Geology

Pub Date : 2026-03-26 Epub Date: 2026-02-09 DOI: 10.1016/j.enggeo.2026.108613

Haiming Liu, Andrea Pasqua, Hannah Nichols, Alessandro Leonardi

Forests play an essential but poorly-understood role in mitigating landslide runout by providing mechanical resistance and dissipating flow energy. Despite growing interest, existing models treat forests as friction modifiers or generic porous obstacles, and largely ignore how grain size controls retention and jamming. This study experimentally investigates and resolves the influence of grain size, slope angle, and tree spacing on landslide–forest interactions using reduced-scale flume tests with different granular materials. Results show that although forests can reduce flow mobility, preferential flow paths may develop along trees, leading to ineffective energy dissipation along the flow flanks. For fine-grained flows composed of sand, the deposition behaviour is governed by the normalised slope angle and the transverse blockage ratio. For coarse-grained flows composed of gravel, the deposition and retention are controlled by two distinct jamming mechanisms: frontal deposit-induced jamming and arching-induced jamming. Frontal deposit-induced jamming occurs in all jamming cases, whereas arching-induced jamming only develops when tree spacing is smaller than three times of the grain size. We capture this variety of phenomena within two phase diagrams for fine-grained and coarse-grained flows. The phase diagrams provide a direct screening rule for minimum tree density and slope condition required to ensure jamming for a given grain size distribution.

森林通过提供机械阻力和耗散水流能量，在减轻滑坡冲击方面发挥了重要但鲜为人知的作用。尽管人们越来越感兴趣，但现有的模型将森林视为摩擦调节剂或一般的多孔障碍，并且在很大程度上忽略了粒度如何控制保留和阻塞。本研究利用不同颗粒材料的小尺度水槽试验，实验研究并解决了粒度、坡角和树木间距对滑坡-森林相互作用的影响。结果表明，虽然森林可以降低流动的流动性，但沿着树木可能形成优先的流动路径，导致沿流侧的能量耗散无效。对于由砂组成的细粒流，沉积行为受归一化坡角和横向堵塞比的支配。对于由砾石组成的粗粒流，其沉积和滞留受两种截然不同的堵塞机制控制：前缘沉积诱发堵塞和拱状诱发堵塞。锋面淤积诱发的淤积在所有淤积情况下都有发生，而弓形诱发的淤积只在树间距小于3倍粒径时发生。我们在细粒度流和粗粒度流的两个相图中捕捉到这种现象的多样性。相图提供了一个直接的筛选规则，以确保最小树密度和斜坡条件，以确保干扰给定的粒度分布。

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引用次数: 0

Investigating the catastrophe mechanism and evolution of anti-frost subgrade in high-speed railways under extreme climatic events 研究极端气候事件下高速铁路防冻路基的突变机制及演变

IF 8.4 1区工程技术 Q1 ENGINEERING, GEOLOGICAL

Engineering Geology

Pub Date : 2026-03-26 Epub Date: 2026-01-29 DOI: 10.1016/j.enggeo.2026.108596

Bowen Tai , Zurun Yue , Pengcheng Wang , Jingpeng Liu

The exacerbation of frost damage in subgrade structures of high-speed railways (HSR) in cold regions, often triggered by extreme climatic events such as severe cold spells, heavy snowfall, and intense rainfall infiltration. To ensure the operational integrity of HSR in seasonally frozen soil regions, it is imperative to investigate the impacts of extreme climate conditions on the stability of typical anti-frost subgrades. This study employs an integrated methodology combining field monitoring, model development, numerical simulations, and theoretical analysis. First, the differential influences of various climatic scenarios on the hydrothermal behavior of seasonally frozen soil are examined. Subsequently, the coupled water-heat-deformation characteristics of a standard anti-frost subgrade structure are analyzed, leading to the development of a novel fully coupled water-heat-strain model. Finally, the model is utilized to predict and assess the structural stability under extreme climate events. Key findings include: (1) marked differential responses in the hydrothermal regime of seasonally frozen soil under varying climate conditions; (2) a time-lag in variations of temperature and moisture with increasing depth; (3) synergistic effects of compound extreme weather events significantly aggravate subgrade damage; and (4) the necessity of holistic consideration of extreme climate, engineering geological conditions and slope effect in the optimal design of anti-frost layers. These insights not only advance the mechanistic understanding of frost deformation processes under extreme climate, but also provide valuable guidelines for the optimized design of anti-frost infrastructures in cold regions.

严寒地区高速铁路路基结构冻损的加剧，往往是由严寒、暴雪、强降雨入渗等极端气候事件引发的。为保证季节性冻土区高铁的运行完整性，研究极端气候条件对典型抗冻路基稳定性的影响是十分必要的。本研究采用现场监测、模型开发、数值模拟和理论分析相结合的综合方法。首先，研究了不同气候情景对季节性冻土热液行为的差异影响。在此基础上，分析了标准防冻路基结构的水-热-变形耦合特性，建立了一种新型的全耦合水-热-应变模型。最后，利用该模型对极端气候事件下的结构稳定性进行预测和评估。主要发现包括：(1)不同气候条件下季节冻土热液状态的显著差异响应；(2)温度和湿度随深度的变化存在时滞；(3)复合极端天气事件的协同效应显著加重路基破坏；(4)防冻层优化设计必须综合考虑极端气候、工程地质条件和边坡效应。这些发现不仅促进了对极端气候条件下霜变形过程的机理认识，而且为寒区防冻基础设施的优化设计提供了有价值的指导。

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引用次数: 0

Probabilistic models in rock slope kinematic analysis employing the reliability engineering approaches and considering the variability of rock joint orientations 采用可靠度工程方法并考虑岩体节理方向变异性的岩质边坡运动学分析中的概率模型

IF 8.4 1区工程技术 Q1 ENGINEERING, GEOLOGICAL

Engineering Geology

Pub Date : 2026-03-26 Epub Date: 2026-02-03 DOI: 10.1016/j.enggeo.2026.108592

Ibnu Rusydy , Ghislain Bournival , Ismet Canbulat , Chengguo Zhang

The variability of rock joint orientations significantly influences the type of slope failure, making probability methods crucial in kinematic analysis. This study aims to integrate probability kinematic analysis with reliability engineering methodologies like reliability block diagrams (RBD), event tree analysis (ETA), and fault tree analysis (FTA) to assess slope stability under joint orientation uncertainty. This study also examines the effect of different total friction angles (Φ) and lateral limit angles (γ_lim) using the response surface methodology (RSM). The linear and circular goodness-of-fit tests determine the statistical distribution, allowing 100,000 random joint orientation values to be generated using Latin hypercube sampling (LHS). Results revealed that all three engineering reliability approaches yielded consistent output when integrated with probabilistic kinematic analysis. The probabilistic kinematic analysis and FTA methods analyses failure systems and effectively estimate the probability of occurrence. Whilst RBD evaluates successful systems and reliability. ETA offers both probabilities and is easier to implement, making it suitable for future applications. The RSM shows that the probability of occurrence increases when Φ is lower and γ_lim is high, concluding that selecting the appropriate Φ is crucial for determining the probability of occurrence. However, in wedge failure, the regression coefficient (β₂) ranges from 2 × 10⁻¹⁷ to 0.0043 for γ_lim between 80° and 90°, indicating a low effect on the probability of occurrence.

岩石节理方向的可变性显著影响边坡破坏类型，使得概率方法在运动学分析中至关重要。本研究旨在将概率运动学分析与可靠性方框图（RBD）、事件树分析（ETA）和故障树分析（FTA）等可靠性工程方法相结合，以评估节理方向不确定性下的边坡稳定性。本研究还使用响应面法（RSM）检验了不同的总摩擦角（Φ）和侧向极限角（γlim）的影响。线性和圆形拟合优度测试确定统计分布，允许使用拉丁超立方体抽样（LHS）生成100,000个随机关节方向值。结果表明，当与概率运动学分析相结合时，所有三种工程可靠性方法都产生了一致的输出。概率运动分析和自由贸易区方法分析了失效系统，有效地估计了发生的概率。而RBD则评估成功的系统和可靠性。ETA提供了两种可能性，并且更容易实现，使其适合未来的应用。RSM表明，当Φ较低，γlim较高时，发生概率增大，因此选择合适的Φ对于确定发生概率至关重要。然而，在楔形破坏中，γlim在80°和90°之间，回归系数（β 2）范围为2 × 10−17 ~ 0.0043，表明对发生概率的影响很小。

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引用次数: 0