This study proposes a High-order Shape Functions-Based Karhunen-Lo & egrave;ve (HSFKL) method to efficiently and accurately simulate irregular, multi-dimensional geotechnical random fields. Unlike conventional KL expansions relying on global polynomials, HSFKL employs high-order shape functions as localised basis functions, naturally adapting to complex and irregular geometries, reducing expansion dimensionality, and facilitating subsequent reliability analysis. By combining Lagrange interpolation with Gauss quadrature and tensor-product techniques, the method converts multi-dimensional integral eigenvalue problems into tractable local element matrix operations, significantly improving computational efficiency. Numerical results indicate that the combination of Legendre shape functions and Serendipity elements is optimal, achieving a 23.2% reduction in expansion terms, a 44.0% decrease in computation time, and up to an 88.5% reduction in discretization error compared with other approaches. Large-scale 3D slope simulations confirm that discretization accuracy critically dictates failure probabilities and mechanisms. To balance precision with computational cost, a 1% covariance error threshold is recommended. Overall, HSFKL establishes a robust and scalable framework, enabling dimensionally efficient reliability analysis for geotechnical systems with significant spatial variability and complex geometries.

Deep soft soil is widely distributed in the southeastern coastal areas of China, posing challenges for conventional cement-soil mixing pile reinforcement owing to its limited effectiveness depth. To address this issue, lightweight technology has been proposed as an alternative for composite foundation treatment, leading to the development of lightweight cement-soil mixing pile composite foundations. This study aims to perform indoor model tests based on previous studies to investigate these aspects. The results indicate that the settlement of the lightweight cement-soil pile composite foundation under maximum loading is reduced by 42.2 % compared to the traditional cement-soil pile composite foundation, demonstrating a significant settlement reduction effect; The lightweight cement-soil piles exhibit notable advantages in optimizing stress distribution and are more suitable for foundation reinforcement in deep and thick soft soil areas. Therefore, lightweight cement-soil piles are more effective in soft soil regions where the pile tips cannot reach the bearing layer, providing a more efficient solution for foundation reinforcement in deep and thick soft soil areas.

In geotechnical engineering, precise probabilistic assessment of slope stability is crucial for risk management and the safe operation of engineering projects. To perform probabilistic assessments of slopes accurately and efficiently, six machine learning (ML) algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Extremely Randomized Tree (ERT), and Gradient Boosting Tree (GBT), are adopted to establish surrogate models for the relationship between a slope's safety factor and soil's shear parameters. Latin Hypercube Sampling (LHS) is employed to generate training samples for constructing surrogate models using ML algorithms. Adaptive Synthetic Sampling (ADASYN) is used to balance the number of samples of failure and safety classes by generating synthetic samples for the failure sample set, and a Genetic algorithm (GA) is used to optimize the hyper-parameters of ML and ADASYN algorithms to improve the accuracy of the surrogate models. Two criteria are proposed to measure the accuracy of surrogate models built using ML with Genetic-ADASYN algorithms, and a formula is presented to estimate the optimal number of samples for the training set. Based on the surrogate models, reliability indices and failure probabilities of slopes can be readily estimated using the Monte Carlo Simulation Method (MCSM). Case studies of five slopes with different complexities are adopted to illustrate the proposed method for the probabilistic analysis of slope stability and to compare the accuracy of surrogate models built using different ML algorithms. Results show that the Genetic-ADASYN algorithm can significantly improve the accuracy of surrogate models built using ML algorithms, and among the six ML algorithms, GBT is the best ML algorithm due to its generalizability and accuracy in slope stability prediction problems. The research findings can provide a reference for risk assessment of slope engineering and help to improve the accuracy and efficiency of probabilistic slope stability analysis.

Accurate assessment of pile group's performance in spatially variable unsaturated expansive soil has long been a challenge in geotechnical engineering. This paper presents a methodology to perform reliability analysis for vertically loaded pile group, where the modified load transfer method (LTM) is utilised to investigate the load-displacement response considering the pile-pile interaction and the non-linear relationship of the pile-soil interface under the influence of matric suction reduction and the swelling of expansive soil; the Karhunen-Loeve (KL) expansion method is adopted to simulate the spatial variability of soil parameters; the first-order reliability method (FORM) is utilised to perform reliability analysis of each pile in the pile group; and the reliability analysis of pile group is then performed using the sequential compounding method (SCM) by considering the pile group as a parallel system. By applying the proposed methodology to a 3 x 3 pile group under different vertical loads and infiltration times, the relative magnitudes of reliability indices for different piles in the pile group and the pile group system under two failure modes of uplifting and sinking are identified. The effects of soil's spatial variability and pile spacing on the reliability of pile group are also analysed.

To improve the computational efficiency and accuracy is a constant pursuit for evaluating the stability of geotechnical structures built in spatially variable soils. This study proposes an important-region-based Karhunen - Lo & egrave;ve expansion and first-order reliability method (KL-FORM-IR method) to perform reliability analysis for shallow foundation resting on spatially variable soil. The KL-FORM-IR method further improves the computational efficiency by ignoring the variability of the soil parameters outside the important region. An identification method for locating the important region of shallow foundation is first proposed, and the effect of the scale factor (Kf), autocorrelation distance of soil properties, and foundation width (B) on the identification of important regions are investigated. For the purpose of validation, the proposed KL-FORM-IR method compared with the KL-FORM and KL-MCSM by performing reliability analyses based on the same numerical model. Conclusively, the KL-FORM-IR method can improve the computational efficiency and it contributes a new approach to the reliability analysis of shallow foundation resting on a spatially variable soil.

Key parameters describing the spatial variability of soil properties based on the random field theory are the scale of fluctuation (SOF) and coefficient of variation (COV). To characterize the spatial variability of soil properties, reducing the impact of these errors and uncertainties is necessary. To accomplish this, for the five main layers of soil, we collected 18 cone penetration test (CPT) data from a highly heterogeneous region in Lianyungang New Airport, Jiangsu Province, China, and used the control variable method to analyze the influence of the estimation method of tendency, its function type and outliers. The results show that, compared with the ordinary least square method (OLSM), the least absolute deviation method (LADM) can more truly reflect the trend component of CPT parameters in the vertical direction, and the influence of other factors on SOF and COV is also studied, such as outliers and estimation functions of trend components. On this basis, a reasonable calculation process of SOF and COV is summarized, which provides a reference for the calculation of SOF and COV in vertical direction in the future. By comparing the SOF calculated by different models, the results show that the squared exponential (SQX) model has the highest SOF in 68.3% of the evaluation, and the single exponential (SNX) model has the lowest SOF in 64.4% of the evaluation. Moreover, we compared the SOF and COV of cone tip resistance (qc) and sleeve friction (fs), which showed that SOF of qc and COV of qc is lower than that of fs in 54.4 and 73.3% of all evaluations, respectively.

Probabilistic analysis has been widely used to assess the inherent uncertainty of variables in laterally loaded pile systems, but the calculation is still difficult and time-consuming. The present study presents an efficient probabilistic analysis framework for a laterally loaded pile system. The performance of the system is defined as the lateral deflection at the pile head and maximum bending moment of the pile shaft, corresponding to two failure modes. Within this framework, the spatial variability of the soil and the correlation between failure modes are considered by the random field theory and the First-Order Reliability Method, respectively. Moreover, the Sequential Compounding Method is used as an efficient tool to determine the system reliability indexes. The framework is confirmed by comparing the reliability indexes of failure modes and systems with those of the Monte Carlo Simulation Method. Furthermore, a parametric analysis and system sensitivity analysis are performed. The results show that the auto-correlation distance, allowable lateral displacement at the pile head, and allowable bending moment of the pile shaft have a great influence on reliability indexes of failure modes and system, and the major parameter of soil in affecting pile is the elastic modulus compared with the undrained shear strength.

The equivalent porous medium (EPM) method is an efficient approximation method for groundwater yield analysis considering the equivalent permeability in a fractured geologic medium (FGM). The EPM method is widely used in many practical hydrogeological problems from local to regional scales. However, when calculating water head and velocity distributions, the suitability of the EPM model remains insufficiently evaluated. The suitability refers to the head error caused by the application of the EPM model. The smaller the error, the better the suitability. In this study, the influence of fracture geometric attributes on the suitability was quantitatively studied in numerical simulation experiments, and the EPM model simulation results were compared to those obtained with the discrete fracture network (DFN) model. The results indicated that the suitability decreased with increasing fracture spacing. When the fracture spacing was smaller than 0.6 m, the influence of an increase in the fracture spacing on the suitability was obvious. For the same fracture spacing, the suitability generally increased with increasing trace length. When the spatial variability of fracture aperture is not considered, the change of the fracture aperture did not impact the suitability. The fracture orientation slightly impacted the suitability, which can be ignored. An evaluation standard based on fracture parameters is proposed to estimate the suitability of the EPM model, which provides a scientific basis to ascertain whether this model can be applied to a given site to solve the head-related hydrogeological issues with FGMs.

The teaching for geotechnical reliability analysis to graduate students is difficult because the concept is abstract and the methods are complicated. Reasons for adopting case-based teaching method for geotechnical reliability analysis are explained, and some specific case-based teaching methods are summarized. The case-based teaching is mainly composed of three parts. The first part consists of teaching of the importance of geotechnical reliability analysis. Geotechnical investigation report is taken as an example for explaining the reason and necessity of geotechnical reliability analysis. The second part consists of teaching of several popularly used reliability analysis methods. Three types of examples with increasing complexities are used for the teaching and comparison of reliability analysis methods. The third part consists of teaching of the application of geotechnical reliability analysis in engineering practice. National norms are recommended to students to strengthen their understanding of the practical importance and necessity of leaning geotechnical reliability analysis.

Mapping of groundwater distribution potential over space, built by synergizing environmental variables and machine learning models, was of great significance for regional water resources management. A total of 245 wells were identified based on field survey in the Chihe River basin in Anhui province, out of which 172 wells locations were randomly used for training the machine models and the other 73 wells for validation process of machine models. Thirteen environmental variables including elevation, slope, slope aspect, plan curvature, profile curvature, topographic wetness index (TWI), drainage density, distance to rivers, distance to faults, lithology, soil type, land use, and normalized difference vegetation index (NDVI) were used to build the spatial database of this research. Three GIS-based machine learning models were used for mapping the groundwater distribution potential: logistic regression (LR), deep neural networks (DNN) and random forest (RF). Then, the applicability of those models was evaluated by the evaluation index of mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R). The final results indicated that the potential of regional groundwater distribution is concentrated in moderate to high potential areas. Among them, the moderate to the high potential distribution area in the LR model accounted for 81.14% of the total area, 90.36% and 87.55% in the DNN model and the RF model, respectively. In addition, three machine learning models can be implemented for prediction of groundwater distribution based on the three evaluation indexes, among which the LR model performs more prominently. The good prediction capabilities of machine learning technologies can provide a reliable scientific basis for spatial prediction of groundwater distribution and management of water resources.

为研究轴向卸荷路径下的土体回弹变形特性以及初始含水率和蒙脱石粉含量等指标的影响,以合肥膨胀土为研究对象,开展了一系列的室内一维压缩回弹试验.试验结果表明:卸荷时合肥膨胀土样的e-p曲线具有明显的先缓后陡特征;每级卸荷量越大,膨胀土产生的回弹变形量也就越大;与其他土质相比,合肥膨胀土的一维压缩回弹变形偏低;最大轴向荷载、卸荷比等指标与回弹变形指标之间具有良好的拟合函数关系;相同卸荷比条件下,试样回弹变形量随初始含水率增高而相应增大;当初始含水率较低时,试样的非饱和吸力会抑制土样的回弹变形;蒙脱石粉含量与试样的一维压缩回弹变形之间具有很好的正相关性.这表明膨胀土的胀缩性会影响到膨胀土基坑坑底土体产生的回弹变形量.

Expansive soil encounters large changes in strength and volume with the water content alteration for rich in hydrophilic montmorillonite minerals. It can be physically/chemically modified to suppress the water sensitivity by mixing with recycled industrial by-products (iron tailing sand and calcium carbide slag in this case) as the backfilling material of subgrades for the engineering practices. The specimens were prepared with 30

To disclose the distribution characteristics, the situation of flow and storage, and processes along flow paths of shallow groundwater in Tan-Lu fault zones, nine hundred and seven groundwater table elevations data and one hundred hydrochemical samples of shallow groundwater were taken from the Tan-Lu fault zone in Anhui province to analyze the characteristic of groundwater distribution. The geographic information system (GIS) method was used to analyze the spatial distribution characteristics of groundwater tables, total dissolved solids (TDS) and chloride ion (Cl − ). Geophysical prospecting, drilling material and regional hydrogeological survey were utilized to disclose groundwater storage and flow regime in the fault zone. The results show that the Tan-Lu fault zone in Anhui province has controlled groundwater flow into the Jiashan basin, Hefei basin, Chaohu area and Qianshan basin, which developed from north to south in this area. Groundwater in theses basins have recharged from surrounding areas to form a water storage space. Geophysical prospecting and drilling technology revealed that the Tan-Lu fault zone provided a flow channel and storage space for ground-water. Faults provide preferential channels in some areas for the groundwater flow and circulation, eventually deep hot-water flows upward and discharges in the form of hot-springs. The identification of the groundwater flow pathway can help to provide a reliable scientific basis for regional spatial development and utilization of groundwater resources.

Most of the pile's vertical static load tests in construction sites are the proof load tests, which is difficult to accurately estimate the ultimate bearing capacity and analyze the reliability of piles. Therefore, a reliability analysis method based on the proof load-settlement (Q-s) data is proposed in this study. In this proposed method, a simple ultimate limit state function based on the hyperbolic model is established, where the random variables of reliability analysis include the model factor of the ultimate bearing capacity and the fitting parameters of the hyperbolic model. The model factor M = R-uR / R-uP is calculated based on the available destructive Q-s data, where the real value of the ultimate bearing capacity (R-uR) is obtained by the complete destructive Q-s data; the predicted value of the ultimate bearing capacity (R-uP) is obtained by the proof Q-s data, a part of the available destructive Q-s data, that before the predetermined load determined by the pile test report. The results demonstrate that the proposed method can easy and effectively perform the reliability analysis based on the proof Q-s data.

有限单元法是进行数值计算及解决工程问题的重要工具,"有限单元法"课程的教学包括理论教学、程序教学及软件教学.其中,程序教学是培养研究生编程能力及创新能力的重要途径之一.针对当前研究生"有限单元法"课程在程序教学方面存在的问题,分析总结了有限元教学程序的选择原则,建议选择简短完整、与学生的专业方向有关的程序作为有限元教学程序,提出了"以点带面,点面结合"的有限元程序教学模式及具体的教学方法.通过指导学生绘制有限元程序流程图、学习重点子程序的编程、运行并修改有限元程序、小组交流与讨论等方式来组织教学,可以有效激发研究生学习"有限单元法"课程的热情,加深对有限单元法理论知识的理解,提高研究生的编程能力及创新能力.

文章以合肥市肥东县为研究区域,从地形地貌、工程地质条件、水文地质条件和地质灾害风险度4个方面选取7个指标构成了建设用地适宜性评价体系,采用G1法和层次分析法(analytic hierarchy process,AHP)的线性组合确定了各个指标的权重,以ArcGIS为平台对各个指标进行空间分析叠加,通过K-means聚类分析将建设用地适宜性分成4类.结果表明:肥东县建设用地适宜和较适宜区域共1891.32 km2,占工作区面积的83.55％;较不适宜区域共266.22 km2,占工作区面积的11.76％;不适宜区域共106.07 km2,占工作区面积的4.69％,涉及乡镇有店埠镇、包公镇和长临河镇等.此次评价与肥东县发展规划相吻合,不仅可以为肥东县的城乡建设发展提供宏观上的把握,还可以为各乡镇的发展政策提供可行性的建议.

文章以安徽省桐城市为研究区,选取高程、坡度、坡向、工程地质岩组、与断裂带的距离、与水系的距离及与道路的距离为区域地质灾害易发性评价因子,运用层次分析(analytic hierarchy process,AHP)法对传统信息量模型(information value model,ⅠVM)的评价结果进行加权分析,确定改进信息量模型(improved in-formation value model,ⅡVM);结合地理信息系统(geographic information system,GIS)对桐城市地质灾害的易发性进行评价,将研究区划分为高易发区、中易发区、低易发区及不易发区4类,研究区主要为不易发区和低易发区,中、高易发区占比28.68％(主要位于研究区西北部);使用受试者工作特征(receiver operating characteristic,ROC)曲线进行评估,ⅡVM评价结果的曲线下面积(area under the curve,AUC)值为0.807 7,高于传统IVM,说明ⅡVM具有更高的精度和有效性.评价结果与研究区地质灾害实际调查情况相符合,可为该区域地质灾害防治提供有效参考.

为适应当前对人才培养的需要,促进高校本科课程的教育教学改革,针对"工程地质学"课程目前存在的问题,建立符合OBE理念的课程教学体系.提出以"学生为中心"、以"成果为导向",从教学目标、教学过程、教学考核及教学质量四个方面入手,通过分解教学目标、设计教学过程、改革考核方式和设立教学质量的持续改进方案,以提高"工程地质学"课程的教学质量.课程教学改革实践可为同类课程和相近专业的教学改革提供一定的参考和借鉴.

Water inrush is a major hidden danger that decreases coal mining safety. Thus, preferential flowpaths of water inrush in mining areas must be investigated to prevent water disasters and guide grouting engineering. In this study, pumping and observation data of five boreholes were input into a 2D groundwater flow model established using VSAFT2 software for inversion to obtain the heterogeneous distribution information of the site. Then, the wide-field electromagnetic method was used to obtain the distribution of resistivity values at different depths in the local area. Finally, we compared and analyzed the relevant geological information of the site, such as the fault zones and karst collapse columns, obtained using the above two methods to determine the preferential flowpaths of water. The results show that (1) the hydraulic conductivity distribution estimated by hydraulic tomography is consistent with the location distribution of high-resistivity and low-resistivity areas identified by the wide-field electromagnetic method; (2) the high hydraulic conductivity zones obtained by hydraulic tomography are consistent with the known distribution of geological fault zones, which is important because water inrush accidents often occur in areas where fault zones exist; and (3) based on the comprehensive analysis of the above results, a preferential flowpaths of water inrush was identified. The flowpaths starts from the F35 fault zone, passes through KCC2, converges with the water from the middle part of fault zones F11 and F12, and finally supplies Dongfeng well D1. This paper compares HT results to resistivity estimates from independently conducted geophysical surveys. The identification of the groundwater flow pathway through HT can help to minimize future water in-rush accidents.