Assessment of bearing capacity of interfering strip footings located near sloping surface considering artificial neural network technique

The bearing capacity of interfering footings located near the slope face suffers from reduced bearing capacity due to the formation of the curtailed passive zone. Depending upon the position of the footing, their spacing and steepness of the slope different extents of bearing capacity reduction can be exhibited. A series of finite element investigation has been done with the aid of Plaxis 3 D v AE.01 to elucidate the influence of various geotechnical and geometrical parameters on the ultimate bearing capacity of interfering surface strip footings located at the crest of the natural soil slope. Based on the large database obtained from the numerical simulation, a6-8-1 Artificial Neural Network architecture has been considered for the assessment of the ultimate bearing capacity of interfering strip footings placed on the crest of natural soil slope. Sensitivity analyses have been conducted to establish the relative significance of the contributory parameters, which exhibited that for the stated problem, apart from shear strength parameters, the setback ratio and spacing of footing are the prime contributory parameters.

Application of artificial neural network for calculating anisotropic friction angle of sands and effect on slope stability

The anisotropy effect is one of the most prominent phenomena in soil mechanics. Although many experimental programs have investigated anisotropy in sand, a computational procedure for determining anisotropy is lacking. Thus, this work aims to develop a procedure for connecting the sand friction angle and the loading orientation. All principal stress rotation tests in the literatures were processed via an artificial neural network. Then, with sensitivity analysis, the effect of intrinsic soil properties,consolidation history, and test sample characteristics on enhancing anisotropy was examined. The results imply that decreasing the grain size of the soil increases the effect of anisotropy on soil shear strength. In addition, increasing the angularity of grains increases the anisotropy effect in the sample. The stability of a sandy slope was also examined by considering the anisotropy in shear strength parameters. If the anisotropy effect is neglected, slope safety is overestimated by 5%-25%. This deviation is more apparent in flatter slopes than in steeper ones. However, the critical slip surface in the most slopes is the same in isotropic and anisotropic conditions.

Artificial neural network based inverse design method for circular sliding slopes

Current design method for circular sliding slopes is not so reasonable that it often results in slope (sliding.) As a result, artificial neural network (ANN) is used to establish an artificial neural network based inverse design method for circular sliding slopes. A sample set containing 21 successful circular sliding slopes excavated in the past is used to train the network. A test sample of 3 successful circular sliding slopes excavated in the past is used to test the trained network. The test results show that the ANN based inverse design method is valid and can be applied to the design of circular sliding slopes.

An Artificial Neural Network-Based Response Surface Method for Reliability Analyses of c-φ Slopes with Spatially Variable Soil

This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model;the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties;and finally,an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables.The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability.As a result,the obtained approximate function can be used as an alternative to the specific analysis process in c-φslope reliability analyses.

Flume experiments to study fine-grain migration and its impact on slope stability

Fine grains migration is a primary cause of landslides and debris flows.This study investigates the effect of fine-grain migration on slope failure through flume experiments,focusing on the spatiotemporal characteristics and mechanisms of slope stability.A series of artificial rainfall flume experiments with varying rainfall intensities and slopes were conducted using soil samples collected from Wei Jia Gully.The experiments monitored pore-water pressure,grain migration,and failure sequences.Grain-size distribution parameters(μand Dc)were analyzed to understand the migration path and accumulation of fine grains.The experiments reveal that fine-grain migration significantly alters soil structure,leading to random blockage and interconnection of internal pore channels.These changes result in fluctuating pore-water pressure distributions and uneven fine-grain accumulation,critical factors in slope stability.Slope failures occur randomly and intermittently,influenced by fine-grain content in runoff and resulting pore-water pressure variations.This study highlights that fine-grain migration plays a vital role in slope stability,with significant implications for predicting and mitigating slope failures.The stochastic nature of fine-grain migration and its impact on soil properties should be incorporated into predictive models to enhance their accuracy and reliability.

Methods for the construction of DEMs of artificial slopes considering morphological features and semantic information

As human activities increase,artificially modified terrain is increasingly widely distributed in road,hydrological,and urban construction.Artificially modified terrain plays an important role in protecting from geological disasters and in the planning and design of urban landscapes.Compared with natural slopes,artificial slopes have obvious morphological characteristics.Traditional modeling methods are no longer suitable for digital elevation model(DEM)modeling of artificial slopes because they often seriously distort the DEM results.In this paper,from the perspective of morphological characteristics,artificial slopes are divided into two types,namely,regular slopes and irregular slopes,based on whether the top and bottom lines of the artificial slope are parallel.Then,according to the morphological characteristics of the two types of slopes,the following DEM construction methods are designed:the first method(perpendicular+inverse distance weighted)is suitable for regular slopes,and the second method(perpendicular+high-accuracy surface modeling)is suitable for irregular slopes.Finally,a DEM construction test is carried out using the artificial slopes in the study area.The results show that for the regular and irregular slopes in the study area,the construction method proposed in this paper has significant advantages in morphological accuracy over the traditional method(triangulated irregular network),and the elevation accuracy method is also superior to the traditional method(using this method,the mean error and standard deviation error of the regular slope DEM are 0.08 m and 0.13 m,respectively,and those of the irregular slope DEM are 0.08 m and 0.06 m).In addition,the top lines and bottom lines can be included in the DEM construction of the background area after processing the elevation information of the boundary line to realize a smooth transition in the boundary between the artificial slope and the background area.

抗冻型人造土冻融适应性及其在边坡植被修复中的应用

[目的]探究抗冻型人造土冻融适应性及其在高寒地区边坡植被修复中的实践效果,为高寒地区边坡植被修复工程应用提供理论依据和应用支撑。[方法]采用室内冻融循环试验剖判人造土在冻融循环60次期间的理化性质变化特征,结合主成分分析确定室外试验监测指标的选取,现场取样监测了近4 a人造土在西藏高寒地区岩质边坡的工程应用及其植被修复效果,并与当地天然土作对照分析。[结果]与天然土相比,抗冻型人造土的抗冻稳定性和肥力均有显著提升(p<0.05),具体表现在其冻融前后的相对动弹性模量、>0.25 mm水稳性大团聚体含量、平均重量直径(MWD)、团聚度及有机质、铵态氮、硝态氮、速效磷和速效钾含量显著提升,而质量损失率和结构破坏率显著降低(p<0.05)。主成分分析显示,室内试验指标中的相对动弹性模量、结构破坏率、pH值和有机质含量与主成分因子的相关性较高,因此在室外试验中监测这4个指标和植被修复情况。室外监测中发现抗冻型人造土4 a内在不同时间段上的结构破坏率均更低,相对动弹性模量和有机质含量更高;其边坡植被修复后的植被覆盖率、物种多样性、地上和地下生物量均更佳。[结论]抗冻型人造土在高寒地区应用时的植被修复效果更佳,特别是其抗冻耐久性、团聚体稳定性和肥力表现突出,均优于天然土,因此其更适宜于边坡植被修复应用。

不同秸秆还田模式下红壤坡耕地产流产沙特征

通过人工模拟降雨试验,研究不同秸秆还田量(0.30、0.45、0.60和0.75 kg/m^(2))和不同还田方式(粉碎覆盖还田(crushed mulch reversion,CV)、粉碎翻耕还田(crushed tillage reversion,RT)、覆盖-粉碎翻耕还田(mulch-crushed tillage reversion,CR))下地表产流产沙变化过程,分析秸秆不同还田方式和不同还田量下地表产流产沙规律及其内在机理。结果表明:1)在相同还田方式下,秸秆还田量对产流时间有显著影响,随着秸秆还田量增加,初始产流时间、产流稳定时间均增加;在相同秸秆还田量下,CV和RT产流时间差异不大,CR推迟产流时间最明显,CR初始产流时间比对照组推迟19.42~55.17 min。2)在相同还田方式下,随着秸秆还田量增加,地表径流初始产流强度、稳定产流强度减小,地表总产流量显著减小,其中RT、CR在0.60 kg/m^(2)与0.75 kg/m^(2)还田量时地表总产流无显著差异;相同秸秆还田量时,CR减流效益显著大于CV、RT,初始径流强度较对照组减少23.64~44.04 mm/h,地表总产流量较对照组减少55.74%~98.92%。3)在相同还田方式下,随着秸秆还田量增加,产沙强度不断减小,地表总产沙显著减小,3种还田方式下还田量在0.60 kg/m^(2)与0.75 kg/m^(2)时地表总产沙无显著差异;相同秸秆还田量下,CR减沙效益大于CV、RT,较对照组减沙效益为93.73%~99.99%。可见,0.60 kg/m^(2)覆盖-粉碎翻耕还田为最佳还田模式。

人工降雨条件下高速公路植被边坡模型水文效应测试

高速公路边坡防护常采用生态护坡方式,尤其以植被结合覆盖方式防护效果较好。为掌握高速公路边坡的水文效应,通过建立边坡试验模型,设置不同处理组合开展人工降雨,探讨不同覆盖物、不同生长期植被、不同降雨强度对边坡模型水文效应的影响,以便确定最佳的边坡植被防护体系参数。结果表明:在降雨强度为120 mm·h^(-1)时,覆盖椰丝草毯、秸秆纤维毯、草帘的边坡比裸土边坡的坡面径流量分别减少23.25%、15.66%、17.94%,而坡体水分渗透量分别增加了65.19%、48.10%、27.89%。在降雨强度为120 mm·h^(-1)时,不同生长期的植被边坡防护能力表现出明显的差异,其中4月生植被边坡模型的坡面径流量最小,坡体水分渗透量最大,抗雨水冲刷能力最强。降雨强度为60 mm·h^(-1)时,植被边坡并未产生坡面径流,植被发挥了很好的保护效果;随着降雨强度逐渐增大,裸土边坡和植被边坡的坡面产生径流时间和坡体水分渗出时间逐渐缩短,坡面径流量和泥沙占比均呈逐渐增大的变化趋势。综合而言,粉砂性高速公路边坡采用狗牙根(12 g·m^(-2))、紫穗槐(14 g·m^(-2))、胡枝子(10 g·m^(-2))种子混播后,覆盖椰丝草毯(300 g·m^(-2)),在正常管理养护条件下,待植物生长3个月后,坡面抗雨水冲刷能力显著增强,在降雨强度不超过120 mm·h^(-1)时,植被结合覆盖方式均表现出较好的防护效果。植物和覆盖物两者相互作用可以减弱雨水对于边坡的冲击强度,消减坡面径流量,同时植物根系有利于雨水渗透坡体,保持水土,涵养水源。

交联聚苯乙烯-植物联合护坡技术对黄土陡坡的控蚀效果

随着经济社会的发展,各类基础设施建设使黄土高原地区产生了许多高陡边坡,为了给黄土陡坡的修复及土壤侵蚀控制提供参考,基于野外原位小区观测及人工模拟降雨试验结果,分析了新型有机材料交联聚苯乙烯(CLPs)与植物联合护坡技术对黄土陡坡的控蚀效果及机理等,结果表明:1)采用CLPs对黄土坡面进行处理后,在相同的降雨情况下,其径流深比裸露坡面的大、产沙量与裸露坡面的相当,即仅采用CLPs对黄土坡面进行处理基本无控蚀作用;2)采用CLPs-植物联合护坡技术的CLPs-草灌坡面与CLPs-草皮坡面,径流深远小于传统草灌坡面和鱼鳞坑植灌木坡面的,土壤侵蚀模数远远小于传统草灌坡面和鱼鳞坑植灌木坡面的,年均土壤侵蚀模数仅为传统草灌坡面和鱼鳞坑植灌木坡面的0.9%~3.2%,即CLPs与植物联合对黄土坡面进行防护的控蚀效果显著;3)植被覆盖度为100%的CLPs-草灌联合修复坡面与无植被覆盖的CLPs护坡坡面相比,坡面径流的流速、雷诺数、弗劳德数、剪切力、功率显著减小,坡面阻力系数、糙率系数显著增大,这是CLPs-草灌联合护坡技术控蚀的水动力学机理;4)采用CLPs-植物联合护坡技术,可使黄土陡坡形成有效的土体加固体系、控蚀体系和保水体系。

水岸村落水体景观营造技术研究

当前,村落水体景观的营造成为提升农村环境质量和生态功能的关键环节。为了实现乡村的可持续发展,以临近黄河流域-珠江水域的某村落为研究对象,提出了因地制宜,沿流域设置坡式护岸和表面径流人工湿地的设计方案。结果表明,该设计下的绿化率提高了17.6%,反映了乡村水体景观营造对提升农村环境质量和生态功能的积极影响。

开挖和降雨耦合诱发工程边坡地质隐患易发性分析

华南多山地区较大的降雨量对边坡稳定性的影响是重要的影响因素之一。近年人类工程活动和降雨环境耦合诱发的地质灾害事件频发。本文以广东惠州市仲恺区一高边坡为例,对开挖和降雨耦合可能诱发的地质灾害事件,选取若干指标,利用bishop法对工程活动后边坡可能发生的地质灾害触发机理进行分析,提出预防建议。研究表明,强降雨对人工开挖边坡起到激发和加速作用,开挖深度加大与暴雨天气叠加,最终导致滑坡复活。

生态边坡植被对土体抗剪强度的影响研究

【目的】工程建设中产生大量的裸坡,需要对边坡进行植被恢复。探究植被对边坡土体的力学性质的影响,可为边坡稳定性评价提供参考。【方法】对研究区的根系和土体进行不同比例的混合,并进行直剪试验。【结果】结果表明:随着含水量的增加,土体的黏聚力呈现降低的趋势,根系能够明显增加土体的黏聚力,土体的内摩擦角也会有一定的提高,提高幅度小于黏聚力增加幅度。【结论】植被能够增强土体抗剪强度,生态护坡对边坡防护具有重要意义。

粤北山区人工切坡诱发崩塌灾害发育特征及稳定性评价

人类频繁工程活动形成的人工切坡容易诱发崩塌、滑坡等地质灾害。本文在对粤北山区开展地质灾害风险调查评价的基础上,分析区内人工切坡及其崩塌的发育特征,提出有针对性的防治措施建议,并以区内典型崩塌点为案例,评价了灾害点在不同工况下的稳定性。

基于人工神经网络的高速公路边坡滑坡风险分析

高速公路边坡滑坡是一种常见的地质灾害,对交通安全和环境保护有严重的影响,本研究以降雨量作为滑坡的重要诱因,使用人工神经网络的数据模型对土质边坡的滑坡风险进行评价。本研究使用了四层神经网络,输入项目包括地形、土质、支护结构、累计降雨量和最大单位时间降雨量,使用的数据集由40条发生滑坡的数据和320条未发生滑坡的数据组成,其中80%用作训练数据,20%用作验证数据。本研究为高速公路边坡滑坡风险评价提供了一种新的思路和方法,期望能够建立一个更加完善和高效的机器学习模型,为高速公路边坡的安全管理和防灾减灾提供更有价值的参考和支持。

静态水位和坡脚压载对库区造地高边坡稳定性的参数影响分析

本文运用有限元边坡稳定分析软件GeoStudio,针对含有机质土层地基上的库区造地高边坡,考虑饱和与非饱和渗流理论,建立了有限元分析模型,分析了水位参数和坡脚压载参数对边坡整体稳定的影响。分析结果表明:在边坡高度一定的情况下,边坡最小稳定安全系数随着水位相对高度的升高先降低后增加,在水位与地面齐平时,边坡安全系数达到最小;坡脚压载的合理高度应小于边坡高度的30%~50%,压载有效长度与压载高度的比值约为坡度倒数的10倍。

Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data

Slope failures lead to catastrophic consequences in numerous countries and thus the stability assessment for slopes is of high interest in geotechnical and geological engineering researches.A hybrid stacking ensemble approach is proposed in this study for enhancing the prediction of slope stability.In the hybrid stacking ensemble approach,we used an artificial bee colony(ABC)algorithm to find out the best combination of base classifiers(level 0)and determined a suitable meta-classifier(level 1)from a pool of 11 individual optimized machine learning(OML)algorithms.Finite element analysis(FEA)was conducted in order to form the synthetic database for the training stage(150 cases)of the proposed model while 107 real field slope cases were used for the testing stage.The results by the hybrid stacking ensemble approach were then compared with that obtained by the 11 individual OML methods using confusion matrix,F1-score,and area under the curve,i.e.AUC-score.The comparisons showed that a significant improvement in the prediction ability of slope stability has been achieved by the hybrid stacking ensemble(AUC?90.4%),which is 7%higher than the best of the 11 individual OML methods(AUC?82.9%).Then,a further comparison was undertaken between the hybrid stacking ensemble method and basic ensemble classifier on slope stability prediction.The results showed a prominent performance of the hybrid stacking ensemble method over the basic ensemble method.Finally,the importance of the variables for slope stability was studied using linear vector quantization(LVQ)method.

Early warning model for slope debris flow initiation

Early warning model of debris flow is important for providing local residents with reliable and accurate warning information to escape from debris flow hazards. This research studied the debris flow initiation in the Yindongzi gully in Dujiangyan City, Sichuan province, China with scaled-down model experiments. We set rainfall intensity and slope angle as dominating parameters and carried out 20 scaled-down model tests under artificial rainfall conditions. The experiments set four slope angles(32°, 34°, 37°, 42°) and five rainfall intensities(60 mm/h, 90 mm/h, 120 mm/h, 150 mm/h, and 180 mm/h) treatments. The characteristic variables in the experiments, such as, rainfall duration, pore water pressure, moisture content, surface inclination, and volume were monitored. The experimental results revealed the failure mode of loose slope material and the process of slope debris flow initiation, as well as the relationship between the surface deformation and the physical parameters of experimental model. A traditional rainfall intensity-duration early warning model(I-D model) was firstly established by using a mathematical regression analysis, and it was then improved into ISD model and ISM model(Here, I is rainfall Intensity, S is Slope angle, D is rainfall Duration, and M is Moisture content). The warning model can provide reliable early warning of slope debris flow initiation.

Three Practical Methods for Analyzing Slope Stability

Since the environmental capacity and the arable as well as the inhabitant lands have actually reached a full balance, the slopes are becoming the more and more important options for various engineering constructions. Because of the geological complexity of the slope, the design and the decision-making of a slope-based engineering is still not practical to rely solely on the theoretical analysis and numerical calculation, but mainly on the experience of the experts. Therefore, it has important practical significance to turn some successful experience into mathematic equations. Based upon the abundant typical slope engineering construction cases in Yunnan, Southwestern China, 3 methods for analyzing the slope stability have been developed in this paper. First of all, the corresponded analogous mathematic equation for analyzing slope stability has been established through case studies. Then, artificial neural network and multivariate regression analysis have also been set up when 7 main influencing factors are adopted.

不同坡向落叶松人工林林分生长模型差异的研究

本文利用红石林业局2016年森林资源调查小班数据库,对落叶松人工林不同坡向的林分生长模型进行了构建,并分析了模型参数差异。结果表明:不同坡向落叶松人工林公顷株数、公顷蓄积量、平均胸径和年龄等主要林分因子差异较小。落叶松人工林林分生长模型为理查德方程,不同坡向模型参数a差异显著,参数b和参数c差异不显著。阳坡、半阴半阳坡和阴坡模型参数a分别为207.7、207.6和181.5,参数b均为0.0513,参数c均为2.7616。