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1.
The Bhuj earthquake (Mw = 7.9) occurred in the western part of India on 26th January 2001 and resulted in the loss of 20,000 lives and caused extensive damage to property. Soil liquefaction related ground failures such as lateral spreading caused significant damage to bridges, dams and other civil engineering structures in entire Kachchh peninsula. The Bhuj area is a part of large sedimentary basin filled with Jurassic, Tertiary and Quaternary deposits. This work pertains to mapping the areas that showed sudden increase in soil moisture after the seismic event, using remote sensing technique. Multi-spectral, spatial and temporal data sets from Indian Remote Sensing Satellite are used to derive the Liquefaction Sensitivity Index (LSeI). The basic concept behind LSeI is that the near infrared and shortwave infrared regions of electromagnetic spectrum are highly absorbed by soil moisture. Thus, the LSeI is herein used to identify the areas with increase in soil moisture after the seismic event. The LSeI map of Bhuj is then correlated with field-based observation on Cyclic Stress Ratio (CSR) and Cyclic Resistance Ratio (CRR), depth to water table, soil density and Liquefaction Severity Index (LSI). The derived LSeI values are in agreement with liquefaction susceptible criteria and observed LSI (R 2 = 0.97). The results of the study indicate that the LSeI after calibration with LSI can be used as a quick tool to map the liquefied areas. On the basis of LSeI, LSI, CRR, CSR and saturation, the unconsolidated sediments of the Bhuj area are classified into three susceptibility classes.  相似文献   

2.
Every year, the Republic of Korea experiences numerous landslides, resulting in property damage and casualties. This study compared the abilities of frequency ratio (FR), analytic hierarchy process (AHP), logistic regression (LR), and artificial neural network (ANN) models to produce landslide susceptibility index (LSI) maps for use in predicting possible landslide occurrence and limiting damage. The areas under the relative operating characteristic (ROC) curves for the FR, AHP, LR, and ANN LSI maps were 0.794, 0.789, 0.794, and 0.806, respectively. Thus, the LSI maps developed by all the models had similar accuracy. A cross-tabulation analysis of landslide occurrence against non-occurrence areas showed generally similar overall accuracies of 65.27, 64.35, 65.51, and 68.47 % for the FR, AHP, LR, and ANN models, respectively. A correlation analysis between the models demonstrated that the LR and ANN models had the highest correlation (0.829), whereas the FR and AHP models had the lowest correlation (0.619).  相似文献   

3.
There have been significant advances in the application of critical state,CS,in liquefaction potential assessment.This was done by comparing state parameter,j with estimated characteristic cyclic stress ratio,CSR due to an earthquake.A cyclic resistance ratio,CRR curve,which can be determined from cyclic liquefaction tests,separates historical liquefied and non-liquefied data points(j,CSR).On the other hand,the concepts of equivalent granular state parameter,j*,which was developed for sands with fines,can be used in lieu j to provide a unifying framework for characterizing the undrained response of sands with non/low plasticity fines,irrespective of fines content(fc).The present work combines these two propositions,and by merely substituting j*for j into the aforementioned CS approach to capture the influence of fc.A series of static and cyclic triaxial tests were conducted,separately and independently of the concept of j*,for sand with up to fc of 30%.The clean sand was collected from Sabarmati river belt at Ahmedabad city in India which was severely affected during the Bhuj earthquake,2001.The experimental data gave a single relation for CRR and j*which was then used to assess liquefaction potential for a SPT based case study,where fc varies along the depth.The prediction matched with the field observation.  相似文献   

4.
Liquefaction resistance of granular soils is commonly characterized by the cyclic resistance ratio (CRR) in the simplified shear stress procedure of liquefaction potential assessment. This parameter is commonly estimated by cyclic tests on reconstituted samples or empirical correlations between liquefied/non-liquefied case histories. The current study employs results of cyclic triaxial tests on reconstituted soil specimens and presents a predictive equation for cyclic resistance ratio (CRR) of clean and silty sands. The CRR equation is a function of relative density, effective mean confining pressure, non-plastic fines content, number of harmonic cycles for liquefaction onset, and some other basic soil properties. It is demonstrated that the developed relationship obtains reasonable accuracy in the prediction of laboratory-based CRR. Based on the developed CRR model, new relationships are then presented for the coefficient of effective overburden pressure (Kσ) and magnitude scaling factor (MSF), two important modification factors in the simplified shear stress procedure. These new modification factors are then compared with those recommended by previous researchers. Finally, the possible application of the proposed CRR model in field condition is shown for a specific case. This study provides a preliminary insight into the liquefaction resistance of silty sands prior to the complementary laboratory studies.  相似文献   

5.
为研究地震作用下饱和砂土液化判别及地震放大效应的影响因素,采用边界面塑性模型框架内开发的砂土本构模型,基于开源有限元平台OpenSees建立了一维剪切梁土柱模型。以循环应力比CSR和循环抗力比CRR为控制指标,对比了不同液化判别方法的差异,分析了地震荷载类型和砂土相对密度对液化判别和放大效应的影响。研究表明:与数值模拟结果相比,Seed简化法计算的CSR更大,判断饱和砂土场地发生液化的可能性更高;冲击型地震波较振动型地震波更容易使饱和砂土场地发生液化,砂土相对密度越小场地越容易发生液化;放大系数随埋深的减小而增大,振动型地震波引起的放大效应整体大于冲击型,埋深较大时放大系数随砂土相对密度的增大而减小。  相似文献   

6.
基于Logistic回归模型的砂土液化概率评价   总被引：2，自引：1，他引：1  
潘建平  孔宪京  邹德高 《岩土力学》2008,29(9):2567-2571
以国内外23次地震中200组场地液化实测数据为基础,通过Logistic回归分析,建立关联修正标准贯入击数N160cs与循环应力比CSR的液化概率模型。以50 %液化概率水平为液化与非液化的临界点,建立了指数形式的抗液化应力比CRR计算式,新建概率模型预测饱和砂土液化与非液化的成功率分别为85.71 %和76.14 %,具有较高的可靠性。与已有模型比较,使用了新的数据和修正系数,消除了一些不合理的偏差,总体判别结果偏于安全。为了将确定性分析方法与概率分析方法联系起来,建立了抗液化安全系数FS与液化概率PL的关系式。算例结果表明,新建概率模型简单、实用、可靠。  相似文献   

7.
This papers presents a new approach for developing a limit state for liquefaction evaluation based on field performance data. As an example to illustrate the new approach, a database that consists of, among many other features, in situ shear wave velocity measurements and field observations of liquefaction/non‐liquefaction in historic earthquakes is analysed. This database is first used to train a neural network to classify liquefaction/non‐liquefaction based on soil resistance parameters and load parameters. The successfully trained and tested neural network is then used to establish a limit state, a multiple dimension boundary that separates ‘zone’ of liquefaction from ‘zone’ of non‐liquefaction. The limit state yields cyclic resistance ratio for a given set of soil resistance parameters. Examination of all cases in the database show that the developed limit state has a high degree of accuracy in predicting the occurrence of liquefaction/non‐liquefaction. The developed neural network model can accurately predict the cyclic resistance ratio of soils. Copyright © 2000 John Wiley & Sons, Ltd.  相似文献   

8.
The determination of liquefaction potential of soils induced by earthquake is a major concern and an essential criterion in the design process of the civil engineering structures. A purely empirical interpretation of the filed case histories relating to liquefaction potential is often not well constrained due to the complication associated with this problem. In this study, an integrated fuzzy neural network model, called Adaptive Neuro-Fuzzy Inference System (ANFIS), is developed for the assessment of liquefaction potential. The model is trained with large databases of liquefaction case histories. Nine parameters such as earthquake magnitude, the water table, the total vertical stress, the effective vertical stress, the depth, the peak acceleration at the ground surface, the cyclic stress ratio, the mean grain size, and the measured cone penetration test tip resistance were used as input parameters. The results revealed that the ANFIS model is a fairly promising approach for the prediction of the soil liquefaction potential and capable of representing the complex relationship between seismic properties of soils and their liquefaction potential.  相似文献   

9.
利用GDS空心圆柱仪进行了一系列主应力方向角?d变化的轴向、扭转、内压和外压四向耦合不排水循环剪切试验。在均等固结条件下,着重研究了循环加载方向角?d0对饱和粉土动力特性的影响。试验结果表明：饱和粉土的双规准化孔压发展模式与?d0无关,但受循环应力比CSR的影响;广义剪应变的发展模式不受?d0的影响。在循环剪切过程中,循环加载方向的变化对粉土的不排水动强度有显著影响,饱和粉土的动强度CRR随着?d0的增大呈现出先减小后增大的变化趋势,且当?d0=45°时CRR最小。同时,建立了反映?d0与CSR影响的孔压、变形的模型,并给出了相应的动强度表达式。  相似文献   

10.
This paper presents simplified dilatometer test (DMT)-based methods for evaluation of liquefaction resistance of soils, which is expressed in terms of cyclic resistance ratio (CRR). Two DMT parameters, horizontal stress index (KD) and dilatometer modulus (ED), are used as an index for assessing liquefaction resistance of soils. Specifically, CRR–KD and CRR–ED boundary curves are established based on the existing boundary curves that have already been developed based on standard penetration test (SPT) and cone penetration test (CPT). One key element in the development of CRR–KD and CRR–ED boundary curves is the correlations between KD (or ED) and the blow count (N) in the SPT or cone tip resistance (qc) from the CPT. In this study, these correlations are established through regression analysis of the test results of SPT, CPT, and DMT conducted side-by-side at each of five sites selected. The validity of the developed CRR–KD and CRR–ED curves for evaluating liquefaction resistance is examined with published liquefaction case histories. The results of the study show that the developed DMT-based models are quite promising as a tool for evaluating liquefaction resistance of soils.  相似文献   

11.
基于深度学习的CZ铁路康定—理塘段滑坡易发性评价   总被引：1，自引：0，他引：1  
CZ铁路康定至理塘段地处青藏高原东部边缘,区域内地形地貌多变、地质构造复杂,滑坡灾害极其发育,严重威胁着CZ铁路康定至理塘段的规划建设和未来安全运行。因此,选取高程、坡向、平面曲率、剖面曲率、地形起伏度、地表切割度、地形湿度指数、归一化植被指数、岩性、距断层距离、距河流距离、距道路距离共计12个影响因子构建滑坡空间数据库,采用深度学习的卷积神经网络(convolutional neural network,CNN)模型进行滑坡易发性评价,根据易发性指数将研究区划分为极高易发区(13.76%)、高易发区(14.00%)、中易发区(15.86%)、低易发区(18.17%)、极低易发区(38.21%)5个等级,并与人工神经网络(artificial neural network,ANN)模型进行对比。结果表明,CNN模型的评价精度AUC(0.87)大于ANN(0.84)模型,且极高易发区的频率比值高于ANN模型,CNN模型在本研究区有着更高的预测能力;极高和高易发区主要分布在水系较为发育的地区,沿着雅砻江和其他河流两侧2 km范围内呈带状分布。滑坡易发性评价结果较好地反映了研究区滑坡灾害发育的分布现状,能够为该区的CZ铁路建设和未来安全运行过程中的防灾减灾工作提供科学的依据。  相似文献   

12.
Without a doubt, landslide is one of the most disastrous natural hazards and landslide susceptibility maps (LSMs) in regional scale are the useful guide to future development planning. Therefore, the importance of generating LSMs through different methods is popular in the international literature. The goal of this study was to evaluate the susceptibility of the occurrence of landslides in Zonouz Plain, located in North-West of Iran. For this purpose, a landslide inventory map was constructed using field survey, air photo/satellite image interpretation, and literature search for historical landslide records. Then, seven landslide-conditioning factors such as lithology, slope, aspect, elevation, land cover, distance to stream, and distance to road were utilized for generation LSMs by various models: frequency ratio (FR), logistic regression (LR), artificial neural network (ANN), and genetic programming (GP) methods in geographic information system (GIS). Finally, total four LSMs were obtained by using these four methods. For verification, the results of LSM analyses were confirmed using the landslide inventory map containing 190 active landslide zones. The validation process showed that the prediction accuracy of LSMs, produced by the FR, LR, ANN, and GP, was 87.57, 89.42, 92.37, and 93.27 %, respectively. The obtained results indicated that the use of GP for generating LSMs provides more accurate prediction in comparison with FR, LR, and ANN. Furthermore; GP model is superior to the ANN model because it can present an explicit formulation instead of weights and biases matrices.  相似文献   

13.
Landslide-related factors were extracted from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, and integrated techniques were developed, applied, and verified for the analysis of landslide susceptibility in Boun, Korea, using a geographic information system (GIS). Digital elevation model (DEM), lineament, normalized difference vegetation index (NDVI), and land-cover factors were extracted from the ASTER images for analysis. Slope, aspect, and curvature were calculated from a DEM topographic database. Using the constructed spatial database, the relationships between the detected landslide locations and six related factors were identified and quantified using frequency ratio (FR), logistic regression (LR), and artificial neural network (ANN) models. These relationships were used as factor ratings in an overlay analysis to create landslide susceptibility indices and maps. Three landslide susceptibility maps were then combined and applied as new input factors in the FR, LR, and ANN models to make improved susceptibility maps. All of the susceptibility maps were verified by comparison with known landslide locations not used for training the models. The combined landslide susceptibility maps created using three landslide-related input factors showed improved accuracy (87.00% in FR, 88.21% in LR, and 86.51% in ANN models) compared to the individual landslide susceptibility maps (84.34% in FR, 85.40% in LR, and 74.29% in ANN models) generated using the six factors from the ASTER images.  相似文献   

14.
基于GIS与WOE-BP模型的滑坡易发性评价   总被引：1，自引：0，他引：1       下载免费PDF全文
郭子正  殷坤龙  付圣  黄发明  桂蕾  夏辉 《地球科学》2019,44(12):4299-4312
区域滑坡易发性研究对地质灾害风险管理具有重要意义.以往研究中,将多元统计模型与机器学习方法相结合用于滑坡易发性评价的研究较少.以三峡库区万州区为例,首先选取9种指标因子（坡度、坡向、剖面曲率、地表纹理、地层岩性、斜坡结构、地质构造、水系分布及土地利用类型）作为滑坡易发性评价指标.基于证据权模型（weights of evidence,WOE）计算得到的对比度和滑坡面积比与分级面积比的相对大小,对各指标因子进行状态分级;再利用粒子群法优化的BP神经网络模型（PSO-BP）得到各指标因子权重.综合两种模型确定的状态分级权重和指标因子权重（WOE-BP）计算滑坡易发性指数（landslide susceptibility index,LSI）,基于GIS平台得到全区滑坡易发性分区图.结果表明:水系、地层岩性和地质构造是影响万州区滑坡发育的主要指标因子;WOE-BP模型的预测精度为80.8%,优于WOE模型的73.1%和BP神经网络模型的71.6%,可为定量计算指标因子权重和优化滑坡易发性评价提供有效途径.   相似文献   

15.
Elastic properties of rocks play a major and crucial role for the design of any engineering structure. Determination of elastic properties in laboratory is tedious, laborious, very time consuming, as well as expertise is required, whereas determination of uniaxial compressive strength (UCS) and tensile strength in laboratory is simple, easy, and less expertise is required. Here, an attempt has been made to predict the elastic properties (Poisson’s ratio and Young’s modulus) of the schistose rocks from unconfined strength (UCS and tensile strength) using artificial neural network (ANN). A three-layer feed-forward back propagation neural network with 2-5-2 architecture was trained up to 855 epochs to predict the elastic properties of rock mass. The network was trained and tested by 120 data sets, and validation of the network was done by 20 new randomly selected data sets of UCS and tensile strength. The samples were collected from the schistose rocks of Nathpa-Jhakri hydropower project site, SJVNL, Himachal Pradesh, India. To check the validity and suitability of the artificial neural network technique, multivariate regression analysis (MVRA) is also performed, and comparison has been made. It was found that ANN gives closer values of predicted Poisson’s ratio and Young’s modulus as compared to MVRA. The coefficient of determination for Poisson’s ratio was 0.9809 and 0.843 by ANN and MVRA, respectively, whereas 0.9922 and 0.9362 for Young’s modulus by ANN and MVRA, respectively. The mean absolute percentage error (MAPE) for Young’s modulus is 11.13 and 28.21 by ANN and MVRA, respectively; whereas MAPE for Poisson’s ratio is 3.64 and 9.23 by ANN and MVRA, respectively.  相似文献   

16.
Soil liquefaction as a transformation of granular material from solid to liquid state is a type of ground failure commonly associated with moderate to large earthquakes and refers to the loss of strength in saturated, cohesionless soils due to the build-up of pore water pressures and reduction of the effective stress during dynamic loading. In this paper, assessment and prediction of liquefaction potential of soils subjected to earthquake using two different artificial neural network models based on mechanical and geotechnical related parameters (model A) and earthquake related parameters (model B) have been proposed. In model A the depth, unit weight, SPT-N value, shear wave velocity, soil type and fine contents and in model B the depth, stress reduction factor, cyclic stress ratio, cyclic resistance ratio, pore pressure, total and effective vertical stress were considered as network inputs. Among the numerous tested models, the 6-4-4-2-1 structure correspond to model A and 7-5-4-6-1 for model B due to minimum network root mean square errors were selected as optimized network architecture models in this study. The performance of the network models were controlled approved and evaluated using several statistical criteria, regression analysis as well as detailed comparison with known accepted procedures. The results represented that the model A satisfied almost all the employed criteria and showed better performance than model B. The sensitivity analysis in this study showed that depth, shear wave velocity and SPT-N value for model A and cyclic resistance ratio, cyclic stress ratio and effective vertical stress for model B are the three most effective parameters on liquefaction potential analysis. Moreover, the calculated absolute error for model A represented better performance than model B. The reasonable agreement of network output in comparison with the results from previously accepted methods indicate satisfactory network performance for prediction of liquefaction potential analysis.  相似文献   

17.
马维嘉  陈国兴  吴琪 《岩土力学》2020,41(2):535-542
循环加载方式与应力路径对砂土的抗液化强度有很大的影响。利用GDS空心圆柱扭剪仪对南海珊瑚砂进行了一系列复杂加载条件下均等固结不排水循环试验,探讨了90°突变应力路径下主应力方向角对珊瑚砂抗液化强度的影响。试验结果发现：以循环应力比（CSR）作为应力水平指标,当不控制中主应力系数b的变化时,主应力方向角 对珊瑚砂的抗液化强度并无显著影响;当控制b始终保持0.5时,珊瑚砂的抗液化强度随着 的增加呈现出先减小后增大的趋势,且在 45°时的抗液化强度最低。基于分析循环荷载引起的土单元大、小循环主应力 、 变化,定义了单元体循环应力比（USR）作为一个新的物理指标,发现不同循环加载方式与应力路径条件下施加于珊瑚砂试样的USR与引起液化所需的循环次数NL存在事实上的唯一性关系。通过引自文献的4种无黏性土原始试验数据的再处理,独立地验证了以USR表征砂类土液化强度的适用性。  相似文献   

18.
王海波  吴琪  杨平 《岩土力学》2018,39(8):2771-2779
为研究细粒含量FC对不同密实状态饱和砂类土液化强度CRR的影响,将不同FC的砂类土试样分为3组：（1）相同的相对密实度 50%;（2）相同的孔隙比 0.90;（3）相同的骨架孔隙比 1.20,对不同FC和密实状态（ 、e和 ）的砂类土进行了一系列不排水循环三轴试验。试验结果显示：e或 相同的砂类土CRR随着FC的增加逐渐降低;具有相同 砂类土的CRR随FC的增加迅速增大,砂类土的CRR与 、e或 都没有较好的相关性。分析表明：不同FC和密实状态砂类土的CRR随等效骨架孔隙比 的增大而降低,两者呈现较好的负幂函数关系,这表明考虑细粒影响程度的 是合理表征不同种类砂类土CRR的一个物理状态指标。通过对比已有的砂类土的试验结果发现：砂类土中的砂粒是影响CRR的重要因素,且随着砂粒的形状从圆状向角状变化,砂类土的总体CRR逐渐增大。  相似文献   

19.
This case study presented herein compares the GIS-based landslide susceptibility mapping methods such as conditional probability (CP), logistic regression (LR), artificial neural networks (ANNs) and support vector machine (SVM) applied in Koyulhisar (Sivas, Turkey). Digital elevation model was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index, normalized difference vegetation index, distance from settlements and roads were used in the landslide susceptibility analyses. In the last stage of the analyses, landslide susceptibility maps were produced from ANN, CP, LR, SVM models, and they were then compared by means of their validations. However, area under curve values obtained from all four methodologies showed that the map obtained from ANN model looks like more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results also showed that the CP is a simple method in landslide susceptibility mapping and highly compatible with GIS operating features. Susceptibility maps can be easily produced using CP, because input process, calculation and output processes are very simple in CP model when compared with the other methods considered in this study.  相似文献   

20.
Medium-coarse sands (CS) were dredged and exhausted in land reclamation. However, the remaining silty-fine sands (FS) were wasted. The liquefaction behavior of dredged silty-FS and the possibility of utilizing the remaining silty-FS as dredger fill source for land reclamation should be investigated. Cyclic consolidation-undrained triaxial tests were performed to investigate the liquefaction resistance of dredged silty-FS under different influencing factors. The cyclic stress ratio (CSR) of dredged silty-FS increased with the increase in initial relative density and consolidation stress ratio and decreased with the increase in silt content and consolidation stress. The CSR first decreased with the increase in clay content up to a threshold value and increased with the increase in clay content. A regression model was created to estimate the relationship between CSR and silt content, clay content, initial relative density, consolidation stress, consolidation stress ratio, and cyclic resistance ratio. Response surface methodology (RSM) was employed to investigate the mutual influence among the five independent variables. On the basis of cyclic triaxial tests, particle flow code models were introduced to investigate the microscopic internal fabric changes of dredged silty-FS and the influence of extended factors on liquefaction. The average microscopic contact force and coordination number between particles controlled the macroscopic mechanical behavior of sands. Sand liquefaction was due to the cumulative loss of coordination number under cyclic loading. The average contact force between particles was linearly decreased to 0 and the coordination number sharply decreased when the sample reached initial liquefaction. On the basis of numerical tests, CSR increased with the increase in D50 and vibration frequency. The influence of vibration frequency was relatively small. In addition, the CS–FS and CS–FS–CS combination layers showed greater liquefaction resistance than the FS layer. In the filling process, the interbed of FS and CS improved the liquefaction resistance of dredged silty-FS to a certain extent.  相似文献   

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