Probabilistic calibration of a coupled hydro-mechanical slope stability model with integration of multiple observations

Multiple types of responses, such as displacements, ground water level, pore water pressures, water contents, etc., are usually measured in comprehensive monitoring programmes for rainfall-induced landslide prevention. In this study, a probabilistic calibration method for coupled hydro-mechanical modelling of slope stability is presented with integration of multiple types of measurements. A numerical example of a soil slope under rainfall infiltration is illustrated to compare the effects of single and multiple types of responses on parameter estimation and model calibration. The results show that the soil parameters can be estimated with less uncertainty and total uncertainty bounds are narrower with multiple types of responses than with a single type of response. Model calibration based on multiple types of responses can compromise different responses and hence the means and standard deviations of model error are the smallest. A feasible correlation coefficient between soil modulus and permeability can be obtained from model calibration with multiple types of responses and single type of response as long as the responses include displacement data.     

Assessing soil salinity using WorldView-2 multispectral images in Timpaki,Crete, Greece

Salinization is one of the major soil degradation threats occurring worldwide. This study evaluates the feasibility of operational surface soil salinity mapping based on state-of-the-art Earth Observation (EO) products captured by sensors on-board WorldView-2 (WV2) and Landsat 8 satellites. The proposed methods are tested in Timpaki, south-central Crete,Greece, where brackish water irrigation puts soil health at risk of soil salinization. In all cases, EO products are calibrated against soil samples collected from bare soil locations. Results indicate a moderate correlation of observed EC_e values with the investigated remote sensing parameters. Regarding sensitivity to saline soil, the yellow band displays higher values. Comparison between methods used in the literature shows that those developed specifically for soil salinity, and especially index S5, perform better. The proposed ‘detection index’ and 3D PCA transformation methodology perform reasonably well in detecting areas with high EC_e values and provide a simple and effective operational alternative for saline topsoil detection and mapping.     

Soil salinity and vegetation cover change detection from multi-temporal remotely sensed imagery in Al Hassa Oasis in Saudi Arabia

Detecting soil salinity changes and its impact on vegetation cover are necessary to understand the relationships between these changes in vegetation cover. This study aims to determine the changes in soil salinity and vegetation cover in Al Hassa Oasis over the past 28 years and investigates whether the salinity change causing the change in vegetation cover. Landsat time series data of years 1985, 2000 and 2013 were used to generate Normalized Difference Vegetation Index (NDVI) and Soil Salinity Index (SI) images, which were then used in image differencing to identify vegetation and salinity change/no-change for two periods. Soil salinity during 2000–2013 exhibits much higher increase compared to 1985–2000, while the vegetation cover declined to 6.31% for the same period. Additionally, highly significant (p < 0.0001) negative relationships found between the NDVI and SI differencing images, confirmed the potential long-term linkage between the changes in soil salinity and vegetation cover.     

A comparative study of soil erosion modelling by MMF,USLE and RUSLE

The quantitative assessment of spatial soil erosion is valuable information to control the erosion. The study area in a part of Narmada river in central India is selected. The main objective is to assess and compare the results obtained from three soil erosion models using GIS platform. Variation in the rate of erosion of the three models is compared considering varying slope, soil and land use of the area. Three models selected are Morgan–Morgan–Finney (MMF), Universal Soil Loss Equation (USLE) and Revised Universal Soil Loss Equation (RUSLE). The best fit or the most reliable model for the study area is selected after validation with the observed sedimentation data. The results give –39.45%, –9.60% and 4.80% difference in the values of sedimentation by MMF, USLE and RUSLE, respectively, from the observed data. Finally, RUSLE model has been found to be most reliable for the study area.     

冻融循环下加筋膨胀土边坡稳定性模型试验

控制边坡在冻融循环中的劣化作用,可保障季节冻土区域膨胀土边坡长期稳定。为确定土工格栅对膨胀土边坡在冻融循环过程中的稳定效果与工程意义,本文开展了膨胀土边坡模型试验,对比冻融过程中边坡内土压力、含水率、位移、温度变化。结果表明：土工格栅可约束膨胀土冻融裂缝,使裂缝发育更为均匀一致,同时减小边坡位移;加筋材料能抑制边坡水分迁移与热传导并减小土压力变化;对膨胀土边坡加筋处理可显著降低含水率波动幅值,从而减小膨胀土受含水率变化引发的胀缩劣化;不同于普通黏土,膨胀土边坡冻融循环中呈现冻缩融胀特点,而边坡加筋可有效提升冻土区膨胀土边坡的冻融稳定性,具有工程应用价值。     

利用反射GPS信号遥感土壤湿度

在水文、气候、农业等问题的研究中,土壤湿度信息十分重要。近年来,利用GPS系统进行遥感的新方法,已经越来越多地引起研究者们的关注。特别是GPS卫星广播的L1（1.57542GHz）频率信号,是土壤湿度遥感的最佳频率。反射GPS信号功率是土壤介电常数的函数,而土壤介电常数又和土壤湿度有关。根据这一原理,美国国家航空与航天局于2002年进行了基于地表面反射GPS信号的遥感土壤湿度实验。结果证明反射GPS信号对土壤湿度特性十分敏感,由于植被的影响对反射率进行了修正,并用此方法模型与其它方法进行了比较。在利用GPS遥感土壤湿度方面,研制高增益天线,更好的修正信号波动的影响,发展更加完善的遥感土壤湿度方法等是今后在此项研究中所要解决的问题。     

中国土壤有机质含量变异性与空间尺度的关系

以中国土种志资料为基础,分析了土壤有机质含量变异与空间尺度的关系及土类内和土类间的变异程度,探讨了不同空间尺度单元下,土壤有机质平均含量与土壤性质的相关性。结果表明,以土壤剖面为单元,随着土壤剖面数的增加,土壤有机质含量的变异系数增大;行政区域尺度单元内部土壤有机质变异程度大于单元间土壤有机质含量的变异程度。以土壤分类单元为空间单元,土类内的有机质含量变异程度小于土类间的变异程度。随着统计单元空间尺度的增大,土壤有机质含量与土壤性质之间的相关性减弱。因此,采用网格法或行政区划分空间区域,获得空间区域单元内土壤有机质含量或贮量的精确估算需要较多的剖面,但外推至数据不足的空间区域时,估算的不确定较小;采用土壤分类单元为空间区域单元,结果则相反。     

利用气象要素计算五道梁地区土壤热流量的试验

根据五道梁地区1994—1997年的热流板测量土壤热流值,初步分析了该地区土壤热流随时间的变化特征。利用1994—1996年的月平均资料分别分析了总辐射、地表温度、地表温度和80 cm深度温度差和2 cm、10 cm深度的土壤热流的相关性,并建立了相应的回归方程;利用得到的回归方程对1997年的土壤热流作计算检验,就月平均资料来说,用总辐射和地温差来计算浅层土壤热流结果是比较可靠的。     

土氡测量在海口市活动断层探测中的初步应用

运用土壤氡射气(土氡)测量方法对海口市的六条隐伏断层进行探测,检验土壤氡射气(土氡)测量在海口市地区的有效性,结果表明,土氡测量探测晚更新世(Q3)和全新世(Q4)隐伏断层具有较好的效果,可以作为海口市活动断层探测一种有效手段。     

Analysis of resistance factors for LRFD of soil nail pullout limit state using default FHWA load and resistance models

Abstract

Resistance factors for load and resistance factor design (LRFD) of pullout limit state of both permanent and temporary soil nails are calibrated against a wide design space using the current Federal Highway Administration (FHWA) nail load and resistance models. The calculated resistance factors were shown to scatter broadly among design scenarios that differ in wall face batter, soil friction angle, nail ultimate bond strength, and surcharge live load. An important lesson learned from the analysis results is that the current practice of using a single resistance factor for LRFD of nail pullout limit state could not result in uniform reliabilities across different design scenarios. Simple artificial neural network (ANN) models were developed for computation of resistance factors. Design examples demonstrated the ability of the ANN models in providing resistance factors that yield satisfactory and consistent reliabilities in different nail pullout designs.