Professor Jinsong Huang

It is of great significance to explore the influences of the spatial resolution and the proportion of model training and testing dataset on the uncertainties of landslide susceptibility prediction(LSP). Taking the landslides in Ningdu County of Jiangxi Province as examples, the frequency ratios of various environmental factors under different spatial resolutions(15, 30, 60, 90 and 120 m) are firstly calculated. Then, the landslide and non-landslide samples are divided into different model training and testing datasets with the proportions of 9/1, 8/2, 7/3, 6/4 and 5/5, and the model input and output variables under 25 combined conditions are obtained. Furthermore, these input and output variables are imported into the Support Vector Machine(SVM) and Random Forest(RF) models to carry out LSP. Finally, the uncertainties of LSP modeling under the 25 combined conditions are discussed using the accuracy assessment as well as the distribution characteristics of landslide susceptibility indexes. The results show that the landslide susceptibility accuracy predicted by the RF model under the spatial resolution of 15 m and training and testing dataset proportion of 9: 1 is the highest, and that the more important environmental factors under each condition are elevation, slope and topographic relief, etc. With decreasing the spatial resolution and/or the proportion of training and testing dataset, the LSP accuracies of both SVM and RF models decrease gradually, and the mean values of landslide susceptibility indexes increase with a decrease of the corresponding standard deviation value. For all combined conditions, as the spatial resolutions and the proportions of training and testing dataset decrease, the LSP accuracies decrease gradually while the corresponding uncertainties increase. It is also indicated that the LSP accuracy of the RF model is better than that of the SVM model under various combined conditions, and that the influence of the spatial resolution on the RF model is significantly greater than that of the proportion of training and testing dataset while there is little difference between the effects of the two factors on the SVM model.

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation. This study presents a machine learning approach based on the C5.0 decision tree (DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data (70% landslide pixels) and validation data (30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model. Susceptibility zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC (area under the receiver operating characteristic (ROC) curve) of the proposed model was the highest, reaching 0.88, compared with traditional models (support vector machine (SVM) = 0.85, Bayesian network (BN) = 0.81, frequency ratio (FR) = 0.75, weight of evidence (WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km2 and 0.88/km2, respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total area. Our results indicate that the distribution of high susceptibility zones was more focused without containing more ¿stable¿ pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices.

This paper aims to explore the influences of different attribute interval numbers (AINs) in the frequency ratio (FR) analysis of continuous environmental factors and the influences of different data-based models on the uncertainties of landslide susceptibility prediction (LSP). Taking Ningdu County of China as study area, 446 landslides and nine environmental factors are first acquired. Then the FR values of environmental factors under 6 different AINs (4, 6, 8, 12, 16 and 20) and 6 different data-based models (FR model, grey relational degree (GRD), logistic regression (LR), multilayer perceptron (MLP), C5.0 decision tree (C5.0 DT) and random forest (RF)) are set to 36 different conditions. Finally, the LSP results with uncertainties under all conditions are discussed. Results show that: 1) For a certain model, the LSP accuracy gradually increases with the AINs increasing from 4 to 8, and then the increase rate decreases until the accuracy is stable with the AINs increasing from 8 to 20; 2) For a certain AIN, the LSP accuracy of RF is higher than that of C5.0 DT, followed by the MLP, LR, FR and GRD; 3) The LSP accuracy is highest under an AIN of 20 and RF and is satisfied under an AIN of 8 and RF, while is the lowest under an AIN of 4 and GRD; 4) The landslide susceptibility indexes (LSIs) under AINs of 4, 6 and 12 are significantly different from the other AINs, and the LSIs calculated by the C5.0 DT and RF are significantly different compared to the other models; 5) The mean values and standard deviations of LSIs calculated by the MLP, C5.0 DT and RF models are relatively smaller and larger, respectively, than those of the other models, indicating that the LSIs calculated by these models are more consistent with the actual landslide distribution features.

The loss of effective stress due to increasing excess pore pressure that results in the upward migration of soil particles, that is, subgrade fluidization and mud pumping, has been a critical issue for railways over many years. Traditional methods such as experimental and analytical approaches can capture macroscopic quantities such as the hydraulic conductivity and critical hydraulic gradient, but they have many limitations when microscopic and localized behavior must be captured. This paper, therefore, presents a novel numerical approach where the microscopic properties of fluid and particles can be better captured when the soil is subjected to an increasing hydraulic gradient. While particle behavior is simulated using the discrete element method (DEM), the fluid dynamics can be described in greater detail using the lattice Boltzmann method (LBM). The mutual LBM-DEM interaction is carried out, so the particle and fluid variables are constantly updated. To validate this numerical method, laboratory testing on a selected subgrade soil is conducted. The results show that the numerical method can reasonably predict the coupled hydraulic and soil fluidization aspects, in relation to the experimental data. Microscopic properties such as the interstitial fluid flowing through the porous spaces of the soil are also captured well by the proposed fluid-particle coupling approach.

Estimating the shear strength of large in situ rock discontinuities is non-trivial because of the multiscale nature of roughness and the fact that only a very limited extent of discontinuity morphology is visible along traces. Recently, a novel stochastic method based on random field theory and Monte Carlo semi-analytical estimation of shear strength was proposed. The method was validated at laboratory scale and its application to one large natural surface showed that it has the potential to bypass the scale effect. However, a critical issue was reported by the authors of the study: it was found that the random field approach used could not generate the correct distribution of gradients on the simulated surfaces, which translates into an inaccurate prediction of shear strength. The authors had to manually adjust the input of the model to achieve a satisfactory prediction. This paper presents a new multiscale approach using random field theory, which now allows a rigorous generation of large synthetic 3D surfaces with controlled distribution of asperity heights and gradients from the profile of a 2D fracture trace (as might be visible in a rock face) referred to as a seed trace. Each seed trace is first decomposed into three daughter profiles corresponding to three levels of roughness. The statistics of each daughter profile form the input of the random field model at each scale level, allowing synthetic daughter surfaces to be created at each scale level. The synthetic daughter surfaces are then superimposed to obtain a composite rough surface comprising three levels of roughness. The approach was successfully validated with 25 input seed traces, coming from 5 different natural surfaces. This rigorous multiscale approach is essential to apply the stochastic method that was recently developed to predict the shear strength of large in situ discontinuities.

Piezocone penetration test (CPTU) and multichannel analysis of surface waves (MASW) are common methods for ground investigation. Integrating the data from the abovementioned two methods for accurate characterization of soil profiles is a critical and difficult issue. This paper proposes an approach for integrating the geotechnical and geophysical testing data based on Bayesian theory. The proposed approach is validated by a synthetic example, and is further applied to the Ballina site, Australia for integrating the site data and characterizing the profiles of soil properties. The results indicate that the updated random field after integrating the CPTU and MASW data provides an accurate and concise description of spatial distribution of the soil parameter throughout the two dimensional (2D) sub-region. When using the CPTU data only, the soil parameters at areas where CPTU is not performed can be inferred, and the local uncertainties of soil profiles near CPTU are reduced. When integrating the 1D CPTU and 2D MASW data, the characterized soil profile has good agreement with the "true" profile, and the uncertainties of soil profiles in the whole sub-region are further reduced. In the Ballina case, the uncertainties of 2D soil profiles are reduced when increasing the integrated site data, and the "weak" areas are effectively identified.

Stability analysis and design of soil slopes are a classic problem in geotechnical engineering. The current commonly-used deterministic analysis (e.g., single factor of safety) approach does not quantify the influences of various uncertainties in slope engineering, while the probabilistic analysis approach is time-consuming because it often requires performing multiple rounds of reliability analyses. A slope model reconstruction method that can well adapt to different slope angles is proposed. The reliability-based design of slope angles for spatially varying slopes based on a small amount of test data is carried out using the inverse first order reliability method. To validate the effectiveness of the proposed method, a representative sandy slope is taken as an example, to conduct the reliability-based design of slope angles. The results indicate that the proposed method can obtain a design scheme of slope angle based on a small amount of test data, which is well consistent with engineering practice. It thereby provides an effective tool for the reliability-based design of slope angles for spatially varying slopes. For the sandy slope in this study, an optimized slope angle that achieves various target probabilities of failure can be obtained after 4 or 5 iterative calculations. In contrast, the deterministic analysis method will obtain a biased design scheme since it cannot quantitatively account for the influences of multiple sources of uncertainties in the slope engineering. To yield a target probability of failure of 1×10-4 which is often acceptable for stability evaluation of slopes, the slope angle of the sandy slope designed using the proposed method should be smaller than 14.13°. By contrast, the slope angle designed using the deterministic analysis approach differs significantly from that designed using the proposed method.

Loess fill slopes are vulnerable to heavy rainfall because of water sensitivity and collapsibility of loess. Studies on the failure mode and mechanism of loess fill slopes are limited and incomplete. In this study, a laboratory flume test is carried out to simulate the failure mode of loess fill slope by monitoring and analyzing its soil hydrological and mechanical parameters including volumetric water content, pore water pressure and horizontal earth pressure. The results show that under continuous rainfall, loess fill slope fails in a backward retrogressive failure mode including gully erosion, partial failure, slope toe failure, central slope failure and top slope failure stages. The failure mechanism of each failure stage has been explained based on the variation of slope hydrological and mechanical conditions with the rainwater infiltration. The evolution processes of the fissures and its effects on the slope failure have been investigated. It is revealed that fissures play an important role in failing the slope by generating preferential flow. Finally, some engineering measures are recommended for the prevention of loess fill slope failure.

Development of large scale solar farms supported by large numbers of short piles has created new challenges for engineers to address. Solar arrays are highly flexible structures and the piles can be designed to move to enable more cost effective design. The structural reliability of the above-ground pile can be assessed and probabilities of failure for different section sizes calculated. Economic analysis incorporating capital cost and whole-of-life maintenance cost can be performed to work out whether adopting smaller section sizes provide the best cost outcome. Assessment of pile movements using Monte-Carlo calculations, unsaturated soil mechanics and updating material parameters with suction have been performed. The results show that soil movements are typically larger than pile movements and that soil can slip past the pile with no pile movement when the limiting conditions occur. The results also highlight that the largest soil and pile movements occur infrequently as a result of extreme wetting or drying conditions. Structural reliability analyses showed that correlating wind speed and direction results in a lower probability of failure than if wind load is considered to be uncorrelated with wind direction. The outcomes of the assessment were sensitive to the adopted probabilistic model for pile durability. The main limitation of the analyses is that there is limited information in the literature relating to the types of probability distributions and their input parameters. This adds uncertainty to the stochastic analysis.

Only the spatial variability of soil shear strength parameters is taken into account in most slope reliability analysis of embankment dams, whereas the spatial variation of unsaturated hydraulic model parameters is ignored.This paper develops a non-intrusive stochastic analysis method for evaluation of slope reliability of embankment dams, wherein the influence of the spatial variability of hydraulic model parameters on the slope reliability of embankment dam is incorporated.The phenomenon that the probability of slope failure decreases with increasing the coefficient of variation (COV) of hydraulic model parameter n is explained.The results indicate that the developed method has much higher computational efficiency in comparison with the direct Monte-Carlo simulation method.The probability of slope failure is positively correlated with the COVs of the cohesion, friction angle and saturated hydraulic conductivity.The variability of the cohesion affects the slope reliability the most, while that of the saturated hydraulic conductivity has the least effect on the slope reliability.In addition, the probability of slope failure almost keeps unchanged as the COV of hydraulic model parameter a varies.It is also interesting to note that the probability of slope failure decreases as the COV of hydraulic model parameter n increases.This is because more realization values of n that are close to the lower bound of 1.05 can be generated when a larger COV of n is used.As a result, a smaller hydraulic conductivity and a lower flow rate that passes through the dam body are induced.Consequently, the safer slope of embankment dam is achieved.

Conventional planning of maintenance and renewal work for railway track is based on heuristics and simple scheduling. The railway industry is now collecting a large amount of data with the fast-paced development of sensor technologies. These data sets carry information about the conditions of various components in railway track. Since just before the beginning of the 21st century, data-driven models have been used in the predictive maintenance of railway track. This study presents a systematic literature review of data-driven models applied in the predictive maintenance of railway track. A taxonomy to classify the existing literature based on types of models and types of applications is provided. It is found that applying the deep learning methods, unsupervised methods, and ensemble methods are the new trends for predictive maintenance of railway track. Rail geometry irregularity, rail head defect, and missing rail components detection were the top three most commonly considered issues within the application of data-driven models. Prediction of rail breaks has received increasing attention in the last four years. Among these data-driven model applications, the collected data types are the most critical factors which affect selecting suitable models. Finally, this study discusses upcoming challenges in the predictive maintenance of railway track.

To study the uncertainties of a collapse susceptibility prediction (CSP) under the coupled conditions of different data-based models and different connection methods between collapses and environmental factors, An¿yuan County in China with 108 collapses is used as the study case, and 11 environmental factors are acquired by data analysis of Landsat TM 8 and high-resolution aerial images, using a hydrological and topographical spatial analysis of Digital Elevation Modeling in ArcGIS 10.2 software. Accordingly, 20 coupled conditions are proposed for CSP with five different connection methods (Probability Statistics (PSs), Frequency Ratio (FR), Information Value (IV), Index of Entropy (IOE) and Weight of Evidence (WOE)) and four data-based models (Analytic Hierarchy Process (AHP), Multiple Linear Regression (MLR), C5.0 Decision Tree (C5.0 DT) and Random Forest (RF)). Finally, the CSP uncertainties are assessed using the area under receiver operation curve (AUC), mean value, standard deviation and significance test, respectively. Results show that: (1) the WOE-based models have the highest AUC accuracy, lowest mean values and average rank, and a relatively large standard deviation; the mean values and average rank of all the FR-, IV-and IOE-based models are relatively large with low standard deviations; meanwhile, the AUC accuracies of FR-, IV-and IOE-based models are consistent but higher than those of the PS-based model. Hence, the WOE exhibits a greater spatial correlation performance than the other four methods. (2) Among all the data-based models, the RF model has the highest AUC accuracy, lowest mean value and mean rank, and a relatively large standard deviation. The CSP performance of the RF model is followed by the C5.0 DT, MLR and AHP models, respectively. (3) Under the coupled conditions, the WOE-RF model has the highest AUC accuracy, a relatively low mean value and average rank, and a high standard deviation. The PS-AHP model is opposite to the WOE-RF model. (4) In addition, the coupled models show slightly better CSP performances than those of the single data-based models not considering connect methods. The CSP performance of the other models falls somewhere in between. It is concluded that the WOE-RF is the most appropriate coupled condition for CSP than the other models.

The attribute interval numbers (AIN) in the frequency ratio analysis of continuous environmental factors and the landslide susceptibility model are two important uncertainties affecting the results of landslide susceptibility prediction (LSP). To study the effects of the two uncertain factors on the change rules of LSP, taking Shangyou County of Jiangxi Province, China, as study area, the AIN values of the continuous environmental factors are respectively set to be 4, 8, 12, 16 and 20. Meanwhile, five different data-based models (analytic hierarchy process (AHP), logistic regression (LR), BP neural network (BPNN), support vector machines (SVM) and random forests (RF)) are selected as LSP models. Hence, there are a total of 25 types of different calculation conditions for LSP. Finally, the accuracy and uncertainties of LSP are analyzed. The results show that: (1) For a certain model, the LSP accuracy gradually increases with the AIN value increasing from 4 to 8, then slowly increases to a stable level with AIN increasing from 8 to 20; (2) For a certain AIN, the LSP accuracy of the RF model is higher than SVM, followed by the BPNN, LR and AHP models; (3) Under all the 25 calculation conditions, the prediction accuracy of AIN=20 and RF model is the highest while that of AIN=4 and AHP model is the lowest, and the modeling efficiency and accuracy of AIN=8 and RF model are very high;(4) The landslide susceptibility indexes calculated by the higher AIN and more advanced machine learning models are more consistent with the actual distribution features of landslide probability and have relatively lower uncertainties. It can be concluded that an efficient and relatively accurate LSP model can be built under the condition of AIN value of 8 and RF model.

Among the machine learning models used for landslide susceptibility indexes calculation, the support vector machine (SVM) is commonly used; however, SVM is time-consuming. In addition, the non-landslide grid cells are selected randomly and/or subjectively, which may result in unreasonable training and validating data for the machine learning models. This study proposes the self-organizing-map (SOM) network-based extreme learning machine (ELM) model to calculate the landslide susceptibility indexes. Wanzhou district in Three Gorges Reservoir Area is selected as the study area. Nine environmental factors are chosen as input variables and 639 investigated landslides are used as recorded landslides. First, an initial landslide susceptibility map is produced using the SOM network, and the reasonable non-landslide grid cells are subsequently selected from the very low susceptible area. Next, the final landslide susceptibility map is produced using the ELM model based on the recorded landslides and reasonable non-landslide grid cells. The single ELM model which selects the non-landslide grid cells randomly, and the SOM network-based SVM model are used for comparisons. It is concluded that the SOM-ELM model possesses higher success and prediction rates than the single ELM and SOM-SVM models, and the ELM has a considerably higher prediction efficiency than the SVM.

How to efficiently assess the system reliability of rock slopes is still challenging. This is because when the probability of failure is low, a large number of deterministic slope stability analyses are required. Based on Subset simulation, this paper proposes an efficient approach for the system reliability analysis of rock slopes. The correlations among multiple potential failure modes are properly accounted for with the aid of the ¿max¿ and ¿min¿ functions. A benchmark rock slope and a real engineered rock slope with multiple correlated failure modes are used to demonstrate the effectiveness of the proposed approach.

It is important to monitor the displacement time series and to explore the failure mechanism of reservoir landslide for early warning. Traditionally, it is a challenge to monitor the landslide displacements real-timely and automatically. Globe Position System (GPS) is considered as the best real-time monitoring technology, however, the accuracies of the landslide displacements monitored by GPS are not assessed effectively. A web-based GPS system is developed to monitor the landslide displacements real-timely and automatically in this study. And the discrete wavelet transform (DWT) is proposed to assess the accuracy of the GPS monitoring displacements. Wangmiao landslide in Three Gorges Reservoir area in China is used as case study. The results show that the web-based GPS system has advantages of high precision, real-time, remote control and automation for landslide monitoring; the Root Mean Square Errors of the monitoring landslide displacements are less than 5 mm. Meanwhile, the results also show that a rapidly falling reservoir water level can trigger the reactivation of Wangmiao landslide. Heavy rainfall is also an important factor, but not a crucial component.

Spudcan foundations are often pushed into a spatially varying non-homogeneous seabed to provide bearing capacity for a mobile jack-up platform. The natural variability of soil properties coupled with the complexity of loading conditions make determining the bearing capacity of spudcan foundations a challenging problem. A random finite element method is established to investigate the bearing capacity of a spudcan foundation embedded in a spatially varying clayey seabed when subjected to vertical, horizontal and moment loadings. A criterion is proposed for determining the characteristic value of the shear strength for the random seabed. Results indicate that the spatial variability in the clayey seabed significantly reduces the bearing capacity of a spudcan foundation. This reduction is more significant in the vertical bearing capacity than in the horizontal and moment bearing capacities. The mean bearing capacity is smaller for the clay with larger coefficient of variation of undrained shear strength. A characteristic value of mean minus a standard deviation of the undrained shear strength is capable to ensure the probability of failure is not greater than 5%. This study provide an evaluation method for the spatial variability effect of a clayey seabed, paving the way for a cost-effective design of spudcan foundations.

Pile load tests are used to refine designs and for quality assurance. They can also be used to verify the reliability of piles and pile groups. Stochastic methods have previously been developed to verify the reliability of single piles. A general stochastic method to verify the reliability of pile groups is developed in this paper. The method can be used to assess the reliability of groups where pile tests have been conducted to the ultimate capacity, to below the ultimate capacity but exceeding specified capacity, and where pile tests fail to achieve the specified capacity. In the latter case, the method allows decisions to be made as to whether the reliability of the entire pile group is satisfactory or whether additional piles need to be installed.

The limit analysis and the finite element method are powerful tools for analysing the bearing capacity of foundations. Previous research mainly focused on the foundations in uniform soils. In realistic conditions, soil properties are always varying spatially due to complex physical, chemical, and biological process in earth evolution. This paper investigates the bearing capacity and failure mechanism of footings buried at various depths in clays with spatially variable distribution of undrained shear strength using the lower bound limit analysis, the upper bound limit analysis, and the finite element analysis. Results show that the bearing capacity increases with increasing buried depths in spatially random soils, which is the same as in the uniform soils. The bearing capacity factors calculated using the finite element method, the lower bound limit analysis, and the upper bound limit analysis for a footing in spatially varied soils are all smaller than the corresponding values in uniform soils. The majority of the bearing capacity factors obtained from the finite element method is bounded by those obtained from the lower bound and the upper bound limit analysis. The shear planes show a clearly unsymmetrical manner in spatially varied soils using the three methods, which is different from the symmetrical shear plane in uniform soils.

After a geotechnical design has been developed, it is common to monitor performance during construction using the observational method by Peck (published in 1969). The observational method is a process where data are collected and geotechnical models updated, allowing timely decisions to be made with respect to risk and opportunity by asset owners or contractors. The observational method is similar to the mathematical formulation for Bayesian updating of material parameters based on measurements. A proof of concept study has been performed to assess the potential for Bayesian updating to be combined with the observational method to allow timely and accurate decision-making during construction of embankments on soft soils. The method was able to converge to an accurate solution prior to 50% consolidation assuming small measurement errors. It is also demonstrated that confidence in the predicted settlement is relatively low at the prior ¿design¿ stage and rapidly increases with three or four measurements spaced over time during the posterior ¿construction¿ phase.

The mechanical properties of dry soil mix (DSM) columns can be highly variable. Variability can be accounted for in the construction specification for deterministic design and directly in reliability based design. Design methods and specifications to date adopt simplifications that do not take the variability of the columns fully into account. This paper uses both simple and advanced probabilistic methods to assess the performance/failure and system redundancy of dry soil mix columns. Reliability-based design methods and examples are given for the design of column strength and the adjustment of the column spacing to achieve a target probability of unacceptable performance or failure. An acceptance criteria chart is developed. The pull-out resistance tests on the DSM columns constructed for the Ballina Bypass motorway construction project in NSW Australia are compared to the chart to provide guidance with respect to acceptance criteria required to achieve the desired performance.

Rainfall induced landslides vary in depth and the deeper the landslide, the greater the damage it causes. This paper investigates, quantitatively, the risk of rainfall induced landslides by assessing the consequence of each failure. The influence of the spatial variability of the saturated hydraulic conductivity and the nature of triggering mechanisms on the risk of rainfall-induced landslides (for an infinite slope) are studied. It is shown that a critical spatial correlation length exists at which the risk is a maximum and the risk is higher when the failure occurs due to a generation of positive pore water pressure. © 2014 Elsevier B.V.

In the study of landslides, it is generally assumed that an impermeable boundary exists at a certain depth and failure occurs at this boundary. In reality this is not always the case and failures can occur at any depth. This paper aims to study the effect of boundary conditions on landslides, using a series of seepage and stability analyses performed over a range of rainfall intensities, and for different failure mechanisms, by studying the failure time and depths corresponding to fully drained, partially drained, and impermeable boundaries. It is shown that these conditions can significantly affect the occurrence and depth of rainfall-induced landslides. © 2014 Elsevier Ltd.

The observational method is one of the most successful processes in geotechnical engineering. Performance monitoring data are the most reliable information that engineers can use to predict future performance of geotechnical projects. This paper presents two examples where Bayesian statistical methods can be used for the prediction of future performance. The first example is to update the capacity of piles using load test results. The second example is to update embankment settlement predictions when field settlement monitoring data are available.

This paper investigates the soil properties, stratigraphy and spatial variability of the soils at the National Field Test Facility in Ballina based on extensive CPTU tests. The soil profile in this site consists of an alluvial crust over a relatively weaker layer of clay and underlain with a layer of sand and Pleistocene age stiff clay. The measured cone penetration resistance, sleeve friction and pore pressure for 26 CPTUs are presented along with the deployment of the CPTUs. The spatial variability in both vertical and horizontal direction of each layer of soils is explored based on the CPTU tests. An exponential autocorrelation function is found to best fit the autocorrelation coefficients. The scale of fluctuation in the vertical direction is 0.04 m in the alluvial crust layer, which is much smaller than that in the underlying clay layer, 0.15 m. The reason is that the clay was deposited under lower energy conditions compared to the more granular crust layers. The horizontal scale of fluctuation is 9.21 m in the alluvial crust layer and 4.92 m in the clay layer.

On the basis of new normative principles, criterions and methods such as Design specification for concrete gravity dams and Specifications for seismic design of hydraulic structures, we calculated the normal use ultimate strength and the carrying capacity ultimate strength of Dachaoshan dam according to the finite element method. The result indicates that the gravity dam is safe and credible; the dam's structure totally reaches the reliability level stated by criterions.

Two methods for estimating the ultimate bearing capacity of gravity dam are studied. The first method is to see whether or not the yield area across the dam. The second method called energy method is to define the ultimate state of the dam. Aiming at a real project, the example is given. It is found that the second method usually gets the lower result.

The paper presents a finite element model for strength analyses of a rotational composite shell under axial compression and internal pressure. The characteristics of stress distribution, stress locations and loads of failure are determined according to the model. The model is also developed to calculate the stability of the shell under axial compression, external pressure and combination of the both. Experiments are completed for studying the strength of the shell under axial compression and internal pressure and the stability of the shell under the combination of axial compression and external pressure.

A stability calculation method of laminated-composite dome structure is presented based on the modified-equivalent cylindrical shell model. The method is used to calculated the stabili ty of missile's dome under axial compression, external pressure and their combination respectively, and some experiments are completed for studying the stability of the dome under combined load. Finally, the relations between the dome's load of failure with characteristic size and laminated form are analyzed and discussed.

The stability of a trapdoor has long been an important benchmark solution in theoretical soil mechanics, and it is also of considerable practical interest in many geotechnical applications. It is well known that natural soils always exhibit spatial variability with the properties varying from point to point. This paper uses the Random Finite Element Method (RFEM) to investigate the influence of spatial variability on the limit loads for a shallow passive trapdoor embedded in a purely cohesive soil. RFEM is an advanced numerical tool for probabilistic geotechnical analysis which merges finite-element methodologies with random field theory in a Monto-Carlo framework. In the current parametric study, the mean undrained shear strength has been held constant while the coefficient of variation and spatial correlation length have been varied systematically. As might be expected, for trapdoors with low values of the coefficient of variation, the mean limit loads agree well with the results from deterministic analysis. For higher values of the coefficient of variation, the mean limit loads fall quite steeply. Failure is defined as occuring when the computed limit load is less than the deterministic solution based on the mean strength, reduced by an appropriate factor of safety. By interpreting the Monte-Carlo simulations in a probabilistic context, the probability of failure is assessed as a function of the factor of safety and the spatial variability of the soil. It is found, for example, that a factor of safety of 2.5 is required to avoid the probability of failure exceeding 5% for soils with strength variability within typical ranges.

Since the charts ofTaylor, it has beenwell knownthat the location of the critical failure mechanism in a homogeneous undrained clay slope goes either deep (tangent to a firm base) or shallow (through the toe) depending on whether the slope angle is, respectively, less than or greater than about 53°. When slopes are made up of variable soils however, these expectations no longer hold true for all cases. In this paper, the influence of random soil strength and slope angle on the location of the critical failure mechanism and probability of failure is examined using the Random Finite Element Method (RFEM). It is found following Monte-Carlo simulation, that there exists a critical value of slope angle above which it would be unconservative to assume high spatial correlation length and below which it would be conservative to assume high spatial correlation length. For ß>48°, both correlation length and slope angle have no influence on the proportion of toe failures. For slope angle lying between 20° and 40°, slopes that have higher correlation length give lower proportion of toe failures. Research into the critical mechanism location forms part of a broader study of slope failure risk, in which the consequences of failure are assumed to be more serious in a deep failure, because a greater volume of soil is affected. © 2015 Taylor & Francis Group, London.

Bearing capacity and failure mechanism of a buried footing in uniformsoils have been simulated using limit analysis and finite element analysis in the past decades. In realistic conditions, soil properties always vary spatially. This dramatically affects the failure mechanism of a footing and, in turn, its bearing capacity. This paper illustrates an investigation into the failure mechanism and bearing capacity of a vertically and centrally loaded footing embedded in spatially variable clayey soils using random lower bound limit analysis, random upper bound limit analysis and random finite element analysis. The footing was embedded to 4 times its width. Monte Carlo simulation was performed for 400 realizations of random fields of undrained shear strength. The majority of the bearing capacity factors obtained from the finite element method is bounded by those obtained from the lower bound limit analysis and the upper bound limit analysis, but more close to the upper bound results. A full-flow failure mechanism is observed for the deeply embedded footing in spatially variable soil. The shear path of the footing shows an unsymmetrical pattern, which results from the spatially variable and unsymmetrical random field of soil shear strength. © 2015 Taylor & Francis Group, London.

Rainfall induced landslides can vary in depth and deeper the landslide, greater is the damage it causes. This paper investigates, quantitatively, the risk of rainfall induced landslides by assessing the consequence of each failure. The influence of the spatial variability of the saturated hydraulic conductivity on the risk of landslides is studied. It is shown that a critical spatial correlation length exists at which the risk is a maximum. © 2015 Taylor & Francis Group, London.

In deep drilling operation, borehole collapse due to insufficient drilling fluid pressure and borehole fracturing as a result of excessive drilling fluid pressure are two major modes of borehole failure. The latter may result in severe loss of drilling fluid into the formation, resulting in potential well control issues with influx of high pressure fluid or gas from adjacent formation layers. To prevent drilling fluid loss, the fluid pressure must not exceed an upper limit, otherwise the borehole will fail in tension or fracture. However, if the fluid pressure is too low, the borehole may collapse or fail in compression. Drilling fluid pressure window is therefore typically set by the upper limit of tensile failure and the lower limit of compressive failure. If the stress state and rock strength require a lower limit which is close to the upper limit, then the drilling fluid pressure design needs to stay within a very narrow window and consequently the feasibility of drilling may be questioned. Based on rigorous mechanics principles, this paper shows that it is possible to experience shear failure due to increasing drilling fluid pressure even before it reaches the upper limit of tensile failure. This may mean a more restricting drilling fluid pressure window in drilling weaker rocks. Implications of near wellbore shear failure are also briefly discussed in the context of water injection design which relies on injection at high pressure. © 2015 Taylor & Francis Group, London.

The drilling induced tensile fractures can be observed from borehole image logs but they must be differentiated from natural fractures. This paper focuses on the understanding of hydraulically or artificially induced tensile fractures while drilling. We present an inversion method using rigorous principles of mechanics, to determine the in-situ formation stress state from observed induced tensile fractures. Contrary to common practices where only vertical or near vertical wells can be analysed, the present method is applicable to wellbores of all orientations. For a geological rock formation and area where the in-situ stress regime can be assumed to be similar, all the relevant borehole image logs can be included to provide information to yield the most probable subsurface in-situ stress state. The proposed inversion method directly solves for the in-situ stress states given any single set of observed tensile fracture location and orientation. It provides not only an estimate for the minimum horizontal stress magnitude and direction, but also the maximum horizontal stress magnitude which is usually very difficult to pin down. The resultant equations are non-linear and a simple numerical scheme is adopted for the solution. Although published data on borehole images of fractures with corresponding in-situ stress information are scarce, two observed field data from published papers are chosen for comparison.

The mechanical properties of natural materials such as rocks and soils vary spatially. This randomness is usually modelled by random field theory so that the material properties can be specified at each point in space. When these point-wise material properties are mapped onto a finite element mesh, discretization errors are inevitable. In this study, the discretization errors are studied and suggestions for element sizes in relation with spatial correlation lengths are given. © (2014) Trans Tech Publications, Switzerland.

The homogenized stiffness of geomaterials that are highly variable at the micro-scale has long been of interest to geotechnical engineers. The purpose of this study is to investigate the influence of porosity and void size on the homogenized or effective properties of geomaterials. A Random Finite Element Method (RFEM) has been developed enabling the generation of spatially random voids of given porosity and size within a block of geomaterial. Following Monte-Carlo simulations, the mean and standard deviation of the effective property can be estimated leading to a probabilistic interpretation involving deformations. The probabilistic approach represents a rational methodology for guiding engineers in the risk management process. The influence of block size and the Representative Volume Elements (RVE) are discussed, in addition to the influence of anisotropy on the effective Young's modulus. © 2014 Taylor & Francis Group, London.

Analytical probabilistic analysis and Monte Carlo simulation based on elasto-plastic Finite Element Method (FEM) on dry soil mix columns are presented. It is shown that analytical method is over conservative because it ignores the supports from adjacent columns. Probabilistic FEM analysis can provide more accurate predictions, and thus lead to more economic designs. Probabilistic FEM analyses show that the effects of adjacent columns can be destructive when applied load is close to the strength. The reliability of the system of columns is analyzed by setting residual strength to zero. Results show that close spacing has more safety margin than loose spacing. © 2014 Taylor & Francis Group, London.

Computational limit analysis provides a fast and convenient means of evaluating the stability or bearing capacity of geostructures. It is based on numerical optimization techniques and the latest trend is to use robust conic programming algorithms. The shortcoming, however, is that the types of problems covered by conic programming are not very general. In practice, this means that only criteria containing linear and quadratic terms (such as Drucker-Prager) or those that involve linear terms in the principal stresses (such as Mohr-Coulomb) can be considered. In the present paper this shortcoming is addressed. The idea is to maintain an efficient and robust conic programming algorithm as the main solution engine. Nonlinear criteria are then handled by a dynamic linearization procedure that involves a sequence of standard conic programming solutions in an iterative scheme that turns out to converge relatively rapidly.

Program PES (Probabilistic Engineered Slopes) provides a repeatable methodology allowing the user to perform a slope stability analysis on a one-sided and two-sided sloping structure using a deterministic or probabilistic approach. Program PES, in contrast with other deterministic or probabilistic classical slope stability methodologies, is cable of seeking out the critical failure surface without assigning a pre-defined failure surface geometry. The probabilistic approach of program PES applies the Random Finite Element Method (RFEM) by Griffiths and Fenton (1993) taking into account the soil spatial variability and allowing the use of different random fields to characterize the spatial variation of any material type. The methodology is compared against the probabilistic approach proposed by the program SLOPE/W version 7.14 (Geostudio Group, 2007), and demonstrates its potential for predicting probability of failure (pf) in non-homogeneous soil structures characterized by phreatic conditions and potential post-earthquake liquefiable conditions. The pf results obtained by program PES have proved that underestimating the influence that the soil material variability has on the computation of pf will lead to lower results of probability and underestimate of the risk of slope instability. Program PES capabilities could be used by the engineering practice to prioritize intervention activities within a risk context. © 2011 American Society of Civil Engineers.

The paper describes probabilistic analyses performed as part of a large expansion to an existing cement manufacturing plant. A raft supported by drilled shafts was proposed for the project, but during installation, significant slurry and concrete loss began to occur indicating numerous voids existed in what was previously considered competent limestone bedrock. Since the possibility of voids, especially at the shaft tip, could serious reduce the shaft capacity, a probabilistic Monte Carlo 3D finite element simulation was proposed for the most heavily loaded raft foundation. The purpose of the simulation was to determine the probability of adverse performance, giving guidance as to whether any remedial measures (e.g., additional structural elements or thickened raft) might be required. © 2011 ASCE.

Reliability tools have been applied to slope stability analysis more than any other geotechnical application on account of the readily understood concept of "probability of failure" as an alternative or complement to the traditional "factor of safety". Probabilistic slope stability methods in the literature are reviewed. Particular attention is focused on the ability of the methods to correctly model spatially varying soil properties. A benchmark slope is reanalyzed and conclusions reached about their suitability for meaningful and conservative prediction of slope reliability. © 2010 ASCE.

The computer program Probabilistic Engineered Slopes (PES), coded in FORTRAN.95, provides a repeatable methodology, which allows the user to perform a slope stability analysis on a one- and two-sided sloping structure, using a deterministic or probabilistic approach. The program PES, in contrast with other deterministic or probabilistic classical slope stability methodologies, is capable of seeking out the critical failure surface without assigning a predefined failure surface geometry. The probabilistic approach of PES applies the Random Finite Element Method (RFEM) by Griffiths and Fenton (1993) [1], taking into account the soil spatial variability and allowing the use of different random fields to characterize the spatial variation of any material type. The methodology is compared against the probabilistic approach proposed with the program SLOPE/W, version 7.14 (Geostudio Group, 2007) [2], and demonstrates its potential for predicting probability of failure (pf) in nonhomogeneous soil structures for given phreatic conditions and potential postearthquake liquefiable conditions. The pf results obtained by program PES have proved that underestimating the influence that the soil material variability has on the computation of pf will lead to unconservative results of probability and underestimate of the risk of slope instability. The program PES has capabilities that could be used by the engineering practice to prioritize intervention activities within a risk context, test the stability conditions of dams during modification phases, and help estimate the probability of failure in cases involving postearthquake liquefaction.

The computer program Probabilistic Engineered Slopes (PES), coded in FORTRAN.95, provides a repeatable methodology, which allows the user to perform a slope stability analysis on a one- and two-sided sloping structure, using a deterministic or probabilistic approach. The program PES, in contrast with other deterministic or probabilistic classical slope stability methodologies, is capable of seeking out the critical failure surface without assigning a predefined failure surface geometry. The probabilistic approach of PES applies the Random Finite Element Method (RFEM) by Griffiths and Fenton (1993) [1], taking into account the soil spatial variability and allowing the use of different random fields to characterize the spatial variation of any material type. The methodology is compared against the probabilistic approach proposed with the program SLOPE/W, version 7.14 (Geostudio Group, 2007) [2], and demonstrates its potential for predicting probability of failure (pf) in nonhomogeneous soil structures for given phreatic conditions and potential postearthquake liquefiable conditions. The pf results obtained by program PES have proved that underestimating the influence that the soil material variability has on the computation of pf will lead to unconservative results of probability and underestimate of the risk of slope instability. The program PES has capabilities that could be used by the engineering practice to prioritize intervention activities within a risk context, test the stability conditions of dams during modification phases, and help estimate the probability of failure in cases involving postearthquake liquefaction.

The paper investigates the probability of failure of 2-d and 3-d slopes using the Random Finite Element Method (RFEM). RFEM combines elastoplasticity with random field theory in a Monte-Carlo framework. It is found that 2-d probabilistic analysis, by implicitly assuming perfect spatial correlation in the third direction, may underestimate the probability of failure of slopes. © 2009 IOS Press.

The influence of a spatially random coefficient of consolidation on one-dimensional uncoupled consolidation has been studied using the Random Finite Element Method. The results of parametric studies are presented, which describe the effect of the standard deviation and correlation length of the coefficient of consolidation on output statistics relating to the overall "effective" coefficient of consolidation. Three "effective" coefficient of consolidation are considered, namely harmonic mean, the log time method and the root time method. Copyright ASCE 2008.

The study investigates the role of spatially random soil on the stability of infinite slopes with application to landslides and other geohazards. The influence of the shear strength mean, standard deviation and spatial correlation length on the probability of failure is thoroughly investigated through parametric studies. The results show that the traditional "first order second moment" approach to this problem is inherently unconservative, due to its inability to allow the failure mechanism to "seek out" the critical depth below ground surface, which is frequently not at the base of the soil layer. Copyright ASCE 2008.

The paper presents probabilistic studies that demonstrate the influence of spatially random soil properties on the stability of shallow landslides using random fields Results indicate that traditional "first order" methods are inherently unconservative when applied to limit analysis problems unless they allow the failure mechanism to "seek out" the most critical location.