1,721,192 research outputs found
Effect of sampling locations on reliability of pile groups
No full textGeotechnical site investigations represent an imperative prerequisite in the pursuit of reliable foundation designs. However, site investigations are frequently restricted to a limited number of locations owing to the constraints imposed by budget and time considerations, thereby potentially yielding a range of adverse consequences. Hence, the development of an efficient site investigation plan - one that optimally selects the number and location of tests, is important to gain adequate information for a given cost. This paper proposes a framework to identify optimal investigation locations by minimizing the probability of erroneous decisions (i.e., error types I and II). A pile group was adopted for the demonstration. The best locations corresponding to various numbers of tests were identified based on the derived probabilities of type I and II errors.</p
Advancing V estimation from CPTu for engineering practice: a data-driven approach
No full textShear wave velocity, V s, is a critical parameter for offshore site characterisation to estimate the small strain shear modulus, which is essential for subsequent geotechnical designs. Direct measurements of V s are often sparse due to time and resource constraints, while indirect estimations of V s based on empirical correlations can exhibit significant errors. This study presents the performance of 125 models with various combinations of standard piezocone tests (CPTu) input features (e.g., depth, z; sleeve friction resistance, f s; corrected cone tip resistance, q t; and pore pressure at the shoulder of the cone, u 2), CPTu and V s data pairing methods, and prediction techniques (support vector regression (SVR), random forest regression (RFR), extreme gradient boosting regression (XGBR), deep neural network (DNN) and multiple linear regression (MLR)). To do this, we compile a seismic piezocone test (SCPTu) database from onshore and offshore sites across the globe (Netherlands, Austria, Germany, Nepal, and Taipei) and consider five different methods for pairing CPTu data (resolution of 0.02 m) and V s data (resolution of 0.5 m and 1 m depending on the dataset). Two cases consider the more conventional downsampling of CPTu data to V s data. The remaining three methods consider augmented V s data to the resolution of CPTu measurements, to fully utilise all the CPTu data. Results indicate that data augmentation enhances predictive performance. Incorporating pore pressure as an input feature also improves model performance, particularly in cemented materials such as chalk. In contrast, the derived features have a negligible influence. The recommended model combines a DNN with four directly measured CPTu parameters (z,f s,q t,and u 2), and uses an augmentation method that assumes constant V s values within each V s interval. This model achieves a mean absolute error (MAE) of 37.3 m/s and a coefficient of determination (R 2) of 0.59.</p
Inferring semi-parametric Gaussian process model parameters for missing geotechnical data prediction
No full textData points in geotechnical site investigation data (i.e., CPT data) may be missing sometimes due to various reasons. This study proposed to use a semi-parametric Gaussian process regression (GPR) method for predicting missing data in geotechnical testing results. Semi-parametric GPR divides the spatial data into the trend function, spatial residual, and measurement errors. Compared with conventional GPR method, semi-parametric GPR enhances model interpretability and accuracy. However, this involves challenges in estimating the parameters in the model. Conventional GPR applications infer the model parameters based on maximum a posteriori (MAP) estimation. However, this method can only provide a point estimation of the model parameters. Point estimation may be trapped by a local optimum result. This study utilizes the Hamiltonian Monte Carlo (HMC) method to get the full posterior distribution of the model parameters. MAP and HMC methods are both applied to infer the model parameters based on a synthetic CPT data set. The performances of both methods are compared with the true model values. The results show that the model parameters estimated from the HMC are more reliable.</p
Deep Bayesian survival analysis of rail useful lifetime
No full textReliable estimation of rail useful lifetime can provide valuable information for predictive maintenance in railway systems. However, in most cases, lifetime data is incomplete because not all pieces of rail experience failure by the end of the study horizon, a problem known as censoring. Ignoring or otherwise mistreating the censored cases might lead to false conclusions. Survival approach is particularly designed to handle censored data for analysing the expected duration of time until one event occurs, which is rail failure in this paper. This paper proposes a deep Bayesian survival approach named BNN-Surv to properly handle censored data for rail useful lifetime modelling. The proposed BNN-Surv model applies the deep neural network in the survival approach to capture the non-linear relationship between covariates and rail useful lifetime. To consider and quantify uncertainty in the model, Monte Carlo dropout, regarded as the approximate Bayesian inference, is incorporated into the deep neural network to provide the confidence interval of the estimated lifetime. The proposed approach is implemented on a four-year dataset including track geometry monitoring data, track characteristics data, various types of defect data, and maintenance and replacement (M&R) data collected from a section of railway tracks in Australia. Through extensive evaluation, including Concordance index (C-index) and root mean square error (RMSE) for evaluating model performance, as well as a proposed CW-index for evaluating uncertainty estimations, the effectiveness of the proposed approach is confirmed. The results show that, compared with other commonly used models, the proposed approach can achieve the best concordance index (C-index) of 0.80, and the estimated rail useful lifetimes are closer to real lifetimes. In addition, the proposed approach can provide the confidence interval of the estimated lifetime, with a correct coverage of 81% of the actual lifetime when the confidence interval is 1.38, which is more useful than point estimates in decision-making and maintenance planning of railroad systems.Railway Engineerin
Advancing spatial-temporal rock fracture prediction with virtual camera-based data augmentation
No full textPredicting rock fractures in unexcavated areas is a critical yet challenging aspect of geotechnical projects. This task involves forecasting the fracture mapping sequences for unexcavated rock faces using the sequences from excavated ones, which is well-suited for spatial–temporal deep learning techniques. Fracture mapping sequences for deep learning model training can be achieved based on field photography. However, the main obstacle lies in the insufficient availability of high-quality photos. Existing data augmentation techniques rely on slices taken from Discrete Fracture Network (DFN) models. However, slices differ significantly from actual photos taken from the field. To overcome this limitation, this study introduces a new framework that uses Virtual Camera Technology (VCT) to generate “virtual photos” from DFN models. The external (e.g., camera location, direction) and internal parameters (e.g., focal length, resolution, sensor size) of cameras can be considered in this method. The “virtual photos” generated from the VCT and conventional slicing method have been extensively compared. The framework is designed to adapt to any distribution of field fractures and camera settings, serving as a universal tool for practical applications. The whole framework has been packaged as an open-source tool for rock “photos” generation. An open-source benchmark database has also been established based on this tool. To validate the framework's feasibility, the Predictive Recurrent Neural Network (PredRNN) method is applied to the generated database. A high degree of similarity is observed between the predicted mapping sequences and the ground truth. The model successfully captured the dynamic changes in fracture patterns across different sections, thereby confirming the framework's practical utility. The source code and dataset can be freely downloaded from GitHub repository (https://github.com/GEO-ATLAS/Rock-Camera).</p
含软弱夹层的强风化泥岩强度及破坏模式试验研究
No full textIn order to investigate the effects of weak interlayer on slope instability, theoretical and experimental studies on the strength and the failure mode of rock containing weak interlayer were conducted. Three types of triaxial specimens of strongly weathered mudstone, muddied interlayer, and mudstone with the weak interlayer were prepared by using the core specimens of a slope borehole, and the triaxial consolidation undrained shear tests were conducted, respectively. Combined with the theoretical analysis, the strengths and failure modes of the strongly weathered mudstone, the muddied interlayer and the mudstone with weak interlayer were studied, and the influence laws of the interlayer angle and the confining pressure on the strengths and failure modes of specimens were revealed. The results show that the stress-strain curves of the strongly weathered mudstone and the muddied interlayer are strain-softening type and strain-hardening type, respectively. There exists a critical inclination angle range for specimens containing the weak interlayer. When the interlayer angle is within the range, the failure surface occurs in the weak interlayer. When the interlayer angle is outside the range, specimens undergo global shear damage. When the confining pressure exceeds 0.4 MPa, the confining pressure has no effect on the failure mode of specimens containing the weak interlayer.</p
Updating reliability of pile groups with load tests considering spatially variable soils
No full textThis paper proposes a rigorous framework to update the reliability of pile groups based on load tests. The proposed approach enables the consideration of the spatial variability of soils, which is disregarded in previous studies. To achieve this, the random finite difference method (RFDM) is utilised to assess the group efficiency, individual pile capacities, and the correlation between individual pile capacities in spatially variable soils. Subsequently, Bayes’ theorem is employed to update individual pile capacities based on load test results, taking into account the correlation between individual pile capacities. Finally, the reliability of pile groups is evaluated based on the group efficiency and updated individual pile capacities. An axially loaded pile group in undrained clays is utilised for demonstration. Results indicate that neglecting the spatial variability of soils may lead to unrealistic assessments of the reliability of pile groups. Specifically, in cases where all piles fail, the ignorance of spatial variability results in an overconservative design. Conversely, in cases where one or more piles pass, it leads to an unconservative design.</p
Study on visualization and failure mode of model test of rock-socketed pile in soft rock
No full textComputed tomography (CT) technology is a kind of nondestructive image reconstruction technology. CT visualization technology is introduced into the physical model test of a single pile, which can visualize a rock-socketed pile in soft rock under different pile top loads. In this article, the visualization process of a model test of a rock-socketed pile in soft rock is carried out by medical CT. The implementation procedure, the composition of the device, and the technical requirements of the test equipment, as well as the CT scanning operation of nondestructive visualization technique of the single pile model test, are introduced in detail. The influence of a marked layer setting, side wall effect, size of the model cylinder, and model pile on the visualization effect of a single pile model test is discussed. The feasibility and reliability of the model test visualization technology of a rock-socketed pile in soft rock are demonstrated by examples, and the failure mode of rock-socketed pile in soft rock is intuitively revealed to be the spherical cavity expansion mode of the pile end.</p
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