14,444 research outputs found
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
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
Bayesian updating on resistance factors of H-Piles with axial load tests
No full textIn the Load and Resistance Factor Design (LRFD) of piles, several design codes recommend higher resistance factors if load tests are conducted. However, no information is provided on how these resistance factors are determined. In this paper, a probabilistic approach based on Bayes' theorem and the First Order Reliability Method (FORM) is proposed to calibrate resistance factors for different numbers of load tests and the corresponding test results. In addition, within-site variability, design methods, types of piles, and ground conditions can also be considered. The proposed approach applied to H-piles under axial load tests shows consistent results with current design codes. Results show that resistance factors are significantly increased even if only one positive test is observed among all the tests. For low variability sites, the differences of resistance factors between various design methods are significantly reduced if one or more tests are positive, while for high variability sites, the differences of resistance factors are only slightly decreased, indicating that design methods should be considered in the latter case. Most of the increase in resistance factors is achieved with a small number of tests. For β-Method used in clay sites, 80% of the increase in resistance factors is achieved with two, four and five consecutive positive tests are observed for low, medium and high variability sites, respectively.</p
Calibrating resistance factors of pile groups based on individual pile proof load tests
No full textPile load tests have been utilized to reduce the uncertainty of pile resistance, thus leading to a higher resistance factor used in the Load and Resistance Factor Design (LRFD). Previous studies have primarily focused on calibrating resistance factors for single piles based on load tests. This calibration hinges upon the resistance bias factor of single piles, defined as the ratio of measured resistance to predicted resistance. Due to the redundancy in the pile group system, it is conventionally assumed that if the individual piles within the group achieve a lower reliability index (e.g., 2.0–2.5), the pile group as a whole attains the target reliability index of 3. However, the approach is empirical as it does not consider system redundancy directly. Moreover, this empirical approach disregards the correlation between resistance bias factors of individual piles, which is inherently influenced by the spatial variability of soils. In this study, the random finite difference method (RFDM) is employed to evaluate the correlation between resistance bias factors of individual piles in spatially variable soils. The resultant correlation matrix is subsequentially employed in Bayes’ theorem to update resistance bias factors using individual pile load test results and their corresponding test locations. The updated resistance bias factors are then used for the direct calibration of resistance factors for pile groups within the framework of LRFD. A pile group subject to vertical loading in undrained clays is adopted for illustration. Comparative analyses between the proposed approach and the empirical approach demonstrate that the latter tends to overestimate the resistance factor. Furthermore, the proposed approach enables the determination of optimal locations for conducting subsequent load tests based on previous test results.</p
su-Based Shaft Friction Design Method and Evaluation for Pipe Pile
No full textThe shaft friction in clay is essential to bearing capacity of pipe piles. Reasonable selection of design methods and parameters is very important for offshore piles. This paper focuses on the current popular vertical load design methods based on undrained strength for pipe piles, especially for offshore piles. Based on the database, the accuracy and reliability of various pile design methods based on undrained strength su in clay were reviewed. The small-scale model tests were conducted to evaluate su-based methods. For the shortcomings of small-diameter piles in the database, a field test of full-scale offshore pile was carried out and analysed. By comparing the calculated and the measured capacity for each method, the reliability of various design methods is evaluated for large-diameter piles. For the design methods based on undrained strength, it demonstrates the importance and reliability of the determination of undrained strength parameters. In view of the vertical loading conditions of offshore large-diameter steel pipe piles, reasonable suggestions for design methods and parameters determination are given
Static and Dynamic Properties and Temperature Sensitivity of Emulsified Asphalt Concrete
No full textAsphalt concrete is a typical rheological material, which is hard brittle at low temperature and reflects soft plastic facture at high temperature; the temperature has a great influence on the mechanical properties of asphalt concrete. In order to eliminate the environmental pollution caused by hot asphalt construction, cationic emulsified asphalt can be used. This paper transforms the temperature control system for static and dynamic triaxial test equipment, which has achieved static and dynamic properties of emulsified asphalt concrete under different temperatures, and researched the temperature sensitivity of emulsified asphalt concrete materials including static stress-strain relationship, static strength, dynamic modulus of elasticity, damping ratio, and so on. The results suggest that (1) temperature has a great influence on the triaxial stress-strain relationship curve of the asphalt concrete. The lower the temperature, the greater the initial tangent modulus of asphalt concrete and the higher the intensity; the more obvious the softening trend, the smaller the failure strain of the specimen and the more obvious the extent of shear dilatancy. When the temperature is below 15.4°C, the temperature sensitivity of the modulus and strength is stronger significantly. (2) With the temperature rising, the asphalt concrete gradually shifts from an elastic state to a viscoelastic state, the dynamic modulus gradually reduces, and the damping ratio increases. When the temperature is above 15.4°C, the temperature sensitivity is obviously stronger for the dynamic elastic modulus and damping ratio. (3) The static and dynamic properties of asphalt concrete are very sensitive to the temperature. The test temperature should be made clear for the static and dynamic tests of asphalt concrete. The specimen temperature and the test ambient temperature must be strictly controlled
sj-docx-1-jht-10.1177_10963480231197091 – Supplemental material for Tourists’ Value Co-Creation With Service Robots: Antecedents and Mechanisms
No full textSupplemental material, sj-docx-1-jht-10.1177_10963480231197091 for Tourists’ Value Co-Creation With Service Robots: Antecedents and Mechanisms by Vera Shanshan Lin, Xinyi Zhang, Yuting Ren, Wei-Jue Huang and Honggen Xiao in Journal of Hospitality & Tourism Research</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
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
- …
