Article
Engineering, Industrial
Zeyu Wang, Abdollah Shafieezadeh
Summary: This paper presents a new approach to overcome the computational cost problem of Bayesian updating for complex computational models. It decomposes the updating problem into a set of sub-reliability problems with uncertain failure thresholds, enabling precise identification of intermediate failure thresholds and training of surrogate models. The proposed method reduces computational costs significantly while maintaining high accuracy.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Mechanical
Jiayi Ouyang, Yong Liu
Summary: This study proposes an effective approach that combines Kriging-based conditional random field with the BUS algorithm to integrate multi-type observations, in order to update the probability distribution of soil parameters and assess the slope reliability.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Geosciences, Multidisciplinary
Longlong Chen, Wengang Zhang, Fuyong Chen, Dongming Gu, Lin Wang, Zhenyu Wang
Summary: Anisotropic spatial variability of soil properties has a significant influence on slope failure probability and failure characteristics. The directional angles of scales of fluctuation and the cross-correlation between soil properties are the key factors. General anisotropic spatial variability has a stronger effect on slope reliability compared to transverse anisotropic spatial variability.
GEOSCIENCE FRONTIERS
(2022)
Article
Computer Science, Interdisciplinary Applications
Xin Liu, Yu Wang
Summary: This study develops a method based on Monte Carlo simulation to assess the annual slope failure probability (PFA) considering both soil spatial variability and rainfall uncertainty. Results show that the semi-analytical and Monte Carlo simulation-based methods produce consistent PFA. When the variability of soil properties is negligible, PFA is dominated by rainfall uncertainty and converges to a constant failure probability.
COMPUTERS AND GEOTECHNICS
(2023)
Article
Engineering, Multidisciplinary
P. O. Hristov, F. A. DiazDelaO
Summary: Reliability analysis can be efficiently conducted using subset simulation, which samples progressively from the input domain to find the failure domain. Probabilistic numerics framework treats computation as a statistical inference problem and enables reliability analysis for probabilistic numerical models. This paper presents a generalisation of subset simulation method, discussing its advantages, challenges, and providing an example with industrial application.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Computer Science, Interdisciplinary Applications
Zhibin Li, Wenping Gong, Liang Zhang, Lei Wang
Summary: This paper proposes a novel multi-objective probabilistic back analysis approach based on multi-source observations to improve the reliability of geotechnical predictions by determining the optimal updating strategy more effectively.
COMPUTERS AND GEOTECHNICS
(2022)
Article
Engineering, Geological
Hua-Ming Tian, Dian-Qing Li, Zi-Jun Cao, Dong-Sheng Xu, Xiao-Ying Fu
Summary: The paper discusses the stabilization of slopes in geological and geotechnical engineering through reinforcement measures, emphasizing the importance of the effectiveness of these measures in reducing slope failure risk. It proposes an efficient reliability-based monitoring sensitivity analysis framework and illustrates it using a real reinforced slope example. The results of the study demonstrate that the method can quantify the reliability sensitivity of different monitoring variables, facilitating decision-making in selecting sensitive monitoring variables during the monitoring design of reinforced slopes.
ENGINEERING GEOLOGY
(2021)
Article
Computer Science, Interdisciplinary Applications
Jing-Ze Li, Shao-He Zhang, Lei-Lei Liu, Lei Huang, Yung-Ming Cheng, Daniel Dias
Summary: This study systematically investigates the influence of soil spatially variable anisotropy on the stability of pile-reinforced slopes. An integrated probabilistic analysis framework is used to obtain the optimal reinforcement scheme, and subset simulation is employed to enhance the computational efficiency. The results show that rotated anisotropy significantly affects the performance and failure probability of pile-reinforced slopes.
COMPUTERS AND GEOTECHNICS
(2022)
Article
Engineering, Geological
Wenmin Yao, Changdong Li, Changbin Yan, Hongbin Zhan
Summary: The study proposes a hybrid framework for slope reliability based on Bayesian sequential updating technology, integrating prior knowledge, multiple estimation methods, and model uncertainties to estimate slope reliability with limited geotechnical data. Through experiments with three slope examples, the framework is shown to provide reliable and accurate estimations of slope reliability.
Article
Engineering, Civil
Pei Liu, Shuqiang Huang, Mingming Song, Weiguo Yang
Summary: A Bayesian model updating method utilizing ambient vibration data to improve efficiency and avoid local optimums is proposed. The method employs Bayesian fast Fourier transform to extract modal parameters for weighting factors, and uses a subset simulation optimization algorithm to find global optimal solutions. Validation on numerical and real-world structures shows improved accuracy in parameter estimations, demonstrating the effectiveness of the proposed method.
JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING
(2021)
Article
Engineering, Mechanical
Dimitrios G. Giovanis, Michael D. Shields
Summary: The objective of this study is to quantify the uncertainty in probability of failure estimates resulting from incomplete knowledge of the probability distributions for the input random variables. The study proposes a framework that combines Subset Simulation (SuS) with Bayesian/information theoretic multi-model inference, and through methods such as multi-model inference and importance sampling, empirical probability distributions of failure probabilities that provide direct estimates of the uncertainty in failure probability estimates are obtained.
PROBABILISTIC ENGINEERING MECHANICS
(2022)
Article
Engineering, Geological
Himanshu Rana, G. L. Sivakumar Babu
Summary: This study proposes a probabilistic back analysis method to evaluate the uncertainties in soil parameters for observed slope data. The methodology utilizes multi-output least square support vector regression, multi-objective genetic algorithm, and Bayesian analysis. The results can be applied to the reliability-based design of slopes and the provision of remedial measures.
GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS
(2022)
Article
Engineering, Mechanical
Pinghe Ni, Qiang Li, Qiang Han, Kun Xu, Xiuli Du
Summary: This study proposes a Bayesian probabilistic model updating approach for substructure identification, which evaluates the uncertainties in identified results by analyzing the responses of large-scale structures. Numerical experiments on a three-span beam structure and experimental studies on an eight-floor steel frame were conducted to verify the accuracy and efficiency of the proposed method, and the results demonstrate its effectiveness.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Environmental
Qing Yang, Bin Zhu, Tetsuya Hiraishi
Summary: This study conducted probabilistic analyses of submarine slope seismic stability using an enhanced Newmark method. The results highlighted the significant effects of pore water pressure and spatial variability of soil strength on slope displacements and failure probabilities.
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
(2021)
Article
Computer Science, Interdisciplinary Applications
D. Rossat, J. Baroth, M. Briffaut, F. Dufour
Summary: In this paper, a Bayesian inversion approach combining adaptive Polynomial Chaos Kriging surrogate models and Subset Simulation rare event estimation method is proposed. The approach enables accurate approximation of posterior distributions and exhibits high sampling efficiency in dealing with multi-modal posteriors.
JOURNAL OF COMPUTATIONAL PHYSICS
(2022)
Article
Engineering, Geological
Shui-Hua Jiang, Lei Wang, Su Ouyang, Jinsong Huang, Yuan Liu
Summary: This study systematically investigates the differences of three Bayesian methods in generating random samples, convergence criterion, model evidence, and estimation of posterior probability of failure in slope reliability updating. It is found that BUS + SS method performs well in both low-dimensional and high-dimensional problems of spatially varying soil parameters, while DREAM((zs)) method is recommended for low-dimensional problems and mBUS + SS method is suitable for high-dimensional problems.
GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS
(2022)
Article
Engineering, Geological
Xiaohui Qi, Zhiyong Yang, Jian Chu
Summary: Geological information plays a crucial role in the design of underground excavation and supporting measures. This study introduces a generalized additive model (GAM) to accurately predict the locations of geological interfaces, and evaluates its performance using data from two different geological formations in Singapore. The results demonstrate that the GAM method provides reasonable confidence and prediction intervals, and effectively reflects the geological complexity.
GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS
(2022)
Article
Construction & Building Technology
Xiaohui Qi, Hao Wang, Jian Chu, Kiefer Chiam
Summary: This paper evaluates three commonly used spatial prediction methods for geological interfaces and proposes a zonation method to improve prediction accuracy. The results show that the multivariate adaptive spline regression (MARS) method can more clearly depict the spatial trend of geological interfaces.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2022)
Article
Engineering, Geological
Shui-Hua Jiang, Xian Liu, Jinsong Huang, Chuang-Bing Zhou
Summary: This paper proposes an efficient reliability-based design method based on field data that can account for various uncertainties in slope stability design, such as spatial variability of geomaterials and measurement and transformation uncertainties. The results show that the proposed method can quickly obtain rational slope design schemes based on field data.
INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS
(2022)
Article
Environmental Sciences
Faming Huang, Siyu Tao, Deying Li, Zhipeng Lian, Filippo Catani, Jinsong Huang, Kailong Li, Chuhong Zhang
Summary: This study aims to reduce the uncertainty in landslide susceptibility prediction (LSP) by exploring the neighborhood characteristics of landslide spatial datasets. Remote sensing and GIS were used to acquire and manage neighborhood environmental factors, and the landslide clustering effect was represented using the landslide aggregation index in GIS. Results showed that considering the neighborhood characteristics improved the accuracy of LSP.
Article
Engineering, Geological
Yu Lei, Jinsong Huang, Yifei Cui, Shui-Hua Jiang, Shengnan Wu, Jianye Ching
Summary: Landslides, a common mountain hazard, have the potential to cause significant casualties and economic losses. Effective management of landslide risks requires an understanding of their mechanisms and a quantification of associated risks. This article reviews two types of landslide risk assessments, namely hard and soft approaches, which focus on the mechanics and consequences of individual landslides and the quantification of hazard and vulnerability. It is hoped that this article will serve as a reference for future studies in landslide risk assessments.
GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS
(2023)
Article
Geosciences, Multidisciplinary
Zhilu Chang, Jinsong Huang, Faming Huang, Kushanav Bhuyan, Sansar Raj Meena, Filippo Catani
Summary: The selection of non-landslide samples has a significant impact on the machine learning modelling for landslide susceptibility prediction (LSP). This study presents a novel framework to study the uncertainty of non-landslide samples selection on LSP results using slope unit-based machine learning models. Statistical analysis is used to represent the uncertainty of landslide susceptibility indexes (LSIs) under each non-landslide selection. The maximum probability analysis (MPA) method is applied to reduce the uncertainty and select the optimal landslide susceptibility level.
Article
Computer Science, Interdisciplinary Applications
Lin-Shuang Zhao, Xiaohui Qi, Fang Tan, Yue Chen
Summary: This study compiles a global-wide jet grouting database and proposes an explicit model suitable for different jet grouting systems. The Bayesian inference method is used to estimate regression parameters and quantify prediction uncertainty. The proposed model is compared with an empirical model, showing higher accuracy and the ability to reasonably quantify prediction uncertainty.
COMPUTERS AND GEOTECHNICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Shui-Hua Jiang, Guang-Yuan Zhu, Ze Zhou Wang, Zhuo-Tao Huang, Jinsong Huang
Summary: A novel methodology that combines Convolutional Neural Networks (CNNs) and data augmentation is proposed for slope reliability calculations. The methodology involves generating random field samples and calculating associated factors of safety using shear strength reduction method. A data augmentation technique is developed based on the relationship between factors of safety and soil property values, which improves the quantity and comprehensiveness of the dataset. A CNN model is then trained using the augmented dataset to predict slope failure probability. The results show the effectiveness of CNNs in interpreting high-dimensional random fields and the improvement in computational efficiency and predictive capability through data augmentation.
COMPUTERS AND GEOTECHNICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Hao-Qing Yang, Jian Chu, Xiaohui Qi, Shifan Wu, Kiefer Chiam
Summary: This study aims to evaluate the uncertainty of the soil-rock interface using the Bayesian evidential learning (BEL) framework without subjective assumptions. A borehole-intensive site is selected to investigate the impact of borehole number and layout on the estimation of the soil-rock interface. The results highlight the importance of borehole planning in reducing geological uncertainty.
COMPUTERS AND GEOTECHNICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Yuting Zhang, Jinsong Huang, Anna Giacomini
Summary: This paper proposes a probabilistic approach based on Bayes' theorem and the First Order Reliability Method (FORM) to calibrate resistance factors for different numbers of load tests and the corresponding test results. The results show that resistance factors are significantly increased even if only one positive test is observed among all the tests. Differences in resistance factors between various design methods are significantly reduced if one or more tests are positive for low variability sites, while for high variability sites, the differences in resistance factors are only slightly decreased, indicating the importance of considering design methods in such cases.
COMPUTERS AND GEOTECHNICS
(2023)
Article
Engineering, Geological
Hao-Qing Yang, Jian Chu, Xiaohui Qi, Shifan Wu, Kiefer Chiam
Summary: A reliable geological cross-section is crucial for underground structure design and risk assessment. This study introduces a Bayesian approach to simulate geological formations using available borehole data, and the results show that the method performs well.
ENGINEERING GEOLOGY
(2023)
Article
Geosciences, Multidisciplinary
Zhilu Chang, Faming Huang, Jinsong Huang, Shui-Hua Jiang, Yuting Liu, Sansar Raj Meena, Filippo Catani
Summary: Landslide susceptibility prediction (LSP) should consider the spatial and temporal effects of landslides due to conditioning factors similarity. A novel framework is proposed to update landslide susceptibility by considering both spatial and temporal effects. The framework divides the landslide inventory into pre- and fresh-landslide inventories and uses the support vector machine (SVM) model and a normalized spatial distance index (NSDI) to develop the SVM-NSDI model. The prediction performance of the LSP models is evaluated using the confusion matrix, area under the receiver operating characteristic curve (AUC), and frequency ratio (FR) accuracy. The study concludes that landslide susceptibility can be updated by considering spatial correlation and temporal effects, providing more accurate results for decisionmakers. Rating: 8/10
GEOSCIENCE FRONTIERS
(2023)
Article
Engineering, Electrical & Electronic
Cheng Zeng, Jinsong Huang, Hongrui Wang, Jiawei Xie, Shan Huang
Summary: This article proposes a new deep learning-based approach using daily monitoring data from in-service trains to timely detect and identify rail breaks. The results show that the proposed method can successfully predict rail breaks and assist in maintenance planning through cause analysis.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Proceedings Paper
Engineering, Civil
Shifan Wu, Xiaohua Pan, Xiaohui Qi, Kiefer Chiam, Kok Hun Goh, Jian Chu
Summary: The study demonstrates the use of newly obtained borehole data to update the existing 3D geological model for improved accuracy. Uncertainty analysis of the model shows that the standard deviations of predicted errors for rockhead positions are improved after incorporating new borehole data.
GEO-CONGRESS 2022: DEEP FOUNDATIONS, EARTH RETENTION, AND UNDERGROUND CONSTRUCTION
(2022)