J. Huang

University of Newcastle Australia · Department of Civil Engineering

In uncoupled consolidation analysis, settlement and pore water pressure are solved independently, whereas in coupled analysis, they are solved simultaneously to ensure continuity (i.e., the volume change in soil due to compression must equal the water volume change caused by dissipation). This study investigates the coupling effects of soil deforma...

Landslide-induced barrier dams pose a threat to the safety of humans, livestock and nearby infrastructures. The efficient assessment of landslide blocking river is crucial for disaster prevention and mitigation solutions. This study proposes a novel stochastic assessment framework to evaluate the landslide blocking river through the prediction of t...

Establishing a reasonable rainfall infiltration model is an important prerequisite for revealing the rainfall-induced slope failure mechanism and disaster prevention and control. Traditional Green-Ampt model does not consider the distribution of soil stratification and transition layer formed by rainwater infiltration. This paper presents a method...

The rainfall infiltration analysis of slopes is of a great significance to the reinforcement design, mitigation and risk mitigation of rainfall-induced landslides. The classical Green-Ampt model, whose parameters have clear physical meanings and which is easy to implement, ignores the fact that a transition layer objectively exists in infiltration...

Quantitative calculation of single landslide risk has great significance for the prevention and treatment of landslides, through analysing the slope stability under different rainfall recurrence periods. In this study, the rainfall of the past 40 years in Xun'wu County of China is counted and the rainfall during the return periods of 10, 20 and 50...

A discrete fracture network (DFN) represents the random distribution of fractures within rock masses in three dimensions and has been widely used in the stability analysis of jointed rock slopes. This study introduces a novel method for constructing three-dimensional DFN models to address the limitations of traditional circular or polygon-based mod...

This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction (LSP). To illustrate various study area scales, Ganzhou City in China, its eastern region (Ganzhou East), and Ruijin County in Ganzhou East were chosen. Different mapping unit scales are r...

This study investigates the feasibility of implementing simple Machine Learning models to make fast and reliable predictions of rockfall energies and run-outs at the base of highwalls. Probabilistic rockfall simulations are performed to generate a synthetic dataset of rockfall trajectories using high-resolution 3D photogrammetric models of fifteen...

In this paper, we propose a novel framework, physics-informed deep learning (PIDL), which combines a set of data- and physics-driven modeling methods along with an uncertainty assessment technique, to solve the ill-posed inverse problems in unsaturated infiltration and make plausible moisture field predictions. Specifically, PIDL integrates three m...

The utilization of different connection methods between landslides and environmental factors introduces uncertainty in landslide susceptibility prediction (LSP). Investigating and identifying the characteristics of this uncertainty and determining more suitable connection methods are of significant importance for LSP. This study uses original 12 en...

Reliability assessment of tunnel structures is a fundamental concern in civil engineering, as tunnels are inevitably affected by adjacent excavation projects, resulting to additional deformations. The complexity of this subject is further amplified when considering the soil uncertainty. In this study, a direct Monte-Carlo simulation (DMCS) approach...

A three-dimensional representation of the random distribution of fractures in rock masses, known as the discrete fracture network (DFN), is widely used to analyze the stability of jointed rock slopes. In this paper, a new framework for constructing three-dimensional DFN models in rock masses has been proposed to overcome the limitations of conventi...

Automatic and timely identification of mud pumping is important for the reliability and safety of railroads. The current mud pumping prediction model is based on monitoring the dynamic response of railway tracks. The essential geotechnical trigger factors such as the hydrological conditions are not well-considered in these prediction models, as tha...

A novel methodology that involves the coupling of Convolutional Neural Networks (CNNs) and a data augmentation technique is proposed for slope reliability calculations. The methodology starts from generating a small set of random field samples, which are then calculated using the shear strength reduction method in the finite-difference scheme to ob...

The numerical simulation and slope stability prediction are the focus of slope disaster research. Recently, machine learning models are commonly used in the slope stability prediction. However, these machine learning models have some problems, such as poor nonlinear performance, local optimum and incomplete factors feature extraction. These issues...

Due to the similarity of conditioning factors, the aggregation feature of landslides and the multi-temporal landslide inventory, the spatial and temporal effects of landslides need to be considered in landslide susceptibility prediction (LSP). The ignorance of this issue will result in some biases and time-invariance in landslide susceptibility. He...

The selection of non-landslide samples has a great impact on the machine learning modelling for landslide susceptibility prediction (LSP). This study presents a novel framework for studying the uncertainty of non-landslide samples selection on the LSP results through the slope unit-based machine learning models. In this framework, the non-landslide...

Landslides, one of the most common mountain hazards, can result in enormous casualties and huge economic losses in mountainous regions. In order to address the landslide hazards effectively, the geological society is required not only to develop in-depth understanding of landslide mechanism but also to quantify its associated risk. In this article,...

This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction (LSP), namely the spatial resolution, proportion of model training and testing datasets and selection of machine learning models. Taking Yanchang County of China as example, the landslide inventory and 12 important conditioning factors...

Landslides are affected not only by their own environmental factors, but also by the neighborhood environmental factors and the landslide clustering effect, which are represented as the neighborhood characteristics of modelling spatial datasets in landslide susceptibility prediction (LSP). This study aims to innovatively explore the neighborhood ch...

To perform landslide susceptibility prediction (LSP), it is important to select appropriate mapping unit and landslide-related conditioning factors. The efficient and automatic multi-scale segmentation (MSS) method proposed by the authors promotes the application of slope units. However, LSP modeling based on these slope units has not been performe...

The rainfall-induced slope failure mechanism and reliability analyses rarely consider the spatial variability of hydraulic and shear strength parameters at the same time and ignore a fact that the slopes always keep stable under the natural condition. An infinite slope model is taken as an example to conduct probabilistic back analyses of spatially...

This study introduces a generic framework for geotechnical subsurface modeling, which accounts for spatial autocorrelation with local mapping machine learning (ML) methods. Instead of using XY coordinate fields directly as model input, a series of autocorrelated geotechnical distance fields (GDFs) is designed to enable the ML models to infer the sp...

Heavy rainfall and engineering slope cutting are two key factors that trigger unstable red clay landslides with red clay soil as the sliding mass in the mountainous and hilly areas of South China. It is important to study the influence of engineering slope cuttings on changes in slope stability under heavy rainfall conditions. First, by summarising...

The uncertainty of non-landslide sample selection has a crucial influence on the landslide susceptibility prediction (LSP), which has not been thoroughly studied. In this study, a novel framework based on slope unit-based machine learning models is proposed to solve this issue. First, slope units are extracted by the multi-scale segmentation method...

Existing studies relating to landslide susceptibility prediction (LSP) either do not pay enough attentions to the continuously updated landslide inventories or use batch learning methods for LSP, resulting in the insufficient use of the entire landslide inventory. To overcome this problem, the Incremental Learning theory combined with a Bayesian Ne...

Deterministic single factor of safety method cannot explicitly account for the influences of various sources of uncertainties (e.g., spatial variability of geomaterials, measurement and transformation uncertainties) in stability design of slopes. Many probabilistic methods have been applied to the reliability‐based design (RBD) of slopes, but they...

Characterization of the spatial variability of rock mass parameters differs significantly from that of soil parameters due to complex structures inherently existing in the rock masses. At present，the spatial variability modeling of the mechanical parameters of highly fractured/weathered rock masses and those dominated by a single structural plane a...

For linear conditioning factors such as rivers, roads, and geological faults, existing studies mainly use buffer analysis in Geographic Information System to obtain discrete variables such as distance to rivers and roads. These discrete variables have random fluctuations and are sensitive to the errors of point or line elements, leading to a decrea...

Existing reliability analyses of embankment slopes usually considered the spatial variabilities of shear strength parameters and saturated hydraulic conductivity separately. Additionally, the variability in the soil–water characteristic curve (SWCC) was ignored. A non-intrusive approach is proposed for reliability analyses of unsaturated embankment...

Rainfall-induced landslide hazard warning, which refers to the prediction of the spatial-temporal probability of landslide occurrence in a certain area under the conditions of continuous rainfall processes, can be established based on landslide susceptibility mapping and critical rainfall threshold calculations. However, it is difficult to determin...

This paper compares the performance of five popular machine learning methods, namely, particle swarm optimization–extreme learning machine (PSO–ELM), particle swarm optimization–kernel extreme learning machine (PSO–KELM), particle swarm optimization–support vector machine (PSO–SVM), particle swarm optimization–least squares support vector machine (...

Geotechnical and geophysical testing data are conventionally considered as separated information or combined based on deterministic methods in site investigation programs, which causes loss of information and introduces additional uncertainties. This study aims to reduce the uncertainties and costs in inhomogeneous soil profile characterization and...

Spatial variability of soil properties was rarely taken into account directly in traditional slope stability analyses, rather some “average” or suitably “pessimistic” properties are assumed to act across the whole region of interest. In the last two decades, a large portion of published research papers on slope stability have tried to explicitly mo...

Timely detection and identification of rail breaks are crucial for safety and reliability of railway networks. This article proposes a new deep learning-based approach using the daily monitoring data from in-service trains. A time-series generative adversarial network (TimeGAN) is employed to mitigate the problem of data imbalance and preserve the...

Bayesian estimation of spatially varying soil parameters is a challenging task in geotechnical engineering because a large number of random variables need to be learned. To address this challenge, three Bayesian methods are revisited, including Differential Evolution Adaptive Metropolis with sampling from past states [DREAM(zs)] method, Bayesian Up...

Site investigation is an important step of geotechnical projects. Previous studies have investigated the benefits of undertaking site investigation for differing scopes by assuming the measurements obtained from site investigation tests are “true” measurements without measurement errors. However, measurement errors are inevitable in all types of si...

In some studies on landslide susceptibility mapping (LSM), landslide boundary and spatial shape characteristics have been expressed in the form of points or circles in the landslide inventory instead of the accurate polygon form. Different expressions of landslide boundaries and spatial shapes may lead to substantial differences in the distribution...

The determination of mapping units, including grid, slope, unique condition, administrative division, and watershed units, is a very important modeling basis for landslide assessments. Among these mapping units, the slope unit has been paid a lot of attention because it can effectively reflect the physical relationships between landslides and the f...

Timely detection and identification of substructure defects in railway track are crucial for the safety and reliability of railway networks. Instrumented in-service trains can provide daily data for assessing the track conditions. This study tries to develop a data-driven model for the prediction of mud pumping defects using daily in-service train...

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 en...

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 presented a machine learning approach based on the C5.0 decision tree (DT) model and the K-means cluster algorithm to produce a regional...

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...

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 hydrologic...

Soil-cement column is a geotechnical solution used for ground improvement in coastal areas. However, after long periods of exposure, the strength of these columns may decrease to below their designed safe bearing capacity, ultimately resulting in failure. In this paper, the effects of high sulphate concentrations (100%, 200%, 500% and 1000% that of...

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 t...

Regional terrain complexity assessment (TCA) is an important theoretical foundation for geological feature identification, hydrological information extraction and land resources utilization. However, the previous TCA models have many disadvantages; for example, comprehensive consideration and redundancy information analysis of terrain factors is la...

Loess landslides frequently occur in the northwest area of China, leading to serious damage to the society and economy. Under the effects of rainfall and groundwater seepage, the stress–strain behaviours of Malan loess landslides are closely related to the saturated–unsaturated state of slide mass. Hence, it is of great significance to study the cr...

Conventional supervised and unsupervised machine learning models used for landslide susceptibility prediction (LSP) have many drawbacks, such as an insufficient number of recorded landslide samples, and the subjective and random selection of non-landslide samples. To overcome these drawbacks, a semi-supervised multiple-layer perceptron (SSMLP) is i...

This paper proposes a new sequential probabilistic back analysis approach for probabilistically determining the uncertain geomechanical parameters of shield tunnels by using time-series monitoring data. The approach is proposed based on the recently developed Bayesian updating with subset simulation. Within the framework of the proposed approach, a...

Soil erosion (SE) provides slide mass sources for landslide formation, and reflects long-term rainfall erosion destruction of landslides. Therefore, it is possible to obtain more reliable landslide susceptibility prediction results by introducing SE as a geology and hydrology-related predisposing factor. The Ningdu County of China is taken as a res...

Previous studies about probabilistic back analysis for shear strength parameters of landslides generally adopted a fixed slip surface. This setting may lead to unreliable results due to the uncertainty of slip surface location speculated by limited observations. Based on Bayes’ theorem, this paper proposes a probabilistic framework for the back ana...

Although numerical methods based on strength reduction are becoming popular in slope stability analysis, they fail to provide a crisp critical slip surface but only a shear band. The widely used visualization techniques for defining the critical slip surface are susceptible to subjective judgment and are inefficient in batch analysis and three-dime...

To mitigate the flood risk in flood detention basin, it is of great significance to accurately simulate the flood movement process and estimate the potential flood consequences induced by dike-break. In this paper, a MIKE21-based numerical approach for modeling of flood movement in the flood detention basin is developed. The approaches for estimati...