ORCID Profile
0000-0002-5159-1635
Current Organisation
The University of Newcastle
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Civil Engineering | Civil Geotechnical Engineering | Geomechanics and Resources Geotechnical Engineering |
Civil Construction Design | Rail Infrastructure and Networks | Primary Mining and Extraction of Mineral Resources not elsewhere classified | Mining and Extraction of Energy Resources not elsewhere classified | Information Processing Services (incl. Data Entry and Capture) | Natural Hazards in Urban and Industrial Environments
Publisher: EDP Sciences
Date: 12-2011
Publisher: Elsevier BV
Date: 10-2017
Publisher: American Society of Civil Engineers (ASCE)
Date: 04-2002
Publisher: Canadian Science Publishing
Date: 09-2015
Abstract: 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.
Publisher: Elsevier BV
Date: 09-2023
Publisher: Elsevier BV
Date: 03-2016
Publisher: Thomas Telford Ltd.
Date: 02-2013
Abstract: The classical problem of a beam on an elastic foundation has long been of practical interest to geotechnical engineers, because it provides a framework for computing deflections not only of foundations, but also of vertically oriented laterally loaded piles. The supporting soil can be modelled as an elastic medium, which can be calibrated to represent the stiffness of the soils adjacent to the beam (or pile). The objective of this paper is to study the influence of spatially random soil stiffness on deformations of transversely loaded homogeneous piles and beams, using a combination of finite-element analysis, random field theory and Monte Carlo simulations. Following code validation against alternative solutions, the method investigates how the statistically defined soil stiffness (mean, standard deviation and spatial correlation length) influences the mean and standard deviation of pile or beam deflection. The goal of such an approach is to estimate the probability of deflections exceeding some design threshold.
Publisher: American Society of Civil Engineers (ASCE)
Date: 03-2010
Publisher: Springer Science and Business Media LLC
Date: 20-05-2020
Publisher: Informa UK Limited
Date: 12-07-2019
Publisher: Hindawi Limited
Date: 06-06-2018
DOI: 10.1155/2018/1450683
Abstract: A key issue in assessment on tunnel face stability is a reliable evaluation of required support pressure on the tunnel face and its variations during tunnel excavation. In this paper, a Bayesian framework involving Markov Chain Monte Carlo (MCMC) simulation is implemented to estimate the uncertainties of limit support pressure. The probabilistic analysis for the three-dimensional face stability of tunnel below river is presented. The friction angle and cohesion are considered as random variables. The uncertainties of friction angle and cohesion and their effects on tunnel face stability prediction are evaluated using the Bayesian method. The three-dimensional model of tunnel face stability below river is based on the limit equilibrium theory and is adopted for the probabilistic analysis. The results show that the posterior uncertainty bounds of friction angle and cohesion are much narrower than the prior ones, implying that the reduction of uncertainty in cohesion and friction significantly reduces the uncertainty of limit support pressure. The uncertainty encompassed in strength parameters are greatly reduced by the MCMC simulation. By conducting uncertainty analysis, MCMC simulation exhibits powerful capability for improving the reliability and accuracy of computational time and calculations.
Publisher: Elsevier BV
Date: 06-2017
Publisher: American Society of Civil Engineers
Date: 06-2017
Publisher: Elsevier BV
Date: 07-2020
Publisher: American Society of Civil Engineers
Date: 25-02-2013
Publisher: Elsevier BV
Date: 03-2013
Publisher: Springer Science and Business Media LLC
Date: 27-02-2018
Publisher: MDPI AG
Date: 17-12-2020
DOI: 10.3390/RS12244134
Abstract: 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.
Publisher: Elsevier BV
Date: 06-2010
DOI: 10.3208/SANDF.50.343
Publisher: Elsevier BV
Date: 09-2014
Publisher: Springer Science and Business Media LLC
Date: 20-11-2022
Publisher: Elsevier BV
Date: 11-2018
Publisher: Elsevier BV
Date: 08-2020
Publisher: Elsevier BV
Date: 09-2015
Publisher: American Society of Civil Engineers
Date: 06-2017
Publisher: Elsevier BV
Date: 03-2019
Publisher: Springer Science and Business Media LLC
Date: 11-09-2019
Publisher: Wiley
Date: 22-03-2011
DOI: 10.1002/NAG.909
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Elsevier BV
Date: 12-2020
Publisher: Elsevier BV
Date: 10-2019
Publisher: Informa UK Limited
Date: 12-01-2023
Publisher: American Society of Civil Engineers
Date: 06-2017
Publisher: Elsevier BV
Date: 02-2017
Publisher: American Society of Civil Engineers (ASCE)
Date: 11-2018
Publisher: EDP Sciences
Date: 19-06-2013
Publisher: American Society of Civil Engineers
Date: 15-02-2010
Publisher: Elsevier BV
Date: 02-2017
Publisher: EDP Sciences
Date: 19-12-2015
Publisher: Elsevier BV
Date: 10-2013
Publisher: Informa UK Limited
Date: 07-02-2019
Publisher: Elsevier BV
Date: 07-2023
Publisher: Elsevier BV
Date: 04-2010
DOI: 10.1016/J.JOCN.2009.07.096
Abstract: Guillain-Barré syndrome (GBS) is an acquired demyelinating neuropathy, characterized by infiltration of peripheral nerves with macrophages and T cells. There have been reports of antibodies to glycolipids in GBS. We have previously found T cell reactivity to glycolipids in patients with the demyelinating form of GBS. This study was performed to characterize the cytokines produced by these T cells. Peripheral blood lymphocytes from patients with GBS, chronic inflammatory demyelinating polyradiculoneuropathy, healthy control patients and other neuropathies were incubated with the ganglioside GM1 and transferred to enzyme-linked immunospot plates. The average number per well of spot-forming cells (SFC) in the absence of antigen was counted. The average spontaneous SFC number was subtracted from the average SFC number in the presence of GM1, to produce a corrected SFC. There was significantly increased production of interferon-gamma but not interleukin-5 in response to stimulation with the ganglioside GM1. This could indicate that SFC have a role in pathogenesis of disease.
Publisher: Springer Science and Business Media LLC
Date: 27-10-2019
Publisher: MDPI AG
Date: 08-06-2020
DOI: 10.3390/IJGI9060377
Abstract: 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 research area. Firstly, 446 landslides are obtained through government disaster survey reports. Secondly, the SE amount in Ningdu County is calculated and nine other conventional predisposing factors are obtained under both 30 m and 60 m grid resolutions to determine the effects of SE on landslide susceptibility prediction. Thirdly, four types of machine-learning predictors with 30 m and 60 m grid resolutions—C5.0 decision tree (C5.0 DT), logistic regression (LR), multilayer perceptron (MLP) and support vector machine (SVM)—are applied to construct the landslide susceptibility prediction models considering the SE factor as SE-C5.0 DT, SE-LR, SE-MLP and SE-SVM models C5.0 DT, LR, MLP and SVM models with no SE are also used for comparisons. Finally, the area under receiver operating feature curve is used to verify the prediction accuracy of these models, and the relative importance of all the 10 predisposing factors is ranked. The results indicate that: (1) SE factor plays the most important role in landslide susceptibility prediction among all 10 predisposing factors under both 30 m and 60 m resolutions (2) the SE-based models have more accurate landslide susceptibility prediction than the single models with no SE factor (3) all the models with 30 m resolutions have higher landslide susceptibility prediction accuracy than those with 60 m resolutions and (4) the C5.0 DT and SVM models show higher landslide susceptibility prediction performance than the MLP and LR models.
Publisher: IWA Publishing
Date: 10-04-2017
Abstract: Many nonlinear models have been proposed to forecast groundwater level. However, the evidence of chaos in groundwater levels in landslide has not been explored. In addition, linear correlation analyses are used to determine the input and output variables for the nonlinear models. Linear correlation analyses are unable to capture the nonlinear relationships between the input and output variables. This paper proposes to use chaos theory to select the input and output variables for nonlinear models. The nonlinear model is constructed based on support vector machine (SVM). The parameters of SVM are obtained by particle swarm optimization (PSO). The proposed PSO-SVM model based on chaos theory (chaotic PSO-SVM) is applied to predict the daily groundwater levels in Huayuan landslide and the weekly, monthly groundwater levels in Baijiabao landslide in the Three Gorges Reservoir Area in China. The results show that there are chaos characteristics in the groundwater levels. The linear correlation analysis based PSO-SVM (linear PSO-SVM) and chaos theory-based back-propagation neural network (chaotic BPNN) are also applied for the purpose of comparison. The results show that the chaotic PSO-SVM model has higher prediction accuracy than the linear PSO-SVM and chaotic BPNN models for the test data considered.
Publisher: American Society of Civil Engineers
Date: 27-10-2010
DOI: 10.1061/41144(391)9
Publisher: Springer International Publishing
Date: 2020
Publisher: American Chemical Society (ACS)
Date: 18-12-2019
Abstract: A biomimetic approach to total synthesis can offer several benefits, including the development of cascade reactions for the rapid generation of molecular complexity, and guidance in the structure revision of old natural products and the anticipation of new ones. Herein, we describe how a biomimetic synthesis of bruceol, a pentacyclic meroterpenoid, led to the anticipation, isolation, and synthesis of isobruceol. The key step in the synthesis of both bruceol and isobruceol was an intramolecular hetero-Diels-Alder reaction of an
Publisher: Wiley
Date: 08-01-2019
DOI: 10.1002/NME.6006
Publisher: EDP Sciences
Date: 2013
Publisher: Elsevier BV
Date: 06-2012
Publisher: Elsevier BV
Date: 08-2017
Publisher: Elsevier BV
Date: 04-2011
Publisher: American Society of Civil Engineers
Date: 29-05-2012
Publisher: American Society of Civil Engineers (ASCE)
Date: 05-2021
Publisher: HARD Publishing Company
Date: 24-11-2016
DOI: 10.15244/PJOES/64307
Publisher: Thomas Telford Ltd.
Date: 10-2016
Abstract: Increases in rainfall and groundwater level rising can cause more frequent failures of unsaturated soil slopes, resulting in large-scale landslides. The aim of this paper is to propose a methodology for prediction of the failure of an infinite soil slope subject to steady unsaturated flow conditions. Probabilistic models for soil porosity, friction angle, matric suction and saturation are developed. The probability of slope failure for different groundwater levels and infiltration (or evaporation) intensities is investigated. Numerical results show that, for a slope with deep groundwater, the slope failure is mainly controlled by the rainfall infiltration, whereas for shallow groundwater, the slope failure is governed by the location of the groundwater level. A merit of the proposed methodology is that the fluctuation of the soil porosity along the depth of a slope is modelled as a Gaussian random field. The paper concludes that the proposed methodology can be used for prediction of slope failures under steady unsaturated flow conditions. Accurate prediction of slope failures can prevent catastrophic consequences of landslides.
Publisher: Elsevier BV
Date: 06-2011
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2013
Publisher: Wiley
Date: 10-12-2009
DOI: 10.1002/NAG.810
Publisher: Informa UK Limited
Date: 12-2021
Publisher: American Society of Civil Engineers
Date: 21-06-2011
DOI: 10.1061/41183(418)15
Publisher: Elsevier BV
Date: 2020
Publisher: Elsevier BV
Date: 02-2018
Publisher: American Society of Civil Engineers
Date: 06-2017
Publisher: Elsevier BV
Date: 06-2020
Publisher: Elsevier BV
Date: 07-2012
Publisher: Wiley
Date: 29-06-2022
DOI: 10.1002/NAG.3414
Abstract: 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 typically require performing numerous deterministic slope stability analyses. In this paper, an efficient reliability‐based design method for spatially varying slopes based on field data is proposed. Here, the RBD of a slope angle is concerned. Reliability‐based design is implemented using an inverse First Order Reliability Method (inverse FORM ‐ IFORM). A sandy slope and a cohesive slope are investigated as ex les, respectively, to illustrate the proposed method. The results indicate that the proposed method can quickly obtain rational design schemes of slope angles accounting for the spatial variability of soil properties, measurement and transformation uncertainties based on the field data. It can act as a practical and effective tool for the RBD of slopes in two‐dimensional spatially variable soils. Additionally, it is found that the random field mesh size affects the RBD results significantly, while the probability distribution and horizontal autocorrelation of soil parameters have slight influences on the RBD results.
Publisher: American Society of Civil Engineers
Date: 06-2017
Publisher: Elsevier BV
Date: 05-2017
Publisher: Society of Petroleum Engineers (SPE)
Date: 27-10-2010
DOI: 10.2118/139592-PA
Abstract: Liétard et al. (1999, 2002) have provided important insight into the mechanism and prediction of transient-state radial mud invasion in the near-wellbore region. They provided type curves describing mud-loss volume vs. time that allow the hydraulic width of natural fractures to be estimated through a curve-matching technique. This paper describes a simpler and more direct method for estimating the hydraulic width by the solution of a cubic equation, with input parameters given by the well radius rw, the overpressure ratio Δp/τy, and the maximum mud loss volume (Vm)max.
Publisher: American Society of Civil Engineers
Date: 17-03-2015
Publisher: American Society of Civil Engineers (ASCE)
Date: 06-2019
Publisher: CRC Press
Date: 04-09-2014
DOI: 10.1201/B17435-298
Publisher: American Society of Civil Engineers
Date: 07-03-2008
DOI: 10.1061/40971(310)15
Publisher: Elsevier BV
Date: 2012
Publisher: Elsevier BV
Date: 09-2017
Publisher: Elsevier BV
Date: 05-2023
Publisher: Informa UK Limited
Date: 02-01-2016
Publisher: Thomas Telford Ltd.
Date: 2016
Abstract: Identification of soil stratification is vital to geotechnical structural design and construction where the soil layer, soil type and properties are necessary inputs. Although methods are available for classifying the soil profiling using measured cone penetration test (CPT) data, the identification of soil stratification at uns led locations is still difficult due to significant variability of natural soil. The identification is further complicated by the considerable uncertainties in the CPT measurements and soil classification methods. This study aims to develop a probabilistic method to predict soil stratification at uns led locations by explicitly filtering the uncertainties in soil classification systems. An established Kriging interpolation technique is used to estimate the CPT parameters which are further interpreted to identify the soil stratification. Equations are derived to quantify the degree of uncertainties reduced by this method. The approaches are illustrated using a database of 26 CPT tests recently sourced from a dike near Ballina, Australia. Results show that the majority of the uncertainties in the soil parameters are screened by a soil classification index. The remaining uncertainties are further filtered by the soil classification systems. A clear stratification with a high degree of confidence is obtained in both horizontal plane and vertical uns led locations, which shows excellent agreement with the existing CPT tests. This study provides a methodology to clearly identify the soil strata and reduce the uncertainties in prediction of design properties, paving the way for a more cost-effective geotechnical design.
Publisher: American Society of Civil Engineers
Date: 07-03-2008
DOI: 10.1061/40971(310)17
Publisher: American Society of Civil Engineers (ASCE)
Date: 10-2009
Publisher: Elsevier BV
Date: 05-2016
Publisher: American Society of Civil Engineers
Date: 06-2017
Publisher: Thomas Telford Ltd.
Date: 08-2017
Abstract: Based on recently published deterministic solutions as a benchmark, the random finite-element method is used here to investigate the influence of spatial variability on the undrained stability of normally consolidated random slopes, where the mean strength increases linearly with depth while the coefficient of variation remains constant. Results are presented in the form of charts that give the mean and standard deviation of a dimensionless stability number. Using the charts presented in this note, engineers can obtain a preliminarily assessment of the probability of failure of normally consolidated clay slopes.
Publisher: Springer Science and Business Media LLC
Date: 07-07-2020
Publisher: Canadian Science Publishing
Date: 03-2019
Abstract: Site investigations provide characterization of soil properties, but inevitable uncertainty remains at locations that have not been examined. Only a limited scope of site investigation can be conducted due to budget and time constraints, hence there are always risks associated with design based on limited investigation information. An efficient geotechnical site investigation should involve choosing the optimal number and location of borehole sites to gain adequate information for a given cost. Using a slope as an ex le, this paper proposes a framework to find the best s ling location that gives the most information while minimizing the probability of making the wrong decisions. The results suggest that the slope crest appears to be the optimal location to conduct geotechnical site exploration for slope stability assessment.
Publisher: Elsevier BV
Date: 07-2017
Publisher: MDPI AG
Date: 06-09-2022
DOI: 10.3390/RS14184436
Abstract: 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 characteristics of landslide spatial datasets for reducing the LSP uncertainty. Neighborhood environmental factors were acquired and managed by remote sensing (RS) and the geographic information system (GIS), then used to represent the influence of landslide neighborhood environmental factors. The landslide aggregation index (LAI) was proposed to represent the landslide clustering effect in GIS. Taking Chongyi County, China, as ex le, and using the hydrological slope unit as the mapping unit, 12 environmental factors including elevation, slope, aspect, profile curvature, plan curvature, topographic relief, lithology, gully density, annual average rainfall, NDVI, NDBI, and road density were selected. Next, the support vector machine (SVM) and random forest (RF) were selected to perform LSP considering the neighborhood characteristics of landslide spatial datasets based on hydrologic slope units. Meanwhile, a grid-based model was also established for comparison. Finally, the LSP uncertainties were analyzed from the prediction accuracy and the distribution patterns of landslide susceptibility indexes (LSIs). Results showed that the improved frequency ratio method using LAI and neighborhood environmental factors can effectively ensure the LSP accuracy, and it was significantly higher than the LSP results without considering the neighborhood conditions. Furthermore, the Wilcoxon rank test in nonparametric test indicates that the neighborhood characteristics of spatial datasets had a great positive influence on the LSP performance.
Publisher: Elsevier BV
Date: 09-2000
Publisher: EDP Sciences
Date: 03-2014
Publisher: Elsevier BV
Date: 11-2018
Publisher: Oxford University Press (OUP)
Date: 23-01-2020
Abstract: We report on the discovery and validation of TOI 813 b (TIC 55525572 b), a transiting exoplanet identified by citizen scientists in data from NASA’s Transiting Exoplanet Survey Satellite (TESS) and the first planet discovered by the Planet Hunters TESS project. The host star is a bright (V = 10.3 mag) subgiant ($R_\\star =1.94\\, R_\\odot$, $M_\\star =1.32\\, M_\\odot$). It was observed almost continuously by TESS during its first year of operations, during which time four in idual transit events were detected. The candidate passed all the standard light curve-based vetting checks, and ground-based follow-up spectroscopy and speckle imaging enabled us to place an upper limit of $2\\, M_{\\rm Jup}$ (99 per cent confidence) on the mass of the companion, and to statistically validate its planetary nature. Detailed modelling of the transits yields a period of $83.8911 _{ - 0.0031 } ^ { + 0.0027 }$ d, a planet radius of 6.71 ± 0.38 R⊕ and a semimajor axis of $0.423 _{ - 0.037 } ^ { + 0.031 }$ AU. The planet’s orbital period combined with the evolved nature of the host star places this object in a relatively underexplored region of parameter space. We estimate that TOI 813 b induces a reflex motion in its host star with a semi- litude of ∼6 m s−1, making this a promising system to measure the mass of a relatively long-period transiting planet.
Publisher: Springer Science and Business Media LLC
Date: 24-06-2016
Publisher: CRC Press
Date: 04-09-2014
DOI: 10.1201/B17435-229
Publisher: Elsevier BV
Date: 08-2019
Publisher: Springer Science and Business Media LLC
Date: 19-04-2020
DOI: 10.1007/S10596-020-09955-4
Abstract: Modeling of hydraulic fracturing processes is of great importance in computational geosciences. In this paper, a phase-field model is developed and applied for investigating the hydraulic fracturing propagation in saturated poroelastic rocks with pre-existing fractures. The phase-field model replaces discrete, discontinuous fractures by continuous diffused damage field, and thus is capable of simulating complex cracking phenomena such as crack branching and coalescence. Specifically, hydraulic fracturing propagation in a rock s le of a single pre-existing natural fracture or natural fracture networks is simulated using the proposed model. It is shown that distance between fractures plays a significant role in the determination of propagation direction of hydraulic fracture. While the rock permeability has a limited influence on the final crack topology induced by hydraulic fracturing, it considerably impacts the distribution of the fluid pressure in rocks. The propagation of hydraulic fractures driven by the injected fluid increases the connectivity of the natural fracture networks, which consequently enhances the effective permeability of the rocks.
Publisher: Elsevier BV
Date: 08-2023
Publisher: Wiley
Date: 03-2009
DOI: 10.1002/CNM.1124
Publisher: Thomas Telford Ltd.
Date: 09-2010
Abstract: One-dimensional consolidation theories for layered soil have been re-examined. Coupled (settlement and excess pore pressure), uncoupled (excess pore pressure only) and the classical Terzaghi equation are solved by the finite-element method. By accounting only for changes in the coefficient of consolidation (c v ), the classical Terzaghi approach is unable to satisfy the flow continuity conditions at the interface between layers.
Publisher: Springer Science and Business Media LLC
Date: 02-2000
DOI: 10.1007/BF02458522
Publisher: CRC Press
Date: 04-09-2014
DOI: 10.1201/B17435-222
Publisher: Elsevier BV
Date: 2024
Publisher: Elsevier BV
Date: 2011
Publisher: CRC Press
Date: 04-09-2014
DOI: 10.1201/B17435-221
Publisher: Hindawi Limited
Date: 03-01-2019
DOI: 10.1155/2019/9736729
Abstract: An original 3D numerical approach for fluid flow in fractured porous media is proposed. The whole research domain is discretized by the Delaunay tetrahedron based on the concept of node saturation. Tetrahedral blocks are impermeable, and fluid only flows through the interconnected interfaces between blocks. Fractures and the porous matrix are replaced by the triangular interface network, which is the so-called equivalent matrix-fracture network (EMFN). In this way, the three-dimensional seepage problem becomes a two-dimensional problem. The finite element method is used to solve the steady-state flow problem. The big finding is that the ratio of the macroconductivity of the whole interface network to the local conductivity of an interface is linearly related to the cubic root of the number of nodes used for mesh generation. A formula is presented to describe this relationship. With this formula, we can make sure that the EMFN produces the same macroscopic hydraulic conductivity as the intact rock. The approach is applied in a series of numerical tests to demonstrate its efficiency. Effects of the hydraulic aperture of fracture and connectivity of the fracture network on the effective hydraulic conductivity of fractured rock masses are systematically investigated.
Publisher: American Society of Civil Engineers
Date: 06-2017
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2008
Publisher: Elsevier BV
Date: 12-2016
Publisher: Springer Science and Business Media LLC
Date: 07-12-2017
DOI: 10.1038/S41598-017-17507-7
Abstract: 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.
Publisher: The Royal Society
Date: 29-07-2009
Abstract: The paper investigates the probability of failure of two-dimensional and three-dimensional slopes using the random finite-element method (RFEM). In this context, RFEM combines elastoplastic finite-element algorithms with random field theory in a Monte Carlo framework. Full account is taken of local averaging and variance reduction over each element, and an exponentially decaying (Markov) spatial correlation function is incorporated. It is found that two-dimensional probabilistic analysis, which implicitly assumes perfect spatial correlation in the out-of-plane direction, may underestimate the probability of failure of slopes.
Publisher: American Society of Civil Engineers (ASCE)
Date: 09-2011
Publisher: American Society of Civil Engineers (ASCE)
Date: 04-2009
Publisher: Informa UK Limited
Date: 26-08-2016
Publisher: Springer Science and Business Media LLC
Date: 15-05-2020
Publisher: Elsevier BV
Date: 2018
Publisher: Wiley
Date: 05-08-2019
DOI: 10.1002/NME.6164
Publisher: Informa UK Limited
Date: 07-09-2015
Publisher: MDPI AG
Date: 12-03-2020
DOI: 10.3390/S20061576
Abstract: Landslide susceptibility prediction (LSP) modeling is an important and challenging problem. Landslide features are generally uncorrelated or nonlinearly correlated, resulting in limited LSP performance when leveraging conventional machine learning models. In this study, a deep-learning-based model using the long short-term memory (LSTM) recurrent neural network and conditional random field (CRF) in cascade-parallel form was proposed for making LSPs based on remote sensing (RS) images and a geographic information system (GIS). The RS images are the main data sources of landslide-related environmental factors, and a GIS is used to analyze, store, and display spatial big data. The cascade-parallel LSTM-CRF consists of frequency ratio values of environmental factors in the input layers, cascade-parallel LSTM for feature extraction in the hidden layers, and cascade-parallel full connection for classification and CRF for landslide/non-landslide state modeling in the output layers. The cascade-parallel form of LSTM can extract features from different layers and merge them into concrete features. The CRF is used to calculate the energy relationship between two grid points, and the extracted features are further smoothed and optimized. As a case study, the cascade-parallel LSTM-CRF was applied to Shicheng County of Jiangxi Province in China. A total of 2709 landslide grid cells were recorded and 2709 non-landslide grid cells were randomly selected from the study area. The results show that, compared with existing main traditional machine learning algorithms, such as multilayer perception, logistic regression, and decision tree, the proposed cascade-parallel LSTM-CRF had a higher landslide prediction rate (positive predictive rate: 72.44%, negative predictive rate: 80%, total predictive rate: 75.67%). In conclusion, the proposed cascade-parallel LSTM-CRF is a novel data-driven deep learning model that overcomes the limitations of traditional machine learning algorithms and achieves promising results for making LSPs.
Publisher: Springer Science and Business Media LLC
Date: 09-08-2017
Start Date: 06-2019
End Date: 12-2023
Amount: $168,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 03-2022
End Date: 03-2025
Amount: $263,112.00
Funder: Australian Research Council
View Funded ActivityStart Date: 08-2017
End Date: 07-2022
Amount: $675,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 07-2018
End Date: 06-2021
Amount: $400,901.00
Funder: Australian Research Council
View Funded ActivityStart Date: 05-2022
End Date: 05-2025
Amount: $453,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 09-2021
End Date: 09-2024
Amount: $713,696.00
Funder: Australian Research Council
View Funded Activity