ORCID Profile
0000-0001-5336-3663
Current Organisation
University of Technology Sydney
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Civil Engineering | Civil engineering | Civil Geotechnical Engineering | Civil geotechnical engineering
Publisher: American Society of Civil Engineers (ASCE)
Date: 05-2022
Publisher: MDPI AG
Date: 10-2023
Publisher: Springer International Publishing
Date: 2018
Publisher: Elsevier BV
Date: 08-2023
Publisher: International Society of Offshore and Polar Engineers
Date: 12-2015
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 28-02-2019
Publisher: Springer Science and Business Media LLC
Date: 07-02-2019
Publisher: Springer Science and Business Media LLC
Date: 29-06-2018
Publisher: Springer Science and Business Media LLC
Date: 17-01-2020
Publisher: Elsevier BV
Date: 08-2021
Publisher: Thomas Telford Ltd.
Date: 2018
Abstract: This study uses an incompressible smoothed-particle hydrodynamics (ISPH) model to investigate the run-out and deposit morphology of granular materials flowing down cut slopes. The primary aim is to study the influence of various factors on the run-out and to summarise a quantitative relationship for direct use in landslide hazard management. In the model, the granular materials are modelled as a rigid perfectly plastic material with a Coulomb yield surface. The coupled continuity equation and momentum equation are solved by a semi-implicit algorithm. The model is first validated and its results are carefully compared with various controlled experiments regarding granular flows. The model reproduces the flows and correctly predicts the deposition profiles under various conditions. Then, the computational results are used to study the run-out and mobility of landslides. For granular columns collapsing onto a flat surface, a normalised run-out and a new scaling relationship are proposed, which are supported by numerous measured and numerical results. A similar relationship for the run-out of granular rectangles on steep slopes has also been explored. It is found that the normalised run-out is mainly determined by the slope angle and the normalised drop height. Furthermore, three types of idealised cut-slope landslides are simulated to study the influence of the initial landslide shape on the run-out. It is found that the normalised run-out of these idealised cut-slope landslides is smaller than that of granular rectangles on slopes of the same angles and drop heights. The difference between the run-outs is found to be mainly determined by the proportion of the whole mass that initially lies above a predictable discontinuity plane.
Publisher: Wiley
Date: 27-05-2018
DOI: 10.1002/NAG.2793
Publisher: Elsevier BV
Date: 02-2020
Publisher: MDPI AG
Date: 19-01-2023
DOI: 10.3390/APP13031361
Abstract: Solid-state (i.e., jammed) granular soils can be prepared into different densities characterised by the mean pressure p and the solid fraction ϕ (i.e., different p-ϕ combinations). The limits for jammed states (i.e., the range of possible p-ϕ) are studied theoretically in the literature or through isotropic compression simulations with the discrete element method (DEM). Shearing also causes unjamming and the critical state is an important reference state for shear deformation. How the jamming limits from isotropic compression tests are related to the critical state is examined in this paper by DEM simulations. Two methods are used to generate isotropic s les. One is the isotropic compression method, which is mainly used for studying jamming in the literature. Possible jammed states from this method lie between two compression lines. The varying-friction methods can generate s les with a larger range of p-ϕ. Isochoric shear tests are conducted on isotropic specimens prepared with both methods. Some specimens reach liquefaction (p′≈ 0) and the others reach the critical state. The obtained critical state p-ϕ line is found to be the same as the loosest jammed state line from the isotropic compression method. Additionally, the critical state stress state is also well described by a Coulomb-type equation in the octahedral profile.
Publisher: Elsevier BV
Date: 11-2021
Publisher: Elsevier BV
Date: 03-2020
Publisher: Springer Science and Business Media LLC
Date: 23-09-2023
DOI: 10.1007/S11440-022-01706-2
Abstract: Data-driven intelligent surrogate models gain popularity recently. Particularly in Monte-Carlo-style stochastic analysis, the influencing factors are considered as inputs, the quantities of interest are considered as outputs, and cheaper-to-evaluate surrogates models are built from a small amount of s le data and are used for the full Monte-Carlo analysis. This paper presents a framework with three innovations: (1) we build surrogate models for a particular problem that covers any possible material properties or boundary conditions commonly encountered in practice, so the models are ready to use, and do not require new data or training anymore. (2) The inputs and outputs to the problem are both spatially variable. Even after discretization, the input and output sizes are in the order of tens of thousands, which is challenging for traditional machine-learning algorithms. We take the footing failure mechanism as an ex le. Two types of neural networks are examined, fully connected networks and deep neural networks with complicated non-sequential structures (a modified U-Net). (3) This study is also the first attempt to use U-Nets as surrogate models for geotechnical problems. Results show that fully connected networks can fit well simple problems with a small input and output size, but fail for complex problems. Deep neural networks that account for the data structure give better results.
Publisher: Springer International Publishing
Date: 2018
Publisher: Elsevier BV
Date: 12-2020
Publisher: Springer Science and Business Media LLC
Date: 10-2017
Publisher: MDPI AG
Date: 13-08-2023
DOI: 10.3390/MATH11163493
Abstract: In landslide risk management, it is important to estimate the run-out distance of landslides (or debris flows) such that the consequences can be estimated. This research presents an innovative array of dimensionless equations that effectively estimate run-out distances, supported by both experimental data and numerical simulations. We employ the coupled Eulerian–Lagrangian (CEL) method to confront the challenges presented in large deformations during landslides. The soil is modelled using the Mohr–Coulomb model, and the failure of cohesionless soil slopes (e.g., sand slopes) is studied. The simulation results are used to study the characteristics of flows and run-out distances. We suggest a normalized run-out and introduce new scaling relationships for it under different conditions such as different plane angles and material properties. The granular flows under different scales can be compared directly with this new scaling law. The new relationships are validated by both experimental and numerical data. Our analysis reveals that the normalized run-out distance in debris flows is contingent on the initial geometry, plane angle, and material properties. An increase in debris volume and plane angle can contribute to an increase in the normalized run-out distance, while a rise in friction angles causes a decrease. In the case of landslides, the normalized run-out distance depends on material properties and the slope angle. An increase in slope angle leads to a corresponding increase in the normalized run-out distance.
Publisher: Springer Science and Business Media LLC
Date: 11-2014
Publisher: Elsevier BV
Date: 10-2020
Publisher: Elsevier BV
Date: 05-2017
Publisher: Springer Science and Business Media LLC
Date: 30-08-2022
Publisher: MDPI AG
Date: 04-09-2023
DOI: 10.3390/SU151713265
Publisher: MDPI AG
Date: 30-05-2023
DOI: 10.3390/ENG4020087
Abstract: Tunnel Boring Machines (TBMs) have become prevalent in tunnel construction due to their high efficiency and reliability. The proliferation of data obtained from site investigations and data acquisition systems provides an opportunity for the application of machine learning (ML) techniques. ML algorithms have been successfully applied in TBM tunnelling because they are particularly effective in capturing complex, non-linear relationships. This study focuses on commonly used ML techniques for TBM tunnelling, with a particular emphasis on data processing, algorithms, optimisation techniques, and evaluation metrics. The primary concerns in TBM applications are discussed, including predicting TBM performance, predicting surface settlement, and time series forecasting. This study reviews the current progress, identifies the challenges, and suggests future developments in the field of intelligent TBM tunnelling construction. This aims to contribute to the ongoing efforts in research and industry toward improving the safety, sustainability, and cost-effectiveness of underground excavation projects.
Publisher: Wiley
Date: 24-04-2018
DOI: 10.1002/NAG.2782
Publisher: Public Library of Science (PLoS)
Date: 24-02-2023
DOI: 10.1371/JOURNAL.PONE.0282265
Abstract: The recent dramatic progress in machine learning is partially attributed to the availability of high-performant computers and development tools. The accelerated linear algebra (XLA) compiler is one such tool that automatically optimises array operations (mostly fusion to reduce memory operations) and compiles the optimised operations into high-performant programs specific to target computing platforms. Like machine-learning models, numerical models are often expressed in array operations, and thus their performance can be boosted by XLA. This study is the first of its kind to examine the efficiency of XLA for numerical models, and the efficiency is examined stringently by comparing its performance with that of optimal implementations. Two shared-memory computing platforms are examined–the CPU platform and the GPU platform. To obtain optimal implementations, the computing speed and its optimisation are rigorously studied by considering different workloads and the corresponding computer performance. Two simple equations are found to faithfully modell the computing speed of numerical models with very few easily-measureable parameters. Regarding operation optimisation within XLA, results show that models expressed in low-level operations (e.g., slice, concatenation, and arithmetic operations) are successfully fused while high-level operations (e.g., convolution and roll) are not. Regarding compilation within XLA, results show that for the CPU platform of certain computers and certain simple numerical models on the GPU platform, XLA achieves high efficiency ( 80%) for large problems and acceptable efficiency (10%~80%) for medium-size problems–the gap is from the overhead cost of Python . Unsatisfactory performance is found for the CPU platform of other computers (operations are compiled in a non-optimal way) and for high-dimensional complex models for the GPU platform, where each GPU thread in XLA handles 4 (single precision) or 2 (double precision) output elements–hoping to exploit the high-performant instructions that can read/write 4 or 2 floating-point numbers with one instruction. However, these instructions are rarely used in the generated code for complex models and performance is negatively affected. Therefore, flags should be added to control the compilation for these non-optimal scenarios.
Publisher: Springer Science and Business Media LLC
Date: 07-11-2019
Publisher: Springer Science and Business Media LLC
Date: 23-01-2021
Publisher: Elsevier BV
Date: 10-2020
Location: United Kingdom of Great Britain and Northern Ireland
Start Date: 2023
End Date: 12-2025
Amount: $450,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2022
End Date: 12-2024
Amount: $403,300.00
Funder: Australian Research Council
View Funded Activity