Civil and Environmental Engineering publications

Permanent URI for this collection

https://hdl.handle.net/2440/908

Browse

Now showing 1 - 20 of 2849

Metadata only
1D compression calculation for composite geomaterial
(Australian Geomechanical Society, 2012) Deng, A.; Zhou, Y.; Australia - New Zealand Conference on Geomechanics (11th : 2012 : Melbourne, Victoria)
A composite geomaterial is formed by blending earth materials with a special material, e.g., expanded polystyrene beads or rubber tire chips, in designated proportions. The composite geomaterial takes the advantages of low unit weight, improved shear strength and integrity, over conventional earth fills, when the materials are used for various geo-infrastructures, e.g., embankments, utility trenches and retaining walls. To study the deformation behaviour of the composite material helps understand the response of the material if subjected to field vertical or lateral loads. In this study, a one-dimensional (1D) compression calculation was developed to depict the compressibility of the material. The calculation was able to account for the effect of mixture proportion, and can be summarised in a unique mathematical form. Case study was illustrated to demonstrate the calculation of the compressibility.
Metadata only
2-D and 3-D finite element analyses for the settlement due to soft ground tunnelling
(Elsevier, 2006) Karakus, M.; Fowell, R.; ITA-AITES 2006 World Tunnel Congress and 32nd ITA General Assembly (2006 : Seoul)
The objective of this paper is to describe and evaluate two-dimensional and three-dimensional Finite Element analyses for New Austrian Tunnelling Method (NATM) in soft ground. Firstly, 2-D plane strain FE analyses were conducted to predict ground response to NATM tunnelling. In order to take account the deformations prior to lining installation Hypothetical Modulus of Elasticity (HME) soft lining approach were adopted. The best 2-D model whose predictions were in a great agreement with the field measurements was found to be sequential excavation model (SEM), which follows closely the construction sequences used in the field. Secondly, three-dimensional FEM analysis has been carried out to predict transverse and longitudinal settlement profiles. Results from the analyses compared with each other as well as field measurements recorded during construction of Heathrow Express Trial tunnel in London clay.
Metadata only
34th Hydrology and Water Resources Symposium, 19-22 November 2012, Dockside, Cockle Bay, Sydney, Australia
(Engineers Australia, 2012) Westra, S.P.
The 2012 Hydrology and Water Resources Symposium addressed the progress made understanding the major uncertainties facing water resources managers in the coming decades and on insights dealing with these challenges. The Symposium proceedings compile 196 papers that provide a lasting record of the Symposium's contribution to the science and practice of hydrology and water resources engineering and management.
Metadata only
3D DC resistivity forward modeling by finite-infinite element coupling method
(Science Press, 2010) Tang, J.; Gong, J.
To solve the problems caused by artificial boundary conditions in conventional finite element modeling, a new 3D DC resistivity finite -infinite element coupling method was proposed. Firstly, the 3D mapping functions of infinite elements were derived. Then, a new type of shape functions was proposed and proved to be the optimal one in both accuracy and time consumption by comparing with several other shape functions. After that, we integrated infinite element method into conventional finite element method to replace the mixed boundary conditions, which made the electrical potential distribute continuously in half space and decay to zero at infinity. Meanwhile, the global system matrix was independent with the locations of source points but still sparse and symmetric. Finally, analyses of numerical tests showed that the finite -infinite coupling method presented in this paper could obtain reasonable numerical solutions in a relatively small meshing area, the accuracy of which was equivalent with that obtained by mixed boundary conditions and higher than that of Neumann boundary conditions. Due to the reduction of the discretization domain and the invariability of the global system matrix with variant source positions, this new method is able to alleviate the computational burden and speed up inversions.
Open Access
3D finite element modeling of absorbing regions for guided wave scattering problems in composite materials
(Global Science and Technology Forum, 2013) Ng, C.
This paper presents a three-dimensional (3D) finite element (FE) model of the absorbing regions for guided wave (GW) scattering in composite materials. The model is aimed at providing an efficient and practical numerical modeling technique for analyzing GW propagation and scattering in infinitely long composite structures. The reported study sought to find the optimal configuration of the absorbing regions to not only minimize the wave reflection from boundaries, but also the computational cost. The model with the optimal absorbing regions was then applied to investigate the GW scattering characteristics for different types and sizes of defects in laminated composite beams.
Metadata only
3D finite element prediction of scattering and mode conversion of lamb waves at delaminations in composite laminates
(International Society for Structural Health Monitoring of Intelligent Infrastructure, 2017) Ng, C.; Pudipeddi, G.; Kotousov, A.; International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII) (5 Dec 2017 - 8 Dec 2017 : Brisbane, Australia); Chan, T.; Mahini, S.
Detection of damage using Lamb wave has attracted significant attention in the last decade. It has been shown that Lamb wave is sensitive to most types of damages, efficient in detecting small and subsurface damage, and able to inspect large area. However, the capability and sensitivity of Lamb wave on detecting delaminations in composite laminates, especially the mode conversion effect, have not yet been fully investigated, which limits quantitative characterization of the delaminations. This study presents a three-dimensional (3D) finite element (FE) simulation of the fundamental anti-symmetric mode (A0) Lamb wave scattering and mode conversion from A0 to fundamental symmetric mode (S0) Lamb wave at the delamination in composite laminates. The 3D FE simulation is first validated using experimentally measured data, such as group velocity and amplitude of the incident Lamb wave in different wave propagation directions. There is good agreement between the FE calculated and experimentally measured results. The 3D FE model is then employed to predict the A0 scattered Lamb wave and mode conversion from A0 to S0 Lamb wave, which provides understanding about the scattering and mode conversion phenomenon of Lamb wave at the delaminations.
Metadata only
3D frequency domain controlled source electromagnetic numerical modeling with coupled finite-infinite element method
(Zhongnan Gongye Daxue/Central South University of Technology, 2014) Tang, J.; Zhang, L.; Gong, J.; Xiao, X.
Due to the large scale of the survey domain, the three dimensional (3D) controlled source electromagnetic (CSEM) numerical modeling usually brings about large amount of data and is very time consuming. To solve these problems, in this study, infinite elements were introduced into the finite element analysis to substitute the conventional artificial boundary conditions, which made the electromagnetic fields diffuse to infinity and attenuate to 0. As a result, the discretization domain for finite elements, as well as the total number of degrees of freedoms and time consumption, can be reduced remarkably. The numerical tests show that with the finite-infinite element coupling algorithm, the finite elements discretization domain is less than 1% of the conventional finite element modeling. The total numbers of freedom degrees are reduced around 33%, and time consumptions are saved around 25% while the accuracy is equal.
Open Access
3D numerical model for dynamic loading-induced multiple fracture zones around underground cavity faces
(Elsevier Sci Ltd, 2013) Tao, M.; Li, X.; Wu, C.
Three dimensional numerical modelling was used to examine the fracture responses around cavities in rock masses experiencing the stress of excavation. In addition to the primary fracture zone in the near-field, numerical modelling generated a second fracture zone in the far-field and an elastic non-fracture zone between the two fields, i.e., fracture and non-fracture zones occurred alternately around a deep cavity. Further research illustrated that the dynamic load and static stress gradient are two necessary precursors for a far-field fracture in the excavation process. Neither quasi-static loading nor homogeneous stress conditions could induce a far-field fracture. A simple theory is introduced, suggesting that multiple fracture zones occur during excavation due to both the initial stress gradient and the dynamic load. This finding indicates that it may be possible to induce continuous rock fractures in deep underground rock masses by employing optimal excavation methods to generate multiple contiguous fracture zones. © 2013 Elsevier Ltd.
Restricted
3D treatment of MASW data for monitoring ground improvement at an uncontrolled fill site
(Taylor and Francis Online, 2009) Suto, K.; Scott, B.T.; International Geophysical Conference and Exhibition (22 Feb 2009 - 25 Feb 2009 : Adelaide, South Australia)
The Multichannel Analysis of Surface Waves, or MASW in short (Park, et al, 1999; Suto, 2007) analyses seismic data in the frequency-velocity domain and estimates the S-wave velocity structure under the seismic receiver array. Its application range varies, commonly from only a few metres to tens of metres, depending on the wavelengths of the surface waves used for analysis. The output from an MASW survey and analysis is essentially a series of 1-dimensional S-wave velocity profiles, generating spatially discrete data points similar to borehole data. As the data are collected along a line and sampled at closely spaced intervals, it is common to present the data in the form of a 2-dimensional section of S-wave velocities along the survey line, rather than a 1-dimensional profile with depth. If an MASW survey consists of closely spaced survey lines, it is possible to present the output of the surveyed area as a 3- dimensional data set. This paper presents an example of an application of the MASW survey method at a landfill site, with data presented in plan view with a number of depth slices.
Metadata only
A bamboo-inspired nanostructure design for flexible, foldable, and twistable energy storage devices
(American Chemical Society, 2015) Sun, Y.; Sills, R.B.; Hu, X.; Seh, Z.W.; Xiao, X.; Xu, H.; Luo, W.; Jin, H.; Xin, Y.; Li, T.; Zhang, Z.; Zhou, J.; Cai, W.; Huang, Y.; Cui, Y.
Flexible energy storage devices are critical components for emerging flexible electronics. Electrode design is key in the development of all-solid-state supercapacitors with superior electrochemical performances and mechanical durability. Herein, we propose a bamboo-like graphitic carbon nanofiber with a well-balanced macro-, meso-, and microporosity, enabling excellent mechanical flexibility, foldability, and electrochemical performances. Our design is inspired by the structure of bamboos, where a periodic distribution of interior holes along the length and graded pore structure at the cross section not only enhance their stability under different mechanical deformation conditions but also provide a high surface area accessible to the electrolyte and low ion-transport resistance. The prepared nanofiber network electrode recovers its initial state easily after 3-folded manipulation. The mechanically robust membrane is explored as a free-standing electrode for a flexible all-solid-state supercapacitor. Without the need for extra support, the volumetric energy and power densities based on the whole device are greatly improved compared to the state-of-the-art devices. Even under continuous dynamic operations of forceful bending (90°) and twisting (180°), the as-designed device still exhibits stable electrochemical performances with 100% capacitance retention. Such a unique supercapacitor holds great promise for high-performance flexible electronics.
Metadata only
A basis function approach for exploring the seasonal and spatial features of storm surge events
(American Geophysical Union, 2017) Wu, W.; Westra, S.; Leonard, M.
Storm surge is a significant contributor to flooding in coastal and estuarine regions. To represent the statistical characteristics of storm surge over a climatologically diverse region, we propose the use of basis functions that capture the temporal progression of individual storm surge events. This extends statistical analyses of surge from considering only the peak to a more multifaceted approach that also includes decay rate and duration. Our results show that there is seasonal variation in storm surge along the Australian coastline. During the dominant storm surge seasons, the peak and duration of storm surge events tend to increase simultaneously at a number of locations, with implications for flood damage assessments and evacuation planning. By combining the dynamic and statistical features of storm surge, it is possible to better understand the factors that can lead to flood risk along the coastline, including estuarine areas that are also affected by fluvial floods.
Metadata only
A Bayesian analysis of sensible heat flux estimation: quantifying uncertainty in meteorological forcing to improve model prediction
(American Geophysical Union, 2013) Ershadi, A.; McCabe, M.; Evans, J.; Mariethoz, G.; Kavetski, D.
The influence of uncertainty in land surface temperature, air temperature, and wind speed on the estimation of sensible heat flux is analyzed using a Bayesian inference technique applied to the Surface Energy Balance System (SEBS) model. The Bayesian approach allows for an explicit quantification of the uncertainties in input variables: a source of error generally ignored in surface heat flux estimation. An application using field measurements from the Soil Moisture Experiment 2002 is presented. The spatial variability of selected input meteorological variables in a multitower site is used to formulate the prior estimates for the sampling uncertainties, and the likelihood function is formulated assuming Gaussian errors in the SEBS model. Land surface temperature, air temperature, and wind speed were estimated by sampling their posterior distribution using a Markov chain Monte Carlo algorithm. Results verify that Bayesian-inferred air temperature and wind speed were generally consistent with those observed at the towers, suggesting that local observations of these variables were spatially representative. Uncertainties in the land surface temperature appear to have the strongest effect on the estimated sensible heat flux, with Bayesian-inferred values differing by up to ±5°C from the observed data. These differences suggest that the footprint of the in situ measured land surface temperature is not representative of the larger-scale variability. As such, these measurements should be used with caution in the calculation of surface heat fluxes and highlight the importance of capturing the spatial variability in the land surface temperature: particularly, for remote sensing retrieval algorithms that use this variable for flux estimation.
Metadata only
A Bayesian approach to artificial neural network model selection
(Modelling and Simulation Society of Australia and New Zealand Inc., 2005) Humphrey, G.; Maier, H.; Lambert, M.; International Congress on Modelling and Simulation (16th : 2005 : Melbourne, Victoria); Zerger, A.; Argent, R.
Artificial neural networks (ANNs) have proven to be extremely valuable tools in the field of water resources engineering. However, one of the most difficult tasks in developing an ANN is determining the optimum level of complexity required to model a given problem, as there is no formal systematic model selection method. The generalisability of an ANN, which is defined by its predictive performance on the universe of possible data, can be significantly impaired if there are too few or too many hidden nodes in the network. Therefore, for an ANN to be a valuable prediction tool, it is important that some effort is made to optimise the number of hidden nodes. This paper presents a Bayesian model selection (BMS) method for ANNs that provides an objective approach for comparing models of varying complexity in order to select the most appropriate ANN structure. Given a set of competing models ℋ1, . . . ,ℋH, BMS is used to compare the posterior probability that each model ℋi is the true data generating function, given a set of observed data y. This probability is also known as the evidence of a model and the ratio of two competing models' evidence values, known as the Bayes' factor, can be used to rank the competing models in terms of the relative evidence in support of each model. For ANNs (and other complex models), the evidence of a model p(ℋ|y) is analytically intractable and, consequently, alternative methods are required to estimate these probabilities for the competing models. One such method involves the use of Markov chain Monte Carlo (MCMC) simulations from the posterior weight distribution p(w|y, ℋ) to approximate the evidence. It has already been shown that there are numerous benefits to estimating the posterior distribution of ANN weights with MCMC methods; therefore, the proposed BMS approach is based on such an approximation of p(y|ℋ), as this only requires a simple additional step after sampling from p(w|y, ℋ). Furthermore, the weight distributions obtained from the MCMC simulation provide a useful check of the accuracy to the approximated Bayes' factors. A problem associated with the use of posterior simulations to estimate a model's evidence is that the approximation may be sensitive to factors associated with the MCMC simulation. Therefore, the proposed BMS method for ANNs incorporates a further check of the accuracy of the computed Bayes' factors by inspecting the marginal posterior distributions of the hidden-to-output layer weights, which indicate whether all of the hidden nodes in the model are necessary. The fact that this check is available is one of the greatest advantages of the proposed approach over conventional model selection methods, which do not provide such a test and instead rely on the modeller's subjective choice of selection criterion. The aim of model selection is to enable generalisation to new cases. Therefore, in the case study presented in this paper, the performance of the proposed BMS method was assessed in comparison to the performance of conventional ANN selection methods on data outside the domain of the training data. This case study, which involves forecasting salinity concentrations in the River Murray at Murray Bridge, South Australia, 14 days in advance, was chosen as it had been shown previously that, if an ANN was trained on the first half of the available data, it would be required to extrapolate in some cases when applied to the second half of the available data set. In this case study, the proposed BMS framework for ANNs was shown to be more successful than conventional model selection methods in selecting an ANN that could approximate the relationship contained in the training data and generalise to new cases outside the domain of those used for training. The Bayes' factors calculated were useful for obtaining an initial guide to the most appropriate model; however, the final step involving inspection of marginal posterior hidden-tooutput weight distributions was necessary for the final selection of the optimum number of hidden nodes. The model selected using the proposed BMS approach not only had the best generalisability, but was also more parsimonious than the models selected using conventional methods and required considerably less time for training.
Metadata only
A Bayesian method to improve the extrapolation ability of ANNs
(ACTA Press, 2005) Humphrey, G.; Maier, H.; Lambert, M.; International Conference on Applied Simulation and Modelling (14th : 2005 : Benalmadena, Spain); Hamza, M.
Although artiﬁcial neural networks have been shown to be superior prediction models in many hydrology-related areas, their known lack of extrapolation capability has limited the wider use and acceptance of ANNs as forecasting models. This problem lies mainly with the fact that a single 'most likely' weight vector, which is determined by calibration with a ﬁnite set of data, is used to deﬁne the function modelled by the ANN. There are, in fact, many different weight vectors that result in approximately equal model performance; however, standard ANN development approaches do not allow for any weight vectors, other than that which provides the best ﬁt to the calibration data, to impact on the predictions made. In this paper, a Bayesian method is presented that enables the entire range of plausible weight vectors to be accounted for in the model predictions. In doing so, the relationship modelled by the ANN is more general and less dominated by the information contained in the calibration data. The method is applied to a real-world case study known to require extrapolation and the resulting ANN is shown to perform signiﬁcantly better than an ANN developed using standard approaches.
Metadata only
A behavioural approach for household outdoor water use modelling
(Engineers Australia, 2011) Micevski, T.; Thyer, M.; Kuczera, G.; IAHR World Congress (34th : 2011 : Brisbane, Australia)
Reliable predictions of household outdoor water use are important inputs for effective design and management of urban water systems. This paper evaluates and enhances the behavioural approach (BA) for modelling outdoor water use. The underlying premise in the BA is that outdoor water use is governed by people's probabilistic behavioural response to recent weather conditions (rainfall and temperature).The BA models used in this paper were evaluated using a 12 year dataset of monthly outdoor water use for 135 homes in the Newcastle region of New South Wales, Australia. The BA model of Coombes et al (2000) was found to found to outperform traditional linear regression techniques, after calibration using a new simulated likelihood calibration approach. However, it was found to be over-parameterised and underestimated observed variability by 22%. An enhanced BA model was more parsimonious and better simulated the observed variability (only 9% underestimation). Conditioning behavioural response on daily rainfall and maximum temperature did not provide good model performance. Rather, the major drivers of household outdoor water use variability were found to be long dry periods (for 80% of homes), while a smaller number (20%) additionally responded to the long hot periods (characterised by the degree day concept).
Metadata only
A behavioural approach to stochastic end use modelling
(2011) Thyer, M.; Micevski, T.; Kuczera, G.; Coombes, P.; Australia's National Water Conference and Exhibition (2011 : Adelaide, South Australia)
Metadata only
A benchmarking approach for comparing data splitting methods for modeling water resources parameters using artificial neural networks
(Amer Geophysical Union, 2013) Wu, W.; May, R.; Maier, H.; Dandy, G.
Data splitting is an important step in the artificial neural network (ANN) development process, whereby the available data are divided into training, testing, and validation subsets to ensure good generalization ability of the model. Considering that only one split of the data is typically used when developing ANN models, data splitting has a significant impact on model performance, depending on which data are allocated to the three subsets. Therefore, it is important to find a data splitting method that consistently results in predictive validation errors that are representative of the predictive errors obtained over the full range of the available data. This paper addresses this issue by introducing a benchmarking approach for comparing different data splitting methods in terms of (1) bias, which is the difference between the expected validation performance over the entire data set and that obtained using a particular data splitting method and (2) variability, which is the spread of the validation errors obtained by repeated implementation of that method. The utility of the proposed approach is assessed on a number of well‐known data splitting methods in the context of four water resources ANN modelling problems. The results obtained indicate that the proposed approach for comparing data splitting methods is more representative than the previous approach where a value of zero is used as the predictive performance benchmark, as it can avoid the selection of an over‐optimistic data splitting method that under‐represents extreme data in the validation set.
Metadata only
A borehole stability study by newly designed laboratory tests on thick-walled hollow cylinders
(Elsevier, 2015) Hashemi, S.; Melkoumian, N.; Taheri, A.
Abstract not available
Metadata only
A bottom-up approach to identifying the maximum operational adaptive capacity of water resource systems to a changing climate
(American Geophysical Union, 2016) Culley, S.; Noble, S.; Yates, A.; Timbs, M.; Westra, S.; Maier, H.R.; Giuliani, M.; Castelletti, A.
Many water resource systems have been designed assuming that the statistical characteristics of future inflows are similar to those of the historical record. This assumption is no longer valid due to large-scale changes in the global climate, potentially causing declines in water resource system performance, or even complete system failure. Upgrading system infrastructure to cope with climate change can require substantial financial outlay, so it might be preferable to optimize existing system performance when possible. This paper builds on decision scaling theory by proposing a bottom-up approach to designing optimal feedback control policies for a water system exposed to a changing climate. This approach not only describes optimal operational policies for a range of potential climatic changes but also enables an assessment of a system's upper limit of its operational adaptive capacity, beyond which upgrades to infrastructure become unavoidable. The approach is illustrated using the Lake Como system in Northern Italy—a regulated system with a complex relationship between climate and system performance. By optimizing system operation under different hydrometeorological states, it is shown that the system can continue to meet its minimum performance requirements for more than three times as many states as it can under current operations. Importantly, a single management policy, no matter how robust, cannot fully utilize existing infrastructure as effectively as an ensemble of flexible management policies that are updated as the climate changes.
Metadata only
A boundary element method for a class of elliptic boundary value problems of functionally graded media
(Elsevier, 2017) Salam, N.; Haddade, A.; Clements, D.; Azis, M.
Abstract not available

Browse

Browsing Civil and Environmental Engineering publications by Title

Results Per Page

Sort Options