Infrastructure Engineering - Research Publications

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    A macro-element model for predicting the combined load behaviour of spudcan foundations in clay overlying sand
    Wang, Y ; Cassidy, MJ ; Bienen, B (Thomas Telford Ltd., 2021-10-26)
    A macro-element model for predicting the load–displacement behaviour of a spudcan foundation in clay overlying sand when subjected to combined vertical, horizontal and moment loading is introduced. Observations from detailed drum centrifuge tests that measured the effect of the underlying sand layer on the foundation behaviour are combined with finite-element results and theoretical developments to derive the components of the model. The yield surface defined by the centrifuge test results suggests that as the spudcan nears the underlying sand layer, the absolute horizontal capacity remains relatively constant, while the vertical and moment capacities increase at approximately the same normalised rate. The model is demonstrated to accurately predict foundation behaviour by retrospectively simulating the experimental results. This macro-element model has the advantage that it can be integrated into the structural analyses of jack-up platforms required for site-specific assessments.
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    Feasibility and performance analysis of hybrid ground source heat pump systems in fourteen cities
    Weeratunge, H ; Aditya, GR ; Dunstall, S ; de Hoog, J ; Narsilio, G ; Halgamuge, S (PERGAMON-ELSEVIER SCIENCE LTD, 2021-11-01)
    Ground source heat pump systems (GSHP) for residential building heating, cooling, and hot water are highly energy efficient but capital intensive when sized for peak demands. The use of supplemental sources of energy with GSHP systems enables improved life-cycle economics through the reduction in the size and cost of the GSHP components. This paper investigates the life-cycle economics of hybrid solar-assisted ground source heat pump systems (SAGSHP) using simulations validated from field data. The economics and optimal sizing of SAGSHP systems for heating dominant climates in four locations in Australia and ten locations elsewhere are evaluated in order to explore the suitability and relative merits of SAGSHP systems in a range of heating dominant climates. In locations having high or moderate levels of solar irradiation, high electricity prices, and high or moderate gas prices, SAGSHP systems are shown to have the lowest life cycle cost amongst alternatives, with predicted savings of up to 30%.
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    Innovative composite structural systems for modular tall buildings
    Thai, HT ; Knobloch, M ; Kuhlmann, U ; Kurz, W ; Schafer, M (https://www.compositeconstructionix.com/, 2021)
    Modular or offsite construction is believed to shape the future of the construction industry as it possesses significant benefits over traditional onsite construction methods. However, most of its application are limited to steel or concrete buildings. Although steel-concrete composite structural system has many merits over the steel and concrete systems, its application in modular buildings is very limited. This paper explores recent developments of composite systems for modular high-rise buildings. They include modular units for resisting vertical gravity loads and lateral structural systems for resisting horizontal forces from wind and earthquake loadings and progressive collapse due to accidental loads such as fire, explosions and impact. Various inter-module joining methods developed in the literature will also be reviewed. Finally, a case study of the most efficient connection is presented to explore its applicability to high-rise modular buildings.
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    Privacy- and context-aware release of trajectory data
    Naghizade, E ; Kulik, L ; Tanin, E ; Bailey, J (ACM, 2020-03)
    The availability of large-scale spatio-temporal datasets along with the advancements in analytical models and tools have created a unique opportunity to create valuable insights into managing key areas of society from transportation and urban planning to epidemiology and natural disasters management. This has encouraged the practice of releasing/publishing trajectory datasets among data owners. However, an ill-informed publication of such rich datasets may have serious privacy implications for individuals. Balancing privacy and utility, as a major goal in the data exchange process, is challenging due to the richness of spatio-temporal datasets. In this article, we focus on an individual's stops as the most sensitive part of the trajectory and aim to preserve them through spatio-temporal perturbation. We model a trajectory as a sequence of stops and moves and propose an efficient algorithm that either substitutes sensitive stop points of a trajectory with moves from the same trajectory or introduces a minimal detour if no safe Point of Interest (POI) can be found on the same route. This hinders the amount of unnecessary distortion, since the footprint of the original trajectory is preserved as much as possible. Our experiments shows that our method balances user privacy and data utility: It protects privacy through preventing an adversary from making inferences about sensitive stops while maintaining a high level of similarity to the original dataset.
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    Detecting and explaining long-term changes in river water quality in south-eastern Australia
    He, Z ; Yao, J ; Lu, Y ; Guo, D (WILEY, 2022-11)
    Abstract Understanding the temporal changes in river water quality is important for catchment water quality management. This study aims to detect and attribute long‐term trends and abrupt changes in river water quality. We used 26 years of water quality data (1994–2020) collected from 102 river monitoring sites across Victoria, south‐eastern Australia. We analysed six water quality constituents that are of key concerns for Australian catchment management, namely: electrical conductivity (EC), total suspended solids, nitrate‐nitrite, total Kjeldahl nitrogen, total phosphorous and filtered reactive phosphorus. To detect trends and abrupt changes in water quality at each site, a Bayesian ensemble modelling approach was applied, namely, the Bayesian estimator of abrupt change, seasonal change, and trend (BEAST). To explain water quality trends, we then built multivariate regressions to link water quality with streamflow and seasonality, and then compared alternative model structures with and without a change in the regression relationships informed by the changes detected by BEAST. Among the six constituents studied, EC shows the most distinct systematic trends, with 21 sites having a significant increase followed by a non‐significant trend; within the 21 sites, 14 had a significant change point in EC around Year 2010. The regression analyses between water quality and streamflow suggested that the observed systematic change in EC could be largely related to reduced streamflow during the Millennium drought, which greatly impacted the climate and hydrology of south‐eastern Australia over the first decade of 2000. The results of this study can help inform the design of effective mitigation strategies and avoid further degradation of water quality across Victoria. Besides, our trend analysis and attribution approaches are applicable to water quality time series in other regions for robust trend analysis and change point detection.
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    Prediction of the post-failure behavior of rocks: Combining artificial intelligence and acoustic emission sensing
    Yousefpour, N ; Pouragha, M (WILEY, 2022-07)
    Abstract Acoustic emission (AE) reading is among the most common methods for monitoring the behavior of brittle materials such as rock and concrete. This study uses discrete element method (DEM) simulations to explore the correlations between the pre‐failure AE readings with the post‐failure behavior and residual strength of rock masses. The deep learning (DL) method based on long short‐term memory (LSTM) algorithms has been applied to generate predictive models based on the data from DEM simulations of biaxial compression. The dataset has been populated by varying interparticle friction while keeping bond cohesion constant. Various configurations of the LSTM algorithm were evaluated considering different scenarios for input features (strain, stress, and AE energy records) and a range of values for the key hyperparameters. The prime AI models show promising accuracy in predicting residual strength decay with strain based on pre‐failure patterns in AE readings. The results indicate that the pre‐failure AE indeed encapsulates information about the developing failure mechanisms and the post‐failure response in rocks, which can be captured through artificial intelligence.
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    Quantitative contribution of the Grain for Green Program to vegetation greening and its spatiotemporal variation across the Chinese Loess Plateau
    Song, Y ; Wang, Y ; Jin, L ; Shi, W ; Aryal, J ; Comber, A (WILEY, 2022-07-15)
    Abstract A distinct greening trend is evident in Asia, especially on the Loess Plateau (LP) of China, which is driven by climate change and large‐scale land‐use‐related ecological projects, especially the 'Grain for Green Program' (GFGP). However, the specific contributions of the GFGP to vegetation greening and the variation of this greening on a large spatiotemporal scale are not yet clear. We used long‐time‐series normalized difference vegetation index datasets and climate datasets based on the double mass curve method to quantify the contributions of ecological projects and climate change to the greening trend on the LP. We found that the interannual fluctuation of vegetation change was likely related to the interannual fluctuation of climate, especially precipitation. The increasing trend of vegetation change after 2005 indicated that the GFGP, as a type of external disturbance, began to improve vegetation growth. The GFGP failed initially to make a positive contribution in the first few years because of the drought conditions in 1999 and 2005. The increased precipitation played a critical role in enhancing the output of the GFGP on the LP after 2005. Then, the contribution of the GFGP increased quickly until 2013, after which it remained stable and reached average values of 58.8% ± 19.34% and 31.7% ± 24.3% in the representative areas that conducted the GFGP and in other regions with a lower implementation intensity of the GFGP, respectively. Our results highlight the contribution the GFGP has made to spatiotemporal variation due to the spatial heterogeneity of the projects, their intensity and the effect of forest stand age.
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    Modeling consolidation of soft clay by developing a fractional differential constitutive model in conjunction with an intelligent displacement inversion method
    Liu, Z ; Hu, W ; Ming, W ; Xiong, S ; Zhou, C ; Zhang, L ; Samui, P (PUBLIC LIBRARY SCIENCE, 2022-09-30)
    Studying the constitutive relation of soft clays is of critical importance for fundamentally understanding their complex consolidation behavior. This study proposes a fractional differential constitutive model in conjunction with an intelligent displacement inversion method based on the classic particle swarm optimization for modeling the deformation behavior of soft clay. The model considered the rheological properties of soft clay at different consolidation stages. In addition, statistical adaptive dynamic particle swarm optimization-least squares support vector machines were implemented to identify the model parameters efficiently. The accuracy and effectiveness of the model were validated using available experimental results. Finally, the application results showed that the proposed model could efficiently simulate coupling properties of soft clay's primary and secondary consolidations.
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    Modelling attenuation of irregular wave fields by artificial ice floes in the laboratory
    Toffoli, A ; Pitt, JPA ; Alberello, A ; Bennetts, LG (ROYAL SOC, 2022-10-31)
    A summary is given on the utility of laboratory experiments for gaining understanding of wave attenuation in the marginal ice zone, as a complement to field observations, theory and numerical models. It is noted that most results to date are for regular incident waves, which, combined with the highly nonlinear wave-floe interaction phenomena observed and measured during experimental tests, implies that the attenuation of regular waves cannot necessarily be used to infer the attenuation of irregular waves. Two experiments are revisited in which irregular wave tests were conducted but not previously reported, one involving a single floe and the other a large number of floes, and the transmission coefficients for the irregular and regular wave tests are compared. The transmission spectra derived from the irregular wave tests agree with the regular wave data but are overpredicted by linear models due to nonlinear dissipative processes, regardless of floe configuration. This article is part of the theme issue 'Theory, modelling and observations of marginal ice zone dynamics: multidisciplinary perspectives and outlooks'.
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    Pedestrian Origin-Destination Estimation Based on Multi-Camera Person Re-Identification
    Li, Y ; Sarvi, M ; Khoshelham, K ; Zhang, Y ; Jiang, Y (MDPI, 2022-10)
    Pedestrian origin-destination (O-D) estimates that record traffic flows between origins and destinations, are essential for the management of pedestrian facilities including pedestrian flow simulation in the planning phase and crowd control in the operation phase. However, current O-D data collection techniques such as surveys, mobile sensing using GPS, Wi-Fi, and Bluetooth, and smart card data have the disadvantage that they are either time consuming and costly, or cannot provide complete O-D information for pedestrian facilities without entrances and exits or pedestrian flow inside the facilities. Due to the full coverage of CCTV cameras and the huge potential of image processing techniques, we address the challenges of pedestrian O-D estimation and propose an image-based O-D estimation framework. By identifying the same person in disjoint camera views, the O-D trajectory of each identity can be accurately generated. Then, state-of-the-art deep neural networks (DNNs) for person re-ID at different congestion levels were compared and improved. Finally, an O-D matrix based on trajectories was generated and the resident time was calculated, which provides recommendations for pedestrian facility improvement. The factors that affect the accuracy of the framework are discussed in this paper, which we believe could provide new insights and stimulate further research into the application of the Internet of cameras to intelligent transport infrastructure management.