Infrastructure Engineering - Research Publications
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ItemSpatial and Spatiotemporal Matching Framework for Causal Inference(Schloss Dagstuhl, 2022-09-01)Matching is a procedure aimed at reducing the impact of observational data bias in causal analysis. Designing matching methods for spatial data reflecting static spatial or dynamic spatio-temporal processes is complex because of the effects of spatial dependence and spatial heterogeneity. Both may be compounded with temporal lag in the dependency effects on the study units. Current matching techniques based on similarity indexes and pairing strategies need to be extended with optimal spatial matching procedures. Here, we propose a decision framework to support analysts through the choice of existing matching methods and anticipate the development of specialized matching methods for spatial data. This framework thus enables to identify knowledge gaps.
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ItemThermal and energy performance evaluation of a full-scale test cabin equipped with PCM embedded radiant chilled ceiling(Elsevier BV, 2023-06-01)The escalating global demand for space cooling has led to the emergence of new cooling technologies, including the phase change material embedded radiant chilled ceiling (PCM-RCC) system. This technology improves energy efficiency and indoor environmental quality, while also offering demand-side flexibility. The present study experimentally evaluates the thermal efficiency and energy performance of a PCM-RCC system in a full-scale test cabin equipped with PCM panels. Here, the transient thermal behaviour of PCM ceiling panels besides the cooling energy delivered during charging-discharging cycles are examined. The indoor thermal comfort and peak electricity demand reduction enabled by the present PCM-RCC are also discussed. The results reveal that chilled water circulation for 4–5 h overnight was sufficient to fully recharge the PCM panels. Over 80% of the occupancy time was classified as “Class B″ thermal comfort according to ISO 7730. The system's daily electricity usage was mostly concentrated during off-peak hours, accounting for ∼70% of the total usage. While the controlling schedule used in this study responded to the transient thermal behaviour of the indoor space and PCM ceiling panels, a more dynamic, predictive schedule is necessary to improve the system's overall efficiency and further enhance indoor thermal comfort in response to the changing environmental conditions.
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ItemUnderstanding uncertainties in contemporary and future extreme wave events for broad-scale impact and adaptation planning(AMER ASSOC ADVANCEMENT SCIENCE, 2023-01-13)Understanding uncertainties in extreme wind-wave events is essential for offshore/coastal risk and adaptation estimates. Despite this, uncertainties in contemporary extreme wave events have not been assessed, and projections are still limited. Here, we quantify, at global scale, the uncertainties in contemporary extreme wave estimates across an ensemble of widely used global wave reanalyses/hindcasts supported by observations. We find that contemporary uncertainties in 50-year return period wave heights ([Formula: see text]) reach (on average) ~2.5 m in regions adjacent to coastlines and are primarily driven by atmospheric forcing. Furthermore, we show that uncertainties in contemporary [Formula: see text] estimates dominate projected 21st-century changes in [Formula: see text] across ~80% of global ocean and coastlines. When translated into broad-scale coastal risk analysis, these uncertainties are comparable to those from storm surges and projected sea level rise. Thus, uncertainties in contemporary extreme wave events need to be combined with those of projections to fully assess potential impacts.
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ItemTemporal pattern mining of urban traffic volume data: a pairwise hybrid clustering method(Taylor and Francis Group, 2023)Multiple pattern analyses of traffic data have been conducted previously; however, it has yet to be explored with an awareness of temporal factors in big real-world traffic data. In this paper, we introduce a hybrid method to measure the intensity of differences among various temporal factors’ data. The proposed method can efficiently process the historical data given temporal factors and provide insightful information about the intensity of variations. After data denoising with basis splines, we reshape the time series into a 2-D latent space using Principal Component Analysis (PCA) according to the type of analysis. Pairwise K-means clustering is then applied after anomaly elimination with DBSCAN to derive Adjusted Rand Index (ARI) matrices. Finally, these matrices are then systematically used to find similar patterns of different temporal perspectives. Multiple analyses are carried out with real data from Melbourne, Australia. Dissimilarities with intensities of up to 80% are detected that are not detectable with general clustering approaches.
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ItemNo Preview AvailableIntelligent vehicle pedestrian light (IVPL): A deep reinforcement learning approach for traffic signal control(Elsevier, 2023-04-01)Deep reinforcement learning (RL) has been widely studied in traffic signal control. Despite the promising results that indicate the superiority of deep RL in terms of the quality of solution and optimality over fixed time signal control, the real-world multi-modal traffic flows, especially pedestrians, are not properly considered nor sufficiently investigated. This study presents a novel deep RL-based adaptive traffic signal model to control the vehicles and pedestrian flows by allocating an equitable green time to each, aiming at minimizing “total user delays” as opposed to “total vehicle delays” dominantly being used in the literature. Our proposed intelligent vehicle pedestrian light (IVPL) method can perform in the absence or presence of pedestrians, especially when there is jaywalking at the intersection, interrupting vehicle flows. To this end, an extended reward function is designed to capture delays due to vehicle-to-vehicle, vehicle-to-pedestrian, and pedestrian-to-pedestrian interactions, as well as red-light delays for vehicles and pedestrians. To evaluate the performance of IVPL, a microsimulation model of an intersection in city of Melbourne is used as a case-study. The real traffic signal parameters of an existing operation system (SCATS) are employed, and the simulation is calibrated using video-based camera data and loop detectors data collected at intersection. The experimental results demonstrate the superiority of the proposed model over fully actuated traffic signal, not only in terms of the quality of optimal solution, but also considering the fact that the proposed model can minimize the “total user delays”.
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ItemAnalysis of the temporal profile of use of bikesharing stations in the Bikesampa system = Análise temporal do perfil de uso das estações de bicicletas compartilhadas do sistema Bikesampa(Associação Nacional de Pesquisa e Ensino em Transportes (ANPET), 2023)Bikeshare systems have gained popularity in recent years, so they need to be responsive to demand. Therefore, it is necessary to understand the temporal behavior of these trips. This research studied the system of fixed stations Bikesampa, in the city of São Paulo, and aimed to classify the stations according to the hourly demand for withdrawals and returns. A k-means grouping was used, resulting in three groupsof stations: (i) balanced, (ii) unbalanced, with a greater number of withdrawals in the morning, and (iii) unbalanced, with a greater number of returns in the morning. It was verified through the spatial autocorrelation analysis that the groups are not randomly distributed in space, suggesting an association with urban space characteristics and the need for different rebalancing strategies between stations depending on location. Knowledge of the temporal behavior of shared bicycle trips allows the development of operating policies and user incentives to improve the efficiency of these systems. Os sistemas de bicicletas compartilhadas ganharam popularidade nos últimos anos, de forma que precisam ser responsivos à demanda. Logo, é necessário entender o comportamento temporal destas viagens. Esta pesquisa estudou o sistema de estações fixas Bikesampa, da cidade de São Paulo, e teve como objetivo classificar as estações segundo a demanda horária de retiradas e devoluções. Utilizou-se um agrupamento k-médias, resultando em três grupos de estações: (i) balanceado, (ii) desbalanceado, com maior número de retiradas na manhã, e (iii) desbalanceado, com maior número de devoluções na manhã. Verificou-se por meio da análise de autocorrelação espacial que os grupos não se distribuem aleatoriamente no espaço, sugerindo uma associação com características do espaço urbano e a necessidade de diferentes estratégias de rebalanceamento entre estações dependendo da localização. O conhecimento do comportamento temporal das viagens de bicicletas compartilhadas permite o desenvolvimento de políticas de operação e de incentivo ao usuário para melhorar a eficiência desses sistemas.
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ItemPlanning for the majorities: are the charging needs and preferences of electric vehicle early adopters similar to those of mainstream consumers?(Oxford University Press (OUP), 2023-02-01)The mass deployment of electric vehicles (EVs) may bring significant challenges to the electricity sector. However, many of these challenges can be converted into opportunities depending on how and when consumers decide to charge their vehicles. While there are currently multiple efforts worldwide investigating EV charging behaviour, these efforts measure the behaviour of EV early adopters and may not represent the actual behaviour of the mainstream consumer. The current study uses data from a survey with near a thousand Australian consumers to shed light on the potential similarities and differences between the charging needs and preferences of EV early adopters and mainstream consumers. We find that consumer groups vary in terms of charging needs, perceived access to residential charging, and acceptance of direct charging control and management by suppliers. Our conclusions point to (i) the need for campaigns that increase the awareness and understanding of residential EV charging by mainstream consumers; (ii) the significant interest across all consumer groups in free workplace charging, which could together with residential demand management strategies leverage the use of solar energy for charging; and (iii) the need for utility plans and management strategies that enhance the mainstream consumer sense of control over charging together with their perceived monetary savings.
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ItemNo Preview Available
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ItemDigitalising modular construction: Enhancement of off-site manufacturing productivity via a manufacturing execution & control (MEC) system(Elsevier, 2023-03-02)Modular construction facilities operate under tight deadlines making the monitoring of manufacturing progress and delivery for optimal factory-to-site operations a significant challenge. This paper presents an action research study that assisted in enhancing the productivity of a manufacturing execution and control system implemented at a facility in Melbourne, Australia. This system monitors production processes, and shares information for timely, well-informed decision-making using an online platform and a mobile app, developed using lean principles. The results showed significant time and cost savings, quality improvement and increased visibility of inefficiencies and will serve as a helpful reference for construction factory managers wishing to improve their manufacturing systems.
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ItemNo Preview AvailablePerformance of a wheat yield prediction model and factors influencing the performance: A review and meta-analysis(ELSEVIER SCI LTD, 2021-09-22)CONTEXT: Process-based crop models provide ways to predict crop growth, evaluate environmental impacts on crops, test various crop management options, and guide crop breeding. They can be used to explore options for mitigating climate change impacts when combined with climate projections and explore mitigation of environmental impacts of production. The Agricultural Production Systems SIMulator (APSIM) is a widely adopted crop model that offers modules for simulation of various crops, soil processes, climate, and grazing within a modelling system that enables robust addition of new components. OBJECTIVE: This study uses APSIM Classic-Wheat as an example to examine yield prediction accuracy of biophysically based crop yield modelling and to analyse the factors influencing the model performance. METHODS: We analysed yield prediction results of APSIM Classic-Wheat from 76 published studies across thirteen countries on four continents. In addition, a meta-database of modelled and observed yields from 30 studies was established and used to identify factors that influence yield prediction uncertainty. RESULTS AND CONCLUSIONS: Our analysis indicates that, with site-specific calibration, APSIM predicts yield with a root mean squared error (RMSE) smaller than 1 t/ha and a normalised RMSE (NRMSE) of about 28%, across a wide range of environmental conditions for independent evaluation periods. The results show increasing errors in yield with limited modelling information and adverse environmental conditions. Using soil hydraulic parameters derived from site-specific measurements and/or tuning cultivar parameters improves yield prediction accuracy: RMSE decreases from 1.25 t/ha to 0.64 t/ha and NRMSE from 32% to 14%. Lower model accuracy was found where APSIM overestimates yield under high water deficit condition and when it underestimates yield under nitrogen limitation. APSIM severely over-predicts yield when some abiotic stresses such as heatwaves and frost affect the crop growth. SIGNIFICANCE: This paper uses APSIM-Wheat as an example to provide perspectives on crop model yield prediction performance under different conditions covering a wide spectrum of management practices, and environments. The findings deepen the understanding of model uncertainty associated with different calibration processes or under various stressed conditions. The results also indicate the need to improve the model's predictive skill by filling functional gaps in the wheat simulations and by assimilating external observations (e.g., biomass information estimated by remote sensing) to adjust the model simulation for stressed crops.