Infrastructure Engineering - Research Publications

Permanent URI for this collection

Search Results

Now showing 1 - 10 of 423
  • Item
    Thumbnail Image
    Supply chain risk management for projects: A review, a taxonomy, a framework, and a research agenda
    Baghalzadeh Shishehgarkhaneh, M ; Moehler, R ; Fang, Y ; Hijazi, A ; Aboutorab, H (University of Bath, 2024)
    This study investigates the critical role of supply chain risk management (SCRM) as a factor for growth and competitive advantage in project management. While there is extensive literature on risk management, there is a notable gap in the development of a comprehensive risk taxonomy tailored to project management supply chains. Through a systematic literature review of 50 scholarly articles, this paper categorizes and identifies prevalent supply chain risks and their potential impacts, which influence the entirety of project supply chains. It introduces a novel SCRM taxonomy, elucidating its significance in the context of project management. Additionally, the research proposes a future research agenda aimed at supporting the theoretical foundations of SCRM, thereby facilitating its formalization and strategic refinement. This endeavor enhances our understanding of SCRM’s key role in project management, providing a structure for future research and application across diverse industries, beyond the traditional focus on construction.
  • Item
    Thumbnail Image
    The affordance of boundary objects for codified project learning sharing between communities of practice: Pilot study results and learnings for selected sustainability demonstration projects
    Ferres, G ; Moehler, R ; Sharp, D (University of Bath, 2024)
    Effective project-to-project learning can prevent projects from repeating the same mistakes however externalised knowledge sharing is necessary to overcome temporal, geographical and organisational barriers. Externalised sharing for this purpose requires the codification of knowledge relating to project learnings within boundary objects, where codification may be impacted by an array of complex considerations. Among these considerations is whether the capacity of the boundary object affords boundary spanning between communities of practice, where boundary-spanning capacity is influenced by the characteristics codified within the object. The grand-challenge context of sustainability demonstration projects provides an important case context for boundary spanning as these projects have knowledge sharing and learning as a central focus, key driver and intended outcome. While the application of boundary objects has been explored in a wide range of domains and cases, this article specifically considers the characteristics of boundary objects representing codified project learnings to afford project-to-project knowledge sharing, a focus which has not yet been studied in either the sustainability demonstration context or any other project-to-future learning organisation case context. An initial pilot study has been conducted with four sustainability demonstration case projects, with results and learnings to guide the refinement of future large-scale research design.
  • Item
    No Preview Available
  • Item
    Thumbnail Image
    Current Operational Battery Energy Storage Systems in Australia and Their Intended Project Objectives on Grid Operational Issues: A Literature Review
    Hui, KP ; Yap, L (IEOM Society International, 2024-02-12)
    As energy companies look to diversify their portfolio in renewable energy, the demand for electrification will continue to increase. There will be increasing demands on the electrical grid infrastructure. Distributed energy resources (DER) such as solar photovoltaic (PV) on rooftops and electric vehicles will experience a host of operational issues such as hosting capacities, overloads, reverse flow, phase balance, frequency drift and voltage variation. Battery energy storage systems can help mitigate some of these problems. In this paper, the literature and public available information on operational battery storage systems in Australia are reviewed and discussed. It is found that both small batteries and large batteries both fundamentally address grid operational issues. As Australia moves towards high DER penetration and high renewable energy generation, there will be a need for more battery energy storage systems to offset operational issues. The lack of private funding especially for smaller batteries may possibly cause PV DER to lag the overall demand for electrification.
  • Item
    Thumbnail Image
    Lightweight traffic anomaly detection: A case study with SCATS volume data of Melbourne
    Taheri Sarteshnizi, I ; Sarvi, M ; Asadi Bagloee, S ; Nassir, N ; Aye, Z (Australasian Transport Research Forum, 2023)
    In this paper, we evaluate performance of an anomaly detection framework with real traffic count data collected by SCATS (Sydney Coordinated Adaptive Traffic System) loop detectors in Melbourne. The goal is to detect anomalous daily volume profiles within temporally large historical traffic data utilizing a lightweight and parameter-free approach and use it for live applications. To achieve this, daily volume profiles are first compressed into two dimensions benefiting from the Principal Component analysis (PCA). Then, a parameter-free version of DBSCAN is applied to the data with unique days of the week. Results from more than 20 different locations in Melbourne are fully visualized and the advantages and disadvantages of the method are discussed. We found that, with this approach, anomalous volume profiles can be accurately detected in a wide range of spatiotemporal data without any pre-training, parameter setting, or using complex learning methods.
  • Item
    Thumbnail Image
    Testing request prioritization strategies to improve the quality of a shared autonomous vehicles service: A Melbourne case study
    Tiwari, S ; Nassir, N ; Sauri Lavieri, P (Australasian Transport Research Forum, 2023)
    Shared autonomous vehicles (SAVs) have the potential to revolutionize urban transport by offering a mobility service that combines the benefits of autonomous vehicles and ride-hailing systems. However, one of the main limitations of these services is the handling of all request types in a singular way, which can increase biases towards a fixed type of passengers who have different properties and accessibility-based constraints. This study proposes a prioritization approach based on different request properties and the urgency of the request. The study uses the MATSim simulator to evaluate the prioritization schemes and implements five different scenarios to assess the collective and individual effects of the schemes. The case study focuses on Melbourne Metropolitan Area, and the prioritization is done based on the existing public transport (PT) service and pre-calculated SAV demand of the network. The study considers different performance measures, such as service efficiency, externalities, and provision equity, to estimate the benefits of the prioritization. The results indicate that prioritization can improve overall equity by spreading wait times evenly across the network. The prioritization approach improves the service with more riders finding the service attractive, resulting in more served rides with lower average vehicle kilometers traveled per ride. In addition, the study shows that the PT mode share is increased in multiple scenarios, demonstrating the positive effect of considering accessibility when prioritizing requests.
  • Item
    Thumbnail Image
    What influences passenger’s arrival rate at stops in Melbourne: WASEA-LSTM: a novel deep learning model leveraging multi-source data fusion
    Rezazada, M ; Nassir, N ; Tanin, E (ATRF, 2023)
    Public transportation demand plays a crucial role in service planning and operation. Accurate prediction of passenger arrival rates at transit stops allows transportation planners and operators to optimize resources and improve service efficiency. Current methodologies primarily focus on weather's impact in the aviation industry, supply dynamics, and arrival time prediction, while overlooking its influence on public transport demand variation. This study addresses these gaps by designing a deep neural network model that can predict public transit demand, using large-scale datasets from multiple sources in Melbourne, Australia. We propose a novel deep learning architecture called Wasea-Lstm (Weather-Aware Smart Exponential Activation LSTM) that captures spatial, temporal, and external correlations for passenger arrival rate prediction at tram stops. The model is trained and tested on integrated datasets from automatic fare collection (AFC), automatic passenger count (APC), and weather data over a period of three months. Results show that the Wasea-Lstm model significantly outperforms benchmark models, including gradient boosting machine (GBMR) and multi-layer perceptron (MLP) regression by 15% and 6% in R2 metric, respectively. The feature importance ranking reveals that stop location, time of the day, temperature, and humidity are the key influencers of passenger arrival behaviour in Melbourne. Overall, this study contributes to the development of a model that accounts for multi-dimensional, high-resolution determinants of passenger demand using large-scale datasets from real world. The proposed Wasea-Lstm architecture shows exceptional performance in precisely forecasting stop-level demand for one of Melbourne's largest tram routes. Moreover, its applicability extends seamlessly to all routes within the network.
  • Item
    No Preview Available
    Monte Carlo and Subset Simulations-Based Reliability Analysis of Composite Frames Using OpenSees
    Tran, H ; Thai, HT ; Uy, B (Springer Nature Switzerland, 2023-01-01)
  • Item
    No Preview Available
    Numerical Study of a Novel Self-lock Connection for Modular Tall Buildings
    Thai, HT (Springer Nature Singapore, 2023-01-01)
  • Item
    No Preview Available
    ADVANCED ANALYSIS OF STEEL‐CONCRETE COMPOSITE BUILDINGS
    Tran, H ; Thai, HT ; Ngo, T ; Uy, B ; Li, D ; Mo, J (Wiley, 2023-02)
    Abstract This paper presents a nonlinear simulation method for composite framing systems constituted from concrete‐filled steel tubular columns (CFST) and composite beam systems. A force‐based fibre beam‐column element in OpenSees was adopted. This element was capable of accurately capturing the local buckling of steel and the confining effect of concrete using the modified stress‐strain relationships of the steel and concrete fibres. A source code for the connection element was also developed in OpenSees to capture the semi‐rigid behaviour of the beam‐to‐column connections of the composite buildings. Through the verification with numerous experiments, the model has shown its capability of accurately simulating composite frames with simplicity and less computational cost. An extensive parametric study was conducted to examine the effect of the bracing systems and the rigidity of the connections on the behaviour and instability of the whole composite buildings.