Post Graduate Thesis Award 2026
THESIS: Microstructure, Residual Stresses and Mechanical Properties of Laser Clad Rail Components
2026 Post Graduate Thesis Award
Winner
Dr. Olivia Kendall
Doctor of Philosophy
Place of Study: Monash University
Olivia’s research investigated the use of laser cladding as an innovative repair and life-extension technology for railway rail components that are subjected to wear, plastic deformation, and rolling contact fatigue. The study addressed the growing need for more sustainable and cost-effective maintenance solutions by evaluating the microstructure, residual stress distribution, and mechanical properties of laser-clad rail steels and light rail components. Through a comprehensive experimental program, including neutron diffraction measurements, the research assessed the performance of newly developed cladding alloys, post-processing treatments, and multilayer deposition strategies on different rail grades. The findings demonstrated that laser cladding can achieve favorable microstructures, enhanced mechanical properties, and significantly reduced residual stress levels while maintaining the structural integrity of the rail. The work provides important technical evidence supporting the safe implementation of laser cladding as a regenerative maintenance strategy for heavy haul, metro, and light rail networks, with the potential to improve asset reliability, extend rail service life, and reduce maintenance costs across the railway industry. As a result of this PhD study, five journal publications have been prepared.
Runner Up
Dr. Sheng Zhang
THESIS: Development of Data-Driven Methods for Railway In-Train Force Monitoring and Damage-Tolerant Maintenance for Railway Couplings
Doctor of Philosophy
Place of Study: Monash University
Sheng’s PhD research addresses a critical challenge facing Australia’s heavy-haul railway industry: the reliable assessment of railway coupling integrity under increasing train lengths and payloads. The thesis developed two complementary innovations to support a future digital twin for railway couplings. First, a novel machine-learning framework, In-trainNet, was created to enable scalable, fleet-wide estimation of in-train forces using Automatic Train Operation data, eliminating the need for extensive physical instrumentation. Second, an automated fatigue life prediction methodology was developed using experimental material characterization, fracture mechanics, and advanced XFEM crack-growth modelling to predict the remaining life of couplings containing defects. Together, these advances provide a practical pathway towards predictive, condition-based maintenance, improving safety, reliability, asset utilization, and lifecycle cost management across heavy-haul railway operations. As a result of this PhD study, two journal publications and one conference paper have been prepared.
Runner Up
Dr. Daniel Roi Agustin
THESIS: Track Buckling Predictions Using Long Track Dynamics Models and Machine Learning
Doctor of Philosophy
Place of Study: Central Queensland University
Daniel’s PhD research addresses the critical challenge of railway track buckling in continuous welded rail systems, a major safety and operational risk for rail networks worldwide. The thesis developed a novel computational framework capable of simulating track dynamics and buckling behavior over realistic kilometre-scale track lengths under the combined influence of thermal stress and dynamic train loading. By integrating advanced finite element modelling, parallel computing, probabilistic risk assessment, and machine learning techniques, the research enables more realistic prediction of track stability while accounting for uncertainties in key parameters such as rail temperature, lateral resistance, track geometry, and ballast conditions. The resulting hybrid physics-based and data-driven framework provides railway operators with a powerful tool for buckling risk assessment, maintenance planning, and infrastructure management, contributing to safer, more resilient, and more efficient railway operations. As a result of this PhD study, nine journal and conference publications have been prepared.
Master’s Thesis Award
Sakura Mukhopadhyay
THESIS: Accurate Distance and Battery Capacity Estimation for Battery-Powered Electric Locomotives
Place of Study: Macquarie University
Sakura’s Masters research addresses a key challenge in the transition towards low-emission railway transportation: accurately assessing the performance of battery-powered electric locomotives operating on real-world railway corridors. The thesis developed an energy-balance-based methodology and supporting algorithms to predict both the maximum distance a battery-powered train can travel, and the battery capacity required for a given route. By incorporating the effects of track geometry, gradients, speed restrictions, stations, tunnels, and other operational constraints, the research moves beyond simplified assumptions commonly used in battery train studies. A comprehensive MATLAB-based dynamic simulation model was also developed and validated against real railway data, providing designers and operators with practical tools to optimize battery-electric railway systems and support future decarbonization of rail transport.