This study examines how railway drainage performance can be better predicted to reduce flooding and infrastructure failure risks, introducing a risk-based approach that classifies assets by the likelihood of deterioration between inspections.
This case study examines how extreme weather, a 2023 heatwave followed by intense rainfall, affected the Great North Run in North East England, the world's largest half-marathon. Combining near-time weather and transport data with participant survey insights, it analyses impacts on return travel, travel options, and delays, offering findings to help authorities and organisers strengthen the resilience of future mass participation events.
This paper examines how climate-driven faults in urban distribution networks interact with rising EV charging demand, a gap in research that has largely focused on renewable integration and smart grid solutions separately. Using power flow analysis in a Glasgow City case study, it finds that climate-related faults could significantly affect grid supply points, while voltage levels in the urban network remain within safe limits.
This paper develops a Levelised Cost of Hydrogen (LCOH) framework to compare five wind-electrolyser configurations for green hydrogen production in the UK. It finds electricity cost is the dominant driver of LCOH, followed by electrolyser cost, and highlights how location, market arrangements, and policy support shape the economic feasibility of green hydrogen deployment.
This paper uses smart EV charging to reduce renewable energy curtailment on strained rural distribution networks. Using a customised AC Optimal Power Flow model with k-means clustering, it targets EV users whose existing habits naturally support grid flexibility.
This paper investigates how clay embankments, used in transportation and flood defense infrastructure, gradually deteriorate due to weather exposure, eventually risking failure. Using multi-phase numerical modeling across well-documented failure case studies, the researchers simulated long-term hydromechanical behavior to predict time to failure.
This paper addresses transport decarbonisation by simulating policies designed to shift travel behavior away from private cars toward active modes like cycling and walking. Using MATSim, an agent-based transport simulation framework, the study models how local and national government interventions could drive this modal shift, offering evidence to support effective policy design for reducing transport emissions.
This systematic review examines how Generative AI and Large Language Models are being applied to renewable energy and smart grid systems, driven by the need to manage increasingly decentralized, data-heavy energy infrastructure as the world moves toward decarbonization and digitalization.
This work demonstrates how agent-based models can simulate transport disruptions by capturing highly detailed, individual-level travel behaviours, enabling faster and more accurate forecasting to support improved emergency planning, infrastructure resilience, and transit management decisions.
This paper introduces PaveGNet, a spatiotemporal graph network that models fine‑grained pavement distress evolution by capturing spatial relationships, temporal dynamics, and external factors such as climate, traffic, and maintenance, providing a scalable, realistic approach for pavement assessment.
In this paper, the authors compare travel behavior of 1,555 EV drivers and 1,363 corresponding EV households to their ICEV counterparts.
This work uses a fully Bayesian Gaussian process emulator to efficiently predict the time‑dependent factor of safety of geotechnical slopes, enabling faster modelling of deterioration to support improved design, maintenance, and asset management decisions.
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