Existing research of extreme rainfall impact on transport networks primarily examines the effect of waterlogging. Although the other two main factors-reduced visibility and traffic-signal power outages-have been shown to significantly affect road operation, their contributions at the network scale remain underexplored. Taking a macroscopic approach, this study gauges the impacts of these three factors on the road network connectivity and efficiency during extreme rainfall through a case study of 26 Local Government Areas in and around Greater London. The result shows that focusing solely on waterlogging while disregarding reduced visibility and traffic signal power failures overestimates road capacities by 15-30% and underestimates network efficiency impacts by 1–23% under different rainfall scenarios. Particularly, the largest impact underestimation is observed for 1-in-30-year rainfall risk, where waterlogging is less dominant, while poor visibility considerably contributes to the impacts. The analysis also suggests that signal power failures during rainfall have limited, localised effects at the network level.

• Transparent, reproducible workflow to build overhead power grids from public data. • Map electricity end users by integrating Google Places API and OpenStreetMap. • Novel distance–capacity method links end users to substations. • Cascading failures modelled via power flow analysis, beyond topological methods. • Estimate service disruptions via post-disruption electricity-served ratio. Cascading failures in power grids can escalate localized disruptions into extensive service outages. This study presents a transparent, reproducible framework to build power grids from publicly available data, integrating commissioned power plants, substations, transmission lines, and hypothetical transformers, for large-scale power system modelling. To assess how grid disruptions lead to service interruptions for industrial users, we combine Google Places API industrial points of interest with OpenStreetMap industrial zones, and link them to substations based on capacity and proximity. We model initial disruptions under two scenarios: 1) random disruptions, implemented through percolation analysis to assess network robustness under stochastic component removals; and 2) targeted disruptions, generated by Monte Carlo sampling of wind-induced fragility curves derived from the 2024 Typhoon Yagi wind field to produce probabilistic failures. The analysis reveals distinct spatial variation in grid vulnerability and traces the propagation of failures through the network, quantifying downstream service disruptions for industrial users. System-wide simulations further identify critical transmission corridors with higher failure and overload risks. These insights provide a basis for targeted investment, resilience assessment, and future research on multi-hazard and adaptive protection strategies.

Data from psychophysiological measures can offer new insight into control room operators’ behaviour, cognition, and mental workload status. This is particularly helpful when assessing capacity to respond to critical plant conditions such as alarm response scenarios. However, wearable tools such as eye tracking and electroencephalography caps can be perceived as intrusive and unsuitable for daily operations. Therefore, this article examines the potential of using real-time data from process and operator-system interactions during abnormal scenarios, recorded and retrieved from the distributed control system’s historian or process log, to provide insight into operator behaviour and predict their response outcomes without intruding on daily tasks. Data for this study were obtained from a design of experiment using a formaldehyde production plant simulator and four human-in-the-loop support configurations. A comparison between configurations in terms of both behaviour and performance is presented. Then, a step-wise logistic regression and a Bayesian network model were used to predict operator error. The results identified predictive metrics, discussed in terms of their value as precursors of overall system performance in alarm response scenarios. Knowledge of relevant and predictive behavioural metrics accessible in real time can better equip decision-makers to predict outcomes and provide timely support measures for operators.