The optimal operation of high-voltage underground power cables is crucial for powering our communities, and it hinges on the intricate dynamics of insulation temperature around the conductor, primarily influenced by joule heating. This temperature responsiveness is further molded by seasonal and diurnal fluctuations in power demand, as well as the moisture content in the surrounding soil. Past research concentrated on theoretical analyses and experiments under dry conditions, but our study expands this scope. Through extensive laboratory tests exploring static and cyclic thermal loads in both dry and saturated sand environments, we uncovered valuable insights. Cyclic thermal loads in dry sand demonstrated a significant thermal charging effect, especially with shorter relaxation times. In static thermal loading, utilizing saturated sand enhanced heat dissipation due to higher thermal conductivity. However, it also revealed a noteworthy observation: a robust convection cell formed after three days of continuous heating, presenting challenges for cables under crop fields despite facilitating efficient cooling. Highlighting the importance of high-voltage power cable infrastructure, our study delves into the critical intersection between infrastructure and the underground soil. Understanding these interactions becomes imperative for the sustainable development of clean energy initiatives. As the world transitions to cleaner energy practices, optimizing the performance of underground power cable systems becomes pivotal in realizing their full potential and aligning with broader clean energy goals. This research contributes essential knowledge to enhance the safety, efficiency, and sustainability of high-voltage underground power cable systems in support of a cleaner and more sustainable energy future.

Power transmission covering long-distances has shifted from overhead high voltage cables to underground power cable systems due to numerous failures under severe weather conditions and electromagnetic pollution. The underground power cable systems are limited by the melting point of the insulator around the conductor, which depends on the surrounding soils’ heat transfer capacity or the thermal conductivity. In the past, numerical and theoretical studies have been conducted based on the mechanistic heat and mass transfer model. However, limited experimental evidence has been provided. Therefore, in this study, we performed a series of experiments for static and cyclic thermal loads with a cylindrical heater embedded in the sand. The results suggest thermal charging of the surrounding dry sand and natural convection within the wet sand. A comparison of heat transfer for dry, unsaturated and fully saturated sand is presented with graphs and colour maps which provide valuable information and insight of heat and mass transfer around an underground power cable. Furthermore, the measurements of thermal conductivity against density, moisture and temperature are presented showing positive nonlinear dependence.

The integration of Artificial Intelligence techniques, particularly Artificial Neural Networks (ANNs), has transformed predictive modeling in structural and durability engineering. This study investigates the use of ANN-based approaches to predict the corrosion rates of mild steel reinforcement embedded in cementitious composites subjected to clay-dominated soil environments. Key environmental parameters, sodium chloride (NaCl) content (0-4%), inhibitor dosage (DOI) (0-5%), and exposure duration (30-180 days), were selected as input variables. Two ANN architectures, Feedforward Backpropagation (FFBP) and Cascadeforward Backpropagation (CFBP), were developed and trained using 72 experimental data points extracted from the literature. The FFBP model outperformed CFBP in terms of predictive accuracy, achieving a correlation coefficient (R) of 0.998, a mean absolute percentage error (MAPE) of 30.43%, and a root mean square error (RMSE) of 0.071 during testing. Sensitivity analysis revealed that inhibitor dosage had the most significant influence on corrosion behavior, followed by NaCl concentration and exposure duration. The findings confirm that ANN models can effectively capture the nonlinear interactions governing corrosion progression, even under complex environmental conditions associated with clayey soils. This research provides a reliable and practical AI-driven framework for assessing corrosion risk, guiding material design, and enhancing long-term infrastructure durability in aggressive subsurface conditions. The study underscores the growing relevance of machine learning in simulating time-dependent deterioration processes in geotechnical and structural materials.

• Large-scale experimental dataset of soil temperature measurements around a cable-representative heat source under controlled thermal loading. • Time-resolved temperature data recorded at multiple radial and vertical locations for steady-state and cyclic heating regimes. • Dataset includes long-duration steady-state heating and symmetric and asymmetric cyclic heating–cooling conditions. • Publicly available dataset suitable for benchmarking, model validation, and data-driven analysis of soil thermal behavior.

Subsea power cables operate within thermally sensitive seabed environments, where heat dissipation into surrounding marine sediments governs allowable current loading and long-term system reliability. As offshore energy transmission expands, understanding the interaction between cable heat generation, sediment thermal properties, and environmental forcing has become increasingly important for accurate thermal assessment and resilient infrastructure design. This paper presents a critical review of thermal–geotechnical interactions in subsea cable systems, synthesizing literature across electrical cable engineering, marine geotechnics, and environmental modeling. Modeling approaches are organized into a taxonomy comprising deterministic thermal models, coupled thermo-hydraulic formulations, data-driven forecasting techniques, and emerging hybrid physics–data frameworks. The review identifies key limitations in current practice, including insufficient representation of seabed heterogeneity, limited treatment of transient environmental forcing, uncertainty in sediment thermal properties, and challenges in integrating monitoring data into predictive models. Building on these observations, a conceptual hybrid modeling framework is proposed as a future research direction, in which physics-based thermal solvers are complemented by data-driven approaches to capture time-dependent boundary conditions and support adaptive assessment. The findings provide a structured foundation for advancing integrated modeling strategies and developing next-generation thermal assessment frameworks that better reflect the dynamic geotechnical conditions governing subsea cable performance.