Heat transfer from/to energy piles can cause plastic contraction of soft clay with a low overconsolidation ratio (OCR) during heating and cooling. This can lead to thermally induced excess pore water pressure and changes in the lateral stress acting on a pile. These thermally induced phenomena cause additional plastic settlement of the soil and pile. Irreversible settlement may cause serviceability or ultimate limit state problems for floating energy piles whose capacity is mainly derived from shaft resistance. Due to intended operational needs or by accident, cyclic nonsymmetrical thermal loads may be applied to floating energy pile groups and piled rafts. The thermomechanical interaction among nonsymmetrical loaded piles is not well understood. In this study, a series of cyclic nonsymmetrical thermally loaded centrifuge model tests were conducted on floating pile groups and piled rafts in soft kaolin clay with an OCR of 1.7 at the pile toe. The performance of floating $2\times 2$ elevated energy pile groups and energy piled rafts was investigated. Three piles in each group were subjected to 15 cycles of $\pm 14°\mathrm{C}$ in temperature change. The thermally induced irreversible settlement and tilting of the elevated pile groups exceeded serviceability and ultimate criteria. The piled rafts similarly settled but to a smaller extent than the elevated group. Their tilting, however, did not exceed the serviceability criterion. It is recommended that energy piled rafts be used in soft clay instead of elevated energy piles.

Landslides are a common and challenging geohazard that may be caused by earthquakes, rainfall, or manmade activity. Various monitoring strategies are used in order to safeguard populations at risk from landslides. This task frequently depends on the utilization of remote sensing methods, which include the observation of Earth from space, laser scanning, and ground-based interferometry. In recent years, there have been notable advancements in technologies utilized for monitoring landslides. The literature lacks a comprehensive study of subsurface monitoring systems using a mixed review approach that combines systematic and scientometric methods. In this study, scientometric and systematic analysis was used to perform a mixed review. An in-depth analysis of existing research on landslide-monitoring techniques was conducted. Surface-monitoring methods for large-scale landslides are given first. Next, local-scale landslide subsurface monitoring methods (movement, forces and stresses, water, temperature, and warning signs) were examined. Next, data-gathering techniques are shown. Finally, the physical modeling and prototype field systems are highlighted. Consequently, key findings about landslide monitoring are reviewed. While the monitoring technique selection is mainly controlled by the initial conditions of the case study, the superior monitoring technique is determined by the measurement accuracy, spatiotemporal resolution, measuring range, cost, durability, and applicability for field deployment. Finally, research suggestions are proposed, where developing a superior distributed subsurface monitoring system for wide-area monitoring is still challenging. Interpolating the complex nonlinear relationship between subsurface monitoring readings is a clear gap to overcome. Warning sign systems are still under development.

Landslides are frequent hillslope events that may present significant risks to humans and infrastructure. Researchers have made ongoing efforts to assess the potential danger associated with landslides, intending to ascertain the location, frequency, and magnitude of these events in a given area. This study is meant to supplement the previous study (Part I), which explored empirical and physically based causative thresholds. In this paper (Part II), a systematic review is used to conduct an in-depth study of existing research on prediction models. Deterministic physical approaches were investigated for local-scale landslides. Next, national-scale landslide susceptibility models are discussed, including qualitative and quantitative models. Consequently, key findings about rainfall-induced landslides are reviewed. The strategy selection is generally governed by data and input factors from a macroscopic perspective, while the better prediction model is defined by dataset quality and analysis model performance from a microscopic perspective. Physically based causative thresholds can be used with limited geotechnical or hydrological data; otherwise, numerical analysis provides optimal accuracy. Among all statistical models, the hybrid artificial intelligence model achieved the best accuracy. Finally, current challenges have concentrated on integrating AI and physical models to obtain high accuracy with little data, prompting research suggestions. Advanced constitutive models for real-time situations are lacking. Dynamic and spatiotemporal susceptibility maps are also used, although their subjectivity needs further research. This study analyses how to choose the best model and determine its key traits. This research provides valuable insights for scholars and practitioners seeking innovative approaches to lessen the severity of landslides.

Although various researchers have reported that scaling effects should be considered carefully when modelling the mechanical behaviour of pile in sand by a geotechnical centrifuge, scaling effects on the coupled thermo-mechanical behaviour of the energy pile have not been reported. In this study, the scaling effects on the centrifuge modelling of floating energy piles are assessed by the so-called ‘modelling of models’ technique. Two centrifuge models of energy piles were tested at the 28 and 56 g-levels to simulate the same prototype in saturated medium dense sand. The ratio of pile diameter to mean sand particle size (D/d₅₀) ranged from 92 to 184. Identical working load was applied to the two piles with a factor of safety of 2·5 and each pile was subjected to seven heating–cooling cycles. After the tests, measured thermally induced settlements of both energy piles were similar (1·3%D). Furthermore, a good agreement was obtained between the two piles in the distribution of the axial load along each energy pile. It is evident that the scaling effects due to particle size on the centrifuge modelling of floating energy piles with a ratio of D/d₅₀ larger than 92 can be ignored.

Introduction and Problem Statement: Landslides represent a significant geological hazard worldwide. One of the primary triggers for these landslides is rainfall, which is becoming more intense as a result of climate change. The available literature has produced extensive research. However, this largely overlooks the use of mixed methodologies. Furthermore, a comprehensive review combining empirical, physically based, deterministic, and phenomenological models is still rare. Objective and Method: This study (Part I of a two-part review) addresses this gap by employing a mixed review that integrates quantitative scientometric analysis with a qualitative systematic review. The primary objective of Part I is to deliver a critical assessment, focusing on empirical and physically based causative threshold models. Main Results and Validation: Macroscopically, our analysis reveals that antecedent rainfall is a more robust indicator than classical intensity–duration (I-D) thresholds, though the latter remains widely used due to its simplicity. Physically based models provide a critical bridge when geotechnical data is scarce, correlating rainfall with internal slope responses like displacement. At a microscopic level, hybrid artificial intelligence (AI) models consistently demonstrate superior predictive accuracy by capturing complex, nonlinear relationships missed by simpler models. These findings are validated through a systematic evaluation of performance metrics across the reviewed literature. Main Conclusions and Significance: We conclude that while empirical thresholds offer operational simplicity, the future of accurate prediction lies in sophisticated hybrid AI models trained on extensive monitoring data. This review synthesizes fragmented knowledge into a unified framework, providing a clear roadmap for model selection.