TerraMosaic Daily Digest: Jan 15, 2026
Daily Summary
Today's literature review reveals a strong focus on improving hazard assessment, monitoring, and mitigation strategies for landslides, floods, and related geotechnical risks. A significant portion of the research leverages remote sensing and machine learning techniques for enhanced detection, prediction, and risk mapping. Studies address diverse geographical contexts, from the Three Gorges Reservoir Area in China to the Italian Dolomites and the Greenland Ice Sheet.
Key themes include the impact of climate change on hazard frequency and intensity, the development of early warning systems, and the application of advanced numerical modeling techniques to understand complex soil and rock behavior. Several papers explore the use of AI and machine learning for tasks ranging from landslide susceptibility mapping and flood loss estimation to optimizing sensor placement in structural health monitoring and predicting cutterhead wear in tunnel boring machines. The integration of multi-source data, including remote sensing imagery, meteorological data, and geotechnical parameters, is a recurring trend across various applications. Finally, several studies focus on the seismic performance of underground structures and infrastructure, highlighting the importance of considering soil-structure interaction and spatial variability in seismic design.
Key Trends
- AI/ML for Hazard Assessment: Machine learning is increasingly used for landslide susceptibility mapping, flood loss estimation, and predicting soil behavior.
- Remote Sensing Integration: Satellite data (SAR, optical) is being combined with LiDAR and other datasets for improved hazard detection, monitoring, and mapping.
- Climate Change Impacts: Several studies investigate the influence of climate change on landslide triggering, snowmelt hydrology, and extreme precipitation events.
- Focus on Underground Infrastructure: A significant number of papers address the seismic performance, stability, and construction challenges related to tunnels, subways, and other underground structures.
- Multiphysics Modeling: Numerical models are becoming more sophisticated, incorporating coupled hydro-thermo-mechanical processes to simulate complex geotechnical phenomena.
Selected Papers
This digest features 147 selected papers from 2,172 papers analyzed across multiple journals. Each paper has been evaluated for its relevance to landslide research and includes links to the original publications.
1. Dry–wet cycle may trigger the catastrophic landslide in Junlian on February 8, 2025
Core Problem: Catastrophic landslides in mountainous regions with seasonal climatic variability due to progressive weakening of rock mass strength under long-term dry–wet cycles.
Key Innovation: Quantifying the weakening of silty mudstone under dry–wet cycles through laboratory tests and field investigations, highlighting the role of groundwater accumulation and sustained precipitation in triggering landslides.
2. Applying the iRIC Morpho2DH model to determine the effectiveness of check dams against debris flows
Core Problem: Assessing the necessity and effectiveness of check dam mitigation structures against debris flows of varying magnitudes to mitigate risk without exacerbating downstream impacts.
Key Innovation: Using the iRIC Morpho2DH two-dimensional numerical model to reconstruct debris flow events and evaluate the impact of check dams, demonstrating the model's robustness for evidence-based planning and risk-informed engineering design.
3. Roles of different controls influencing the intensity–duration rainfall thresholds for triggering landslides: an intercomparison of published thresholds
Core Problem: Understanding the influence of various site-specific conditions, determination methodologies, and targeted scales on rainfall thresholds for triggering landslides to improve forecasting accuracy.
Key Innovation: Intercomparing published rainfall thresholds to determine the effects of controlling factors, revealing that data quality and quantity are crucial in determining reliable rainfall thresholds for shallow landslides and debris flows.
4. Rainfall threshold analysis for various geological formations in Northeastern Guangdong, China: a physically-based approach
Core Problem: Evaluating rainfall intensity and duration thresholds for landslide prediction in Guangdong's northern region, focusing on areas with historical high-intensity rainfall and landslides.
Key Innovation: Using the physically-based model TRIGRS to assess slope stability and rainfall thresholds, highlighting the geological role in slope failure and aiding in future rainfall-based threshold evaluations for early landslide warnings.
5. Landslide assessment research in the three gorges reservoir area: A review of methodological advances and future directions
Core Problem: Systematic review of recent advances in landslide assessment within the Three Gorges Reservoir Area (TGRA), outlining the technological evolution and limitations of various methods.
Key Innovation: Proposing a four-tier theoretical framework encompassing stability–susceptibility–hazard–risk, elucidating the technical linkages and integration pathways for multi-scale assessments, and focusing on dynamic monitoring, mechanism-informed modeling, and climate adaptation for future research directions.
6. Slurry pressure transmission in saturated sand during shield tunneling: expanding from infiltration tests to realistic scale simulation
Core Problem: Understanding slurry pressure transmission in saturated sand during shield tunneling is crucial for optimizing tunneling operations and preventing ground deformation or instability.
Key Innovation: Combines laboratory infiltration tests with 3D numerical simulations to provide quantitative insights into slurry pressure transmission, sand concentration, and cutterhead operation intervals, offering practical guidance for optimizing slurry parameters and tunneling operations in saturated sand layers.
7. Micro–macro investigation on the permeability reduction in surrounding rock of sandstone-type uranium deposit using microbially induced calcium carbonate precipitation technology
Core Problem: Preventing leachate migration and groundwater contamination during in situ leaching (ISL) of sandstone-type uranium deposits (STUDs) where natural aquicludes are absent.
Key Innovation: Employs microbially induced calcium carbonate precipitation (MICP) technology to construct an artificial aquiclude by reducing the permeability of surrounding sandstone, providing both macroscale effectiveness and microstructural insight.
8. Influence of Fault Geometry on the Development Height of Overlying Rock Fissures in Phosphate Ore Bodies
Core Problem: Faults significantly increase the vertical displacement of overlying rock layers and pillars in the mining area, increasing the ORF height.
Key Innovation: This study reveals the quantitative relationship between fault parameters (dip and drop) and ORF height, which offers a theoretical foundation for the forecasting, prevention, and control of fissure zones for the safe mining of fault-bearing phosphate mines.
9. Effect of spatial variability of soil properties on liquefaction behaviour – a probabilistic approach
Core Problem: Assessing soil liquefaction hazard by modeling subsurface spatial variability and seismic hazard.
Key Innovation: High-resolution, site-specific probabilistic framework using Gaussian Random Field modeling and Monte Carlo simulation to generate realizations of cone tip resistance and sleeve friction.
10. Probabilistic seismic liquefaction triggering assessment of gravelly soils
Core Problem: Developing liquefaction triggering models for gravelly soils, which are often overlooked in liquefaction assessments.
Key Innovation: Compiled a database of 215 gravelly case histories and developed probability-based liquefaction triggering predictive models incorporating adjustments for earthquake duration, vertical effective stress, and median grain size.
11. Elastodynamic imaging of voids in a PML-truncated layered solid using a deep convolutional neural network
Core Problem: Detecting subsurface voids that pose risks to infrastructure stability.
Key Innovation: A deep convolutional neural network (DCNN) framework for elastodynamic imaging of voids using surface responses from non-scanning type probing, trained with data generated by a level-set wave solver.
12. Forecasting CO2 injection-induced fault reactivation: A hybrid approach and its application to the Illinois Basin–Decatur Project
Core Problem: Assessing the risks of injection-induced fault reactivation during geological CO2 storage (GCS).
Key Innovation: An integrated assessment combining physics-based modeling and probabilistic forecasting to evaluate fault reactivation risks, analyzing fault slip tendency indices, Coulomb failure stress, and seismogenic index.
13. Seismic site characterization using satellite-derived terrain morphometry and geological data: A machine learning approach for predominant frequency prediction
Core Problem: Characterizing predominant frequency (fo) across large seismically active regions with limited field measurements.
Key Innovation: A DEM-based machine learning methodology for regional-scale fo prediction using stacked ensemble models trained on terrain morphometric parameters, geological classifications, and bedrock depth information.
14. Fracture evolution and differential mechanical response of surrounding rock in deep tunnel excavation: A case study under complex geological conditions
Core Problem: Investigating fracture evolution mechanisms of surrounding rock in deep tunnels under complex geological conditions to control large deformations.
Key Innovation: An innovative “ahead-lag complementary monitoring network” integrating borehole television, multipoint extensometers, and hollow inclusion strain gauges to capture fracture responses in different rock types, leading to differentiated support strategies.
15. Probabilistic characterization of 3D geotechnical variability by fusion of multi-fidelity measurements using Gaussian Process Regression
Core Problem: Characterizing three-dimensional (3D) subsurface geotechnical properties with sparse high-fidelity measurements.
Key Innovation: A Gaussian Process Regression (GPR)–based framework to fuse information from multi-fidelity measurements, training separate Gaussian process models on each low-fidelity dataset and using their combined predictions to approximate high-fidelity values.
16. Nano-SiO₂ modified microbial-induced carbonate precipitation: Application in altered rock fracture zone reinforcement and strengthening mechanism
Core Problem: Reinforcing altered rock fracture zones using microbial-induced carbonate precipitation (MICP).
Key Innovation: Studying the strengthening effect of nano-SiO2 (NS) on MICP for reinforcing altered rock fracture zones, showing that NS enhances mechanical strength and compensates for the weakening effect of MICP on cohesion.
17. Creep behavior of rocks under coupled high stress and water pressure
Core Problem: Understanding the time-dependent deformation and failure mechanisms of rock under coupled high stress and water pressure.
Key Innovation: Investigating the seepage–creep behavior of sandstone, marble, and granite under different hydraulic boundary conditions, revealing a U-shaped creep failure trend with increasing water pressure and a dual strengthening effect under saturated conditions.
18. Water-sand mixture inrush in underground pathways: Risk factors and mitigation strategies
Core Problem: Water-sand mixture inrush (WSMI) events pose severe threats to mining safety, infrastructure stability, and subsurface operations.
Key Innovation: This study first develops a pathway loss model to integrate frictional and expansion-induced hydraulic head losses, and then applies the Sobol-based global sensitivity analysis (GSA) to the model to evaluate WSMI risk for the following two scenarios: (1) direct pathway-induced WSMI (with short, gravity-driven pathways) and (2) indirect or combined pathway-induced WSMI (with long, complex, pressure-driven pathways).
19. Late-Quaternary activity of parallel normal faults along the southern margin of the Yuguang Basin in the Shanxi Rift, China and its seismogeological implications
Core Problem: Understanding the co-seismic slip history and seismogenic potential of the Yu-Guang Basin southern marginal fault (YBSM Fault) in the Shanxi Rift, China.
Key Innovation: Combined t-LiDAR analysis of bedrock fault surfaces with trenching of sedimentary faults to evaluate fault activity and paleoearthquake history, revealing two seismic events on the bedrock fault and a Holocene fault in sediments.
20. An intense peak of paraglacial dismantlement of mountain slopes: Insights from dating and volume quantification of rock-slope failure deposits in the Icelandic Westfjords (Dýrafjörður and Önundarfjörður)
Core Problem: Documenting the magnitude, duration, and geomorphic impact of Early and Middle Holocene paraglacial denudation through the study of rock slope failures (RSFs) in the Westfjords of Iceland.
Key Innovation: Using Schmidt-hammer exposure-age dating calibrated with radiocarbon dating to determine the timing and volume of RSFs, revealing a peak in activity between 12 and 6 cal. ka BP and a significant lag between deglaciation and peak RSF activity.
21. Participatory engagement and lived experiences of early warning and preparedness in two rural communities in Kerala, South India
Core Problem: Early Warning Systems (EWS) often fail to reach rural populations or prompt preparedness.
Key Innovation: Co-production through Participatory Rural Appraisal (PRA) techniques can strengthen people-centered EWS at the community level.
22. Citizen participation in flood risk management: Usability Insights from the RiverCure Portal
Core Problem: Extreme weather events have become increasingly frequent and severe, with rain-induced floods and inundations remaining the deadliest form of natural disaster.
Key Innovation: A web-based platform with geographic information system capabilities, that integrates the definition of geographic contexts with sensor data and hydrodynamic modeling tools, enabling decision-makers and researchers to assess and respond to flood risks.
23. Barriers to effective flood risk management in India: A case of 2021 Chiplun flooding
Core Problem: Selective engagement or silence regarding different dimensions of risk makes effective flood risk management in India challenging.
Key Innovation: Integrating the Narrative Policy Framework (NPF) with the Pressure and Release Framework (PAR) to examine the narratives surrounding flood risk.
24. Navigating risk and resilience: Exploring cultural, local responses, livelihoods, and institutions to Mount Merapi's volcanic hazards
Core Problem: Mount Merapi poses complex risks while fostering unique opportunities for surrounding communities.
Key Innovation: Integration of local capacities with modern tools—such as real-time monitoring and participatory evacuation planning—amplifies their effectiveness.
25. Communicating safety: The impact of warning signs and messages on reducing risky driving in flood conditions
Core Problem: Vehicle-related flood fatalities are common, and past research has largely focused on psychological, experiential, and sociodemographic factors that contribute to driver safety behavior. Less is known about how flood-related road signs and messages are understood and acted on.
Key Innovation: Findings show that past exposure to two different flood-safety signs commonly found in the United States encourage safer driving.
26. Impact of urban gray infrastructure on urban flooding: a city-scale drainage and surface water modeling framework
Core Problem: Addressing urban floods is an escalating concern owing to the rising incidence of heavy precipitation events and the intricacies of urban drainage and hydrodynamics.
Key Innovation: An advanced city-scale urban flood modeling framework that dynamically integrates the role of gray infrastructure (e.g., roads, buildings, and drainage systems) and real-time storage dynamics in urban drainage networks.
27. The Effect of Evacuation Decisions on Flash Flood Preparedness in Fujairah, UAE: When the Waters Rise Are We Ready in Desert Country?
Core Problem: There has been very little research about the increasing vulnerabilities of the population to flash flooding or about community preparedness and response to this hazard in UAE.
Key Innovation: A questionnaire based on the Protective Action Decision Model (PADM) was administered to 223 residents in the Emirate of Fujairah to identify predictors of flash flood evacuation.
28. A Stochastic Rain-on-Grid Framework for Handling Spatio-Temporal Rainfall Uncertainty in Impact-based Flood Nowcasting
Core Problem: Predicting flash flood impacts remains a major challenge due to intrinsic uncertainty in rainfall spatial-temporal structure and limited understanding of how rainfall organization propagates through hydrological and hydrodynamic processes to generate urban-scale impacts.
Key Innovation: A Stochastic Rain-on-Grid framework that explicitly accounts for rainfall uncertainty by coupling a high-resolution stochastic rainfall generator with a 2D hydrodynamic model operating at the watershed scale.
29. Harnessing social sensing for real-time flood event reconstruction: A digital autopsy of the 2024 Valencia DANA
Core Problem: Extreme rainfall and flash-flood events in the Western Mediterranean pose persistent challenges for real-time monitoring and emergency response.
Key Innovation: Reconstructs the impacts of the October 2024 catastrophic floods in Valencia using more than 156,000 geolocated messages from X (formerly Twitter) as a form of social sensing.
30. Neighborhood-scale assessment of urban flood impacts on transportation network resilience: A case study of Mavişehir, İzmir
Core Problem: Despite extensive research on the impacts of flooding on urban transportation, few studies have systematically assessed neighborhood-scale flood resilience.
Key Innovation: Using GIS-based network modeling and quasi-real-time traffic data, the analysis measures accessibility disruptions, increased travel times, and the network's capacity to support emergency operations during inundation.
31. Waterlogging susceptibility assessment in developed urban area using explainable machine learning methods with different negative sampling strategies
Core Problem: Rainstorm-induced waterlogging has become a frequent hazard threatening sustainable development, causing disruptions from traffic paralysis to casualties.
Key Innovation: A catchment unit-based framework based on morphological characteristics and proposed three data-driven negative sample sampling strategies to train explainable ML models to assess waterlogging susceptibility.
32. Quantifying uncertainty in tropical cyclone risk under present and future climates: Implication for disaster risk management in the Philippines
Core Problem: Tropical cyclones (TCs) pose a growing risk to coastal communities in Southeast Asia due to climate change and rapid urbanisation.
Key Innovation: We employ a probabilistic TC wind risk model that integrates high-resolution exposure data and sector-specific vulnerability functions to quantify uncertainty in TC risk under present and future climate conditions.
33. Nuisance flood risk: Defining a new horizon in urban flood risk management through hydrodynamic flood hazard modelling and indicator-based vulnerability assessment
Core Problem: Nuisance Flooding (NF), a form of low-depth, low-velocity inundation that disproportionately affects densely populated megacities, remains largely underrepresented in existing literature.
Key Innovation: A flood risk assessment framework explicitly tailored to NF conditions. A sophisticated 1D–2D coupled hydrodynamic model was employed to simulate high-resolution flood hazards, while vulnerability to critical and infrastructure facilities was assessed using a Shannon Entropy-cum-TOPSIS framework.
34. Fire hazards induced by power distribution networks: Modeling and Mapping
Core Problem: Wildland-urban fires triggered by failures in electrical distribution systems.
Key Innovation: A probabilistic hazard framework integrating wind fragility analysis, ignition probability modeling, and fire spread simulations to map fire hazards.
35. Multiple levels of human instability due to urban overland flow within the 21st century: An urban Catchment study case in Brazil
Core Problem: Risk to pedestrian stability due to overland flow forces during extreme rainfall events in urban environments.
Key Innovation: Hydrodynamic modeling to assess human instability with multiple levels of vulnerability, considering age, gender, weight, and height under climate change conditions.
36. Study on unsupervised gas outburst hazard early warning method based on spatiotemporal graph convolution network
Core Problem: Early warning of coal and gas outbursts in tunneling.
Key Innovation: Application of a deep learning-based spatiotemporal graph convolution model for gas outburst early warning using risk features of spatiotemporal data.
37. Time-Vertex machine learning for optimal sensor placement in temporal graph signals: Applications in structural health monitoring
Core Problem: Efficient sensor placement in structural health monitoring (SHM) to reduce costs without compromising monitoring quality, considering the temporal dynamics of structural behavior.
Key Innovation: A Time-Vertex Machine Learning (TVML) framework integrating Graph Signal Processing (GSP), time-domain analysis, and machine learning for interpretable and efficient sensor placement.
38. Rapid post-earthquake functionality prediction of subway systems based on graph neural networks and attentive transfer learning
Core Problem: Predicting the post-earthquake functionality of subway systems rapidly and accurately to aid in disaster response and recovery efforts.
Key Innovation: A graph neural network (FuncGNN) with a Hierarchical Gate-Query Attention (HGQA) mechanism for enhanced cross-domain transferability, enabling accurate predictions even with limited data from the target subway systems.
39. Connectivity-based seismic design strategy for bridge networks by controlling fragility correlation among individual bridges
Core Problem: Enhancing the seismic connectivity of bridge networks, which is crucial for post-earthquake relief and reconstruction, by addressing the fragility correlation among bridges.
Key Innovation: A seismic design strategy that adjusts design parameters to enhance and reduce fragility correlation on the same and different paths, respectively, while ensuring bridge safety against the design seismic action.
40. Probabilistic assessment of dynamic urban evacuation-sheltering functionality under typhoons based on interdependent road-shelter network
Core Problem: Assessing the functionality of urban evacuation-sheltering systems (UESS) during typhoons, considering the dynamic interdependence of road networks and shelters.
Key Innovation: A holistic functionality metric capturing temporal variations in evacuation timeliness and shelter availability, with a probabilistic assessment framework for UESS.
41. Introduction to a 45-year (1979–2023) global daily snow cover fraction product from multiple AVHRR satellites with accuracy assessment
Core Problem: Accurate monitoring of snow cover dynamics is essential for climate change attribution and water resource management, but long-term consistent data is lacking.
Key Innovation: A 45-year global daily snow cover fraction dataset (AVHRR10C1.V4) is generated by integrating data from 16 AVHRR sensors, addressing orbital drift and inter-sensor inconsistencies.
42. Estimating the upper depth of subsurface water on the Greenland Ice Sheet using multi-frequency passive microwave remote sensing, radiative transfer modeling, and machine learning
Core Problem: Monitoring subsurface water depth on the Greenland Ice Sheet is important for accurate runoff estimations, but is challenging to measure remotely.
Key Innovation: A machine learning model is trained using multi-frequency microwave brightness temperatures and radiative transfer modeling to estimate the upper depth of liquid water on the ice sheet.
43. Deep learning detection and analysis of eddies in the East Greenland marginal ice zone from Sentinel-1 SAR imagery
Core Problem: Detecting ocean eddies in the marginal ice zone (MIZ) is challenging due to complex surface signatures, yet these eddies are crucial for sea-ice dynamics and polar ocean-atmosphere interactions.
Key Innovation: Developed MIZ-EDYOLO, a deep learning model based on YOLOv9-t, customized for detecting MIZ eddies from dual-polarized Sentinel-1 SAR imagery, achieving high detection accuracy and enabling efficient, automated eddy identification.
44. Pan-Arctic winter sea ice classification using Sentinel-1 dual-polarized SAR images
Core Problem: Automated fine-scale sea ice classification across the pan-Arctic region is hindered by limited labeled data and overlapping backscatter intensity among ice types.
Key Innovation: Proposes IceDeepLab, a Deep Learning model based on DeepLabv3+, for automatic mapping of sea ice types across the pan-Arctic region in winter using Sentinel-1 dual-polarized SAR images, incorporating radar incidence angle as an input.
45. Satellite monitoring of Greenland wintertime buried lake drainage and potential ice flow response
Core Problem: Limited identification of buried lake drainages (BLDs) and unclear spatiotemporal dynamics across the Greenland Ice Sheet (GrIS), despite their potential influence on ice flow dynamics.
Key Innovation: Detects pan-GrIS wintertime BLDs by integrating Sentinel-1 and -2 satellite imagery and ArcticDEM data, revealing that BLDs have a more profound effect on ice flow dynamics than previously assumed, including triggering ice velocity anomalies and rerouting subglacial hydrologic pathways.
46. Remote sensing meta modal representation for missing modality land cover mapping: From EarthMiss dataset to MetaRS method
Core Problem: Extracting land cover mapping information is difficult when key remote sensing modalities are missing, and existing methods disrupt feature distribution.
Key Innovation: Introduces EarthMiss, a multimodal remote sensing land cover dataset, and MetaRS, a remote sensing meta modal representation framework for missing modality land cover mapping, which uses a meta-modal aware module and representation regularization training strategy.
47. The fully-automatic Sentinel-1 Global Flood Monitoring service: Scientific challenges and future directions
Core Problem: On-demand flood mapping services depend on human operators, limiting timely response. There is a need for automated, reliable flood mapping under diverse conditions.
Key Innovation: The Global Flood Monitoring (GFM) service processes all Sentinel-1 land images fully automatically in near-real time, combining three flood-mapping algorithms with reference water datasets and offering a novel flood-likelihood layer.
48. Characterizing land use changes triggered by crop-aquaculture co-cultivation from 2013 to 2022 based on a robust classification framework: Illustration in Jianghan Plain, China
Core Problem: Long-term mapping of land-use transformations triggered by rice-crayfish farming is challenging due to sample limitations and spectral complexities.
Key Innovation: Developed a robust classification framework integrating synergistic sample generation and hierarchical classification, achieving high accuracy in mapping land use dynamics in the Jianghan Plain.
49. Change tensor: Estimating complex topographic changes from point clouds using Riemann manifold surfaces
Core Problem: Estimating topographic surface changes (rigid movement and non-rigid deformation) from multi-temporal 3D point clouds is challenging due to surface roughness and point cloud heterogeneities.
Key Innovation: A method using Riemann manifold surfaces and change tensors to separate rigid and non-rigid topographic surface changes, tested on simulated and real topographic changes in mountain regions.
50. A Spatially Masked Adaptive Gated Network for multimodal post-flood water extent mapping using SAR and incomplete multispectral data
Core Problem: Adaptive integration of partially available MSI data into SAR-based post-flood water extent mapping is underexplored.
Key Innovation: A Spatially Masked Adaptive Gated Network (SMAGNet) that utilizes SAR data as the primary input and integrates complementary MSI data through feature fusion for post-flood water extent mapping.
51. Progressive uncertainty-guided network for binary segmentation in high-resolution remote sensing imagery
Core Problem: Binary semantic segmentation in remote sensing imagery faces challenges due to complex object appearances, ambiguous boundaries, and high similarity between foreground and background, introducing significant uncertainty.
Key Innovation: A Progressive Uncertainty-Guided Segmentation Network (PUGNet) that explicitly models uncertainty in a context-aware manner, decomposing it into foreground, background, and contextual components.
52. Mapping aboveground tree biomass and uncertainty using an upscaling approach: A case study of the larch forests in northeastern China using UAV laser scanning data
Core Problem: Forest biomass mapping and monitoring are vital for understanding carbon cycling, but a remote sensing-based framework that upscales biomass estimation from the individual-tree level remains underdeveloped, especially for rigorously quantifying the propagation of associated uncertainties.
Key Innovation: An upscaling framework for aboveground biomass (AGB) mapping and prediction uncertainty estimation using unmanned aerial vehicle laser scanning (UAVLS) data, including an analytical framework to characterize the AGB prediction uncertainty considering error propagation throughout the whole upscaling workflow.
53. Toward noise-resilient retrieval of land surface temperature and emissivity using airborne thermal infrared hyperspectral imagery
Core Problem: Effective retrieval of surface parameters using thermal infrared remote sensing is fundamentally challenging due to degraded spectral quality caused by narrow bandwidths of thermal infrared hyperspectral imagers, atmospheric line absorption interference, and limitations in sensor manufacturing.
Key Innovation: A Noise-Resilient Atmospheric Compensation with Temperature and Emissivity Separation (NRAC-TES) method, where the noise-resistant capability is mainly achieved through the NRAC module during the atmospheric compensation (AC) stage.
54. Dual-domain representation alignment for unsupervised height estimation from cross-resolution remote sensing images
Core Problem: Existing unsupervised domain adaptation methods for single-view remote sensing images often neglect the discrepancy in spatial resolution between the source and target domains, restricting their ability to generalize from low ground sample distance (GSD) to fine GSD images effectively.
Key Innovation: A cross-resolution unsupervised height estimation framework with Dual-Domain Representation Alignment (DDRA) to address both challenges: (1) How to capture resolution-invariant representations for better unsupervised domain adaptation; (2) How to maintain geometric integrity and spatial layout across domains.
55. ARSGaussian: 3D Gaussian Splatting with LiDAR for aerial remote sensing novel view synthesis
Core Problem: Novel View Synthesis (NVS) can reconstruct scenes from multi-view images and synthesize novel images from new viewpoints, which provides technical support for tasks such as target recognition and environmental perception. However, the challenges brought by large distances and sparse viewing angles during collection can cause the model to easily produce floaters and overgrowth issues due to geometric estimation errors.
Key Innovation: An innovative novel view synthesis (NVS) method for aerial remote sensing that incorporates LiDAR point cloud as constraints into the 3D Gaussian Splatting approach, adaptively guiding the Gaussians to grow and split along geometric benchmarks, thereby addressing the overgrowth and floaters issues.
56. Information transmission: Inferring change area from change moment in time series remote sensing images
Core Problem: Time series change detection is a critical task for exploring ecosystem dynamics using time series remote sensing images, because it can simultaneously indicate ‘where’ and ‘when’ change occur. While deep learning has shown excellent performance in this domain, it continues to approach change area detection and change moment identification as distinct tasks.
Key Innovation: A time series change detection network, named CAIM-Net (Change Area Inference from Moment Network), to ensure consistency between change area and change moment results by inferring change area from change moment based on the intrinsic relationship between time series analysis and spatial change detection.
57. Denoising VIIRS and Sentinel-2 MSI ocean color imagery for improved floating algae monitoring using noise-simulation-aided deep learning
Core Problem: The floating algae index (FAI) images derived from Visible Infrared Imaging Radiometer Suite (VIIRS) and Sentinel-2 Multispectral Instrument (MSI) have been widely used to monitor open ocean and coastal floating algal blooms, but they often suffer from complex and variable noise with different strengths, orientations, and distributions.
Key Innovation: A two-step denoising process: 1) simulating noise using spatial frequency domain information to generate customized representative training data from limited samples, and 2) training the state-of-the-art Multi-scale Image Restoration Network (MIRNet), which integrates multi-scale residual learning and attention mechanisms, for optimal performance.
58. A differentiable method for novel view SAR image generation via 3D Gaussian Splatting
Core Problem: SAR target images often suffer from insufficient view coverage due to the constraints of observation geometry, which poses challenges for data-driven SAR target classification and recognition methods.
Key Innovation: Integration of SAR imaging mechanisms with the concept of 3D Gaussian Splatting (3DGS), proposing an advanced differentiable method for novel view SAR image generation.
59. MHFNet: Multimodal hybrid fusion framework for misaligned SAR-Optical ship detection
Core Problem: Multimodal ship detection using optical and synthetic aperture radar (SAR) imagery is highly desirable for robust maritime monitoring. However, most existing fusion-based methods assume spatially aligned optical–SAR pairs, an assumption that rarely holds in practice due to differences in sensor geometry, acquisition timing, and registration errors.
Key Innovation: MHFNet, an alignment-aware hybrid fusion framework that systematically integrates improvements at the feature, loss, and decision levels, including a Feature Alignment and Fusion Module (FAFM), an Uncertainty-Aware Shape IoU Loss (US-Loss), and a Distance Matching Probabilistic Fusion (DMP-Fusion) algorithm.
60. Towards resolution-arbitrary remote sensing change detection with Spatial-frequency dual domain learning
Core Problem: Deep learning-based change detection (CD) is widely used for same-resolution images. However, high-resolution remote sensing images are often not continuously available over time, necessitating the ability to handle images with arbitrary resolution differences.
Key Innovation: A novel resolution-arbitrary CD network that enables CD with input images of arbitrary resolution differences, including a gradient-enhanced magnification-arbitrary module and a difference dual-domain learning module.
61. Beyond spectral signals: Geographic features drive bathymetric accuracy in the turbid Sancha Lake using machine learning
Core Problem: Accurate bathymetric mapping in inland water bodies presents significant challenges for conventional optical remote sensing due to complex water quality conditions and variable bottom types.
Key Innovation: A novel Spectral-Geospatial XGBoost Regression (SG-XGBoost) model that revolutionizes depth estimation by integrating comprehensive spectral transformations with explicit geographic coordinates through gradient boosting methodology.
62. Altimetry river water level retrieval over complex environments: assessment and diagnosis of different strategies
Core Problem: There remain many challenges to retrieving accurate river water levels using satellite altimetry, especially for rivers surrounded by various water bodies.
Key Innovation: A strategy that combines FFSAR and MWaPP+ to substantially enhance accuracy and the number of observations in complex river environments.
63. Deep learning for epistemic uncertainty in SMAP-derived soil moisture estimates over the Kulfo watershed, Ethiopia
Core Problem: Quantifying the reliability of soil moisture estimates remains a key challenge, especially in semi-arid regions where water availability is highly climate-dependent.
Key Innovation: A deep learning framework to produce a more reliable and better-calibrated measure of epistemic uncertainty, which directly quantifies analytical confidence, using data from the Soil Moisture Active Passive (SMAP) mission.
64. Mapping hidden heritage: Self-supervised pre-training on high-resolution LiDAR DEM derivatives for archaeological stone wall detection
Core Problem: Automated mapping of undocumented dry-stone walls in remote, vegetated areas is challenging due to occlusion and limited labeled training data.
Key Innovation: A self-supervised cross-view pre-training framework (DINO-CV) using knowledge distillation on LiDAR DEM derivatives to map dry-stone walls, addressing occlusion and data scarcity.
65. An integrated object-based-deep learning approach applied for mapping armed conflict impacts and land scars
Core Problem: Mapping environmental impacts and land scars from wars and armed conflicts requires efficient, cost-effective, and transferable methods.
Key Innovation: An integrated object-based image analysis (OBIA) and deep learning convolutional neural network (DL-CNN) approach for mapping war-related land scars, validated across multiple conflict zones.
66. Near real-time monitoring reveals extensive recent forest disturbance in Ghana's protected areas
Core Problem: Undocumented extent, rate, and locations of forest disturbances within Ghana's Protected Areas (PAs) due to logging and mining activities.
Key Innovation: Application of the fusion near real-time (FNRT) algorithm using Landsat, Sentinel-1, and Sentinel-2 data to monitor and quantify forest loss in Ghana's PAs.
67. Monitoring cropland cultivation, abandonment, fallowing and recultivation dynamics with regard to conflict intensity in war-affected Ukraine
Core Problem: Inconsistent estimates of abandoned cropland in Ukraine due to conflict, hindering post-war recovery planning and tax relief efforts.
Key Innovation: A two-level stratified random sampling design and unbiased estimators of area to quantify cultivated, fallow, and abandoned cropland in Ukraine, distinguishing fallow from abandoned lands.
68. Sensitivity to soil moisture of Capella X-band high-resolution SAR data over forests
Core Problem: Monitoring soil moisture in forests is difficult due to tree interference, limiting applications like landslide prediction and wildfire risk assessment.
Key Innovation: Demonstrates strong correlation (up to 0.95) between X-band SAR data and soil moisture in forest openings, enabling high-resolution soil moisture mapping for hazard applications.
69. 2023 activity of Nyamulagira volcano monitored by SAR interferometric coherence
Core Problem: Monitoring volcanic activity, including lava flows, is crucial for hazard assessment and risk management, especially in remote areas.
Key Innovation: Uses multi-temporal InSAR coherence to track lava effusion, flow extent, and solidification time at Nyamulagira volcano, supporting local monitoring efforts.
70. Historical influences of climate, vegetation and soil erosion on primary productivity of Huguangyan Maar Lake, southern China, documented by sedimentary phosphorus fractions for the past 1400 years
Core Problem: Understanding long-term phosphorus fraction dynamics in lake sediments and their responses to changes in soil erosion are critical for aquatic environmental management, but the interactions between climate, vegetation, and soil erosion in regulating lacustrine P fractions and primary productivity remain poorly constrained.
Key Innovation: Comprehensive analysis of P fractions in Huguangyan Maar Lake sediments, integrated with soil erosion records, pollen assemblages, paleoclimate data, and productivity proxies to reveal the synergistic effects of erosion, vegetation, and climate on primary productivity.
71. Linking runoff and sediment characteristics to karst development degree in small watershed
Core Problem: The surface/subsurface “dual” structure in karst areas complicates regional soil erosion and sediment transport processes, requiring better understanding for effective water-sediment management strategies.
Key Innovation: Investigated runoff and sediment processes in three karst watersheds in southern China with different development degrees, using long-term hydrological data and hysteresis analysis to establish the relationship between sediment transport and karst development.
72. Detection of the freezing state and non-closure distances of loess with different water contents under bidirectional freezing by ultrasonic testing
Core Problem: Accurately determining the freezing state and non-closure distances of artificial frozen walls is crucial for evaluating their development effectiveness, but traditional methods have limitations.
Key Innovation: Integrates temperature/water content monitoring with ultrasonic testing to analyze the evolution of ultrasonic parameters during loess freezing, establishing criteria for judging frozen wall closure and predicting non-closure distances.
73. Uniaxial compressive behavior of frozen silty clay at extreme low temperature with varying initial water content
Core Problem: Understanding frozen soil mechanics at temperatures below −30 °C is needed for construction in extremely cold conditions.
Key Innovation: Uniaxial compression tests on silty clay at varied water contents and temperatures down to -130°C, analyzing the influence of extreme low temperature and initial water content on mechanical behavior and failure mode.
74. Evolution of freezing in drainage ditches of high-speed railway tunnels in cold regions
Core Problem: Freezing of drainage ditches in high-speed railway tunnels in cold regions can lead to frost-related damages, compromising tunnel operation.
Key Innovation: Combines field monitoring, numerical simulation, and theoretical analysis to investigate the water freezing mechanism in drainage ditches, predicting sections prone to freezing based on longitudinal temperature distribution.
75. Hydro-thermo-mechanical coupling analysis of freeze-thaw process and optimization of freezing scheme in soft clay stratum
Core Problem: Frost heave and thaw settlement of soft clay stratum during Artificial Ground Freezing (AGF) projects.
Key Innovation: Integrated approach combining laboratory tests, theoretical modeling, and numerical simulations to address challenges of frost heave and thaw settlement. A novel hydro-thermo-mechanical model was proposed.
76. Research on the effects of train-induced wind on the thermal environment of tunnels in seasonally frozen regions
Core Problem: Aerodynamic flow induced by trains during tunnel traversal exerts a growing impact on the thermal environment of cold-region tunnels.
Key Innovation: Defines two thermal conditions (positive/negative effect) based on tunnel temperature monitoring data and analyzes the effect of train-induced wind on the tunnel thermal environment under these conditions.
77. Study on the influence of temperature field during thawing and sinking process of tropical undersea tunnel based on pipe curtain freezing method
Core Problem: Tropical undersea tunnels encounter significant risks related to freezing, thawing, and subsidence.
Key Innovation: Employs both physical similarity tests and numerical simulations to elucidate the evolution of the forced thawing temperature field and the thawing behavior of permafrost using the pipe curtain freezing method.
78. Intelligent identification and deformation analysis of subsurface cavities in deep excavations using CNN-based inverse modeling
Core Problem: Characterizing subsurface cavities in soft soil during deep excavation using limited monitoring data.
Key Innovation: A CNN-based inversion model to predict cavity dimensions and positions from deformation monitoring data.
79. Dynamic prediction of surrounding rock grades in TBM tunnels based on physics–data dual-driven model
Core Problem: Accurate and dynamic identification of surrounding rock grades in TBM tunnels for excavation safety.
Key Innovation: A physics-data dual-driven model combining CNN-LSTM with geological principles for improved rock grade identification.
80. Enhanced Swin-Transformer model for real-time assessment of overall shield machine cutterhead wear
Core Problem: Real-time assessment of shield machine cutterhead wear for tunneling safety and efficiency.
Key Innovation: An enhanced Swin-Transformer model (Swin-ICB Net) for real-time cutterhead wear assessment from operational parameters.
81. Automating graphical analysis in ground classification: A cluster-metrics-based approach
Core Problem: Automated ground classification in TBM operations for real-time decision-making.
Key Innovation: A cluster-metrics-based approach using machine learning to characterize TBM parameter scatter plots for ground classification.
82. A “Proactive-Prevention and Post-Resistant” support method for alleviating rockburst in deep-buried large-section tunnels
Core Problem: Mitigating rockbursts in deep, large-section tunnels where conventional support methods are insufficient.
Key Innovation: A 'Proactive-Prevention and Post-Resistant' support method using an initial tunnel and pre-stressed rockbolts to create an advanced pressure arch.
83. Precursor response mechanism of tunnel water surge in water-rich fault fracture zones based on similar physical model
Core Problem: Understanding and predicting water surges in tunnels within water-rich fault fracture zones.
Key Innovation: A physical model to analyze water surge precursors and a calculation model for critical anti-outburst thickness.
84. Mechanical response and damage evolution of surrounding rock in shallow-buried twin-arch tunnel under asymmetric loading with variable slope topography
Core Problem: Asymmetric deformation and failure in shallow twin-arch tunnels due to variable slope topography.
Key Innovation: Combined physical model testing and numerical simulation to examine displacement and stress evolution during excavation under asymmetric loading.
85. Effects of rock cavern fires on the interaction behavior and thermal spalling fracturing of rock support system
Core Problem: Compromised effectiveness of rock support systems during and after a fire event in underground caverns.
Key Innovation: Numerical study using CFD and a thermo-mechanical coupling phase field method to simulate heat conduction, thermal spalling, and deformation.
86. Seismic analysis of cross-fault tunnel based on source-based deterministic ground motion simulation method
Core Problem: Seismic analysis of cross-fault tunnels considering both inertial forces and fault dislocations.
Key Innovation: Introducing a source-based deterministic ground motion simulation method into seismic load calculation for cross-fault tunnels.
87. Deep tunnel deformation analysis based on large-scale physical test and fractional derivative creep model
Core Problem: Time-dependent deformation in deep rock tunnels under high in-situ stress.
Key Innovation: A fractional-damage viscoplastic creep model validated with large-scale physical tests to reproduce the full creep response of surrounding rock.
88. Seismic performance of double-layer tunnel linings: a multi-performance-level framework
Core Problem: Understanding the seismic behavior of double-layer tunnel lining systems under varying seismic intensities.
Key Innovation: Finite element modeling and theoretical formulations to analyze damage progression, failure mechanisms, and load transfer in double-layer linings.
89. Influence of saturation on the dynamic response of shallow-buried tunnel in unsaturated porous media under SV wave incidence: Coupled effects of incident angle, porosity, and frequency
Core Problem: Dynamic response of tunnels in unsaturated porous media under seismic loading.
Key Innovation: Analytical model using wave-function expansion and Hankel-function integral transform to investigate the effects of saturation, incident angle, porosity, and frequency.
90. A computational framework for dynamic quantitative assessment of surrounding rock damage based on failure approaching index in underground construction
Core Problem: Dynamic quantification of surrounding rock damage during underground construction.
Key Innovation: A dynamic evaluation system based on a failure approaching index (FAI) verified through experiments, simulations, and on-site applications.
91. Mechanism-informed overburden control based on granular load-bearing body: Application in shallow-buried coal seams
Core Problem: Overburden damage control in shallow-buried coal seams.
Key Innovation: A Balanced Mining Method (BMM) integrating directional roof cutting with the formation of a granular load-bearing body (GLB) for controlled overburden deformation.
92. Evaluation and analysis of the ductile dynamic response of mountain tunnels based on shaking table tests
Core Problem: Limited research on ductile displacements and deformation rate control indices in mountain tunnels under seismic loading.
Key Innovation: Direct measurement of ductile displacement time-history using shaking table tests, providing MDR thresholds for different tunnel depths and identifying four damage patterns.
93. Multi-method constrained stress states in the Qiabuqia geothermal field, NW China: Insights from basin-basement contrasts
Core Problem: Understanding stress variations between sedimentary basin fill and underlying granite basement in geothermal fields.
Key Innovation: Integrated ASR, hydraulic fracturing, and acoustic image logging to reveal stress contrasts and their influence on geothermal reservoir stability.
94. Seismic response and energy release of simulated faults with varying morphology and pre-stress under impact disturbance
Core Problem: Predicting induced seismicity risk by understanding the seismic response and energy release of fault surfaces under impact disturbances.
Key Innovation: Direct shear experiments on simulated faults with 3D-DIC and rock CT to quantify dynamic deformation, failure characteristics, and strain-energy distribution.
95. Rock mass discontinuity trace mapping using a voxel-based morphology-topology framework
Core Problem: Challenges in existing point cloud-based methods for discontinuity trace mapping, including insufficient trace connectivity and ambiguous topological relationships.
Key Innovation: A voxel-based morphology-topology approach for robust extraction of vectorized paths from the trace spatial skeleton, improving trace connectivity and topological accuracy.
96. Nonlinear progressive failure mechanism and shear strength model of deeply buried jinping marble under direct shear
Core Problem: Limited understanding of the nonlinear shear behavior of deep rocks, vital for safe design of underground engineering.
Key Innovation: Direct shear tests on Jinping marble with AE monitoring, revealing progressive failure mechanisms and a novel nonlinear shear strength model based on decoupled cohesion and friction angle evolution.
97. Failure behaviour simulation of transversely isotropic rocks considering realistic grain structure and bedding plane morphology
Core Problem: Accurately capturing rock failure behavior in transversely isotropic rocks with complex microstructures.
Key Innovation: A novel grain-based model (GBM-T) based on the bonded particle method is proposed to explore the intrinsic mechanism underlying the failure of transversely isotropic rocks at the grain scale.
98. Microstructure-driven prediction of undrained shear strength of deep-sea sediments: A multivariate approach bridging physical–mechanical properties
Core Problem: Conventional terrestrial soil models show limited applicability for predicting undrained shear strength in deep-sea environments, particularly underestimating strength parameters by neglecting sediment sensitivity and liquidity index.
Key Innovation: Predictive models are developed incorporating buoyant unit weight, liquidity index, and sensitivity as key governing factors, achieving superior accuracy compared to existing methods.
99. Predictive modeling of pore pressure build-up in vibratory pile driving through machine learning
Core Problem: Understanding and predicting soil liquefaction during vibratory pile driving for large infrastructure projects.
Key Innovation: Integration of 3D numerical modeling with machine learning (ANN and symbolic regression) to predict pore pressure and liquefaction potential, validated against experimental data.
100. SMOTE-BN-FLA: enhanced Bayesian network for rainfall-induced flood loss estimation and mechanism decoding in data-scarce regions
Core Problem: Accurate estimation of rainfall-induced flood losses in data-scarce regions with imbalanced data distributions.
Key Innovation: Integrated framework coupling Synthetic Minority Oversampling Technique (SMOTE) with data-driven Bayesian Networks (BN) for flood loss assessment and uncertainty quantification.
101. Steeper spatiotemporal distribution of extreme precipitation intensity in urban than rural regions
Core Problem: Understanding how urbanization alters the spatiotemporal characteristics of extreme precipitation and its impact on urban flooding.
Key Innovation: Analysis of urbanization-induced asymmetric spatiotemporal reorganization of extreme precipitation, revealing centralized intensification and peripheral weakening in urban areas.
102. Impacts of severe land use changes on the hydrology of snow dominated catchments in southern Quebec
Core Problem: Understanding hydrological response to land use changes in snow-dominated catchments.
Key Innovation: Evaluates hydrological response to extreme land use scenarios using the HYDROTEL model and CRCM5 climate simulations, focusing on snowmelt and peak flows.
103. Isotopic evidence unveils the regulation of biocrusts on shrub root water uptake strategy and water use efficiency in a semiarid ecosystem
Core Problem: Understanding the influence of biocrusts on vascular plant water use strategies in drylands.
Key Innovation: Investigates how moss-dominated biocrusts modulate shrub water uptake and water use efficiency using isotopic analysis and root trait measurements.
104. A Dimensionless Geomechanical Core to Inform ML-AI Prediction of Seismic-Induced Landslides
Core Problem: Predicting and mapping seismic-induced landslides requires computationally efficient methods that bridge the gap between large-scale modeling and advanced numerical simulations.
Key Innovation: A novel, scalable, and modular computational framework (Geomechanical Core - GMC) designed as the central engine component of hybrid AI systems for predicting seismic-induced landslides, using a generalized failure model, a dimensionless constitutive model, and a serial modular architecture.
105. Numerical investigation of thermo-mechanical response and heat transfer in clay during thermal cone penetration testing
Core Problem: Evaluating soil thermal properties using the Thermal Cone Penetration Test (T-CPT) requires accounting for the influence of the post-penetration process on subsequent soil responses during heat transfer.
Key Innovation: Development of a sequential coupled thermo-mechanical numerical model that explicitly simulates the penetration and subsequent heating-cooling phases of T-CPT in clayey soils, considering the soil's initial state parameters.
106. A hybrid Finite Volume and Material Point Method (FVMPM) for simulating multiphase flow in and around deformable porous media
Core Problem: Simulating multiphase flow within and around porous media, capturing fluid-solid coupling behaviors of granular materials.
Key Innovation: A hybrid finite volume and material point method (FVMPM) algorithm combining FVM for complex flow fields and MPM for large deformation behaviors of solids, with GPU acceleration.
107. Advancing internal erosion analysis through three-dimensional FEM simulation: insights from the Agly river dike
Core Problem: Investigating internal erosion, a primary cause of degradation and failure in hydraulic structures and natural deposits, where existing studies are limited to 2D cross-sectional analyses.
Key Innovation: Developing a large-scale 3D geometry model integrated with geophysical imaging data and a transient FEM to simulate seepage and fines migration, demonstrating the superiority of 3D modeling in capturing complex flow dynamics and erosion phenomena.
108. Decoding seepage percolation and preferential seepage paths in evolving rock fracture networks using complex network theory
Core Problem: Identifying seepage percolation and preferential seepage paths in evolving fracture networks, crucial for understanding hydraulic properties of engineered rock masses.
Key Innovation: Introducing a dynamic analysis framework based on complex network theory to represent fracture networks and track evolving seepage percolation and preferential flow paths efficiently.
109. Mesoscopic mechanisms of anisotropic suffusion behaviors of gap-graded soil: Identifying preferential suffusion paths based on strong-force chains and anisotropic pore structures
Core Problem: Understanding the meso-scale mechanisms underlying the effects of stress state on the suffusion behavior of gap-graded soil.
Key Innovation: Using CFD-DEM coupled simulations to examine suffusion behavior under different initial stress conditions, introducing an index to quantify the alignment between major principal stress and seepage direction, and developing a meso-scale analysis framework based on Voronoi tessellation to identify preferential suffusion paths.
110. Mathematical descriptions of grading linked with prediction of mechanical consequences of suffusion
Core Problem: Internal erosion in earth embankments changes soil properties, and this paper aims to capture these effects mathematically.
Key Innovation: New mathematical links and constitutive model ingredients are presented to capture the effects of suffusion, applying to a gap-graded soil. The evolution of the particle size distribution is characterised through a grading state index, defined in terms of geometrical properties which are fractal.
111. Machine learning-based national Vs30 models and maps for Italy
Core Problem: Estimating time-averaged shear-wave velocity in the uppermost 30 m (Vs30) across Italy, improving upon existing methods.
Key Innovation: A machine learning XGBoost–SHAP framework is used to integrate continuous and categorical variables, training two independent XGBoost models and modelling their residuals to construct a final combined Vs30 map at 300 m resolution.
112. Seismic site characterization and classification in Cameroon using full microtremor horizontal-to-vertical spectral ratio analysis and inversion
Core Problem: Characterizing seismic sites in Cameroon using microtremor H/V spectral ratio method to assess site effects and potential ground-motion amplification during earthquakes.
Key Innovation: Estimating H/V spectral ratios, inverting the full spectrum for shear-wave velocity profiles, and classifying sites according to seismic building codes, providing the first comprehensive database of Vs30 and site conditions for Cameroon and proposing a new empirical relationship for Vs30 applicable to regions with similar soil conditions.
113. Centrifuge experimental study on the seismic response of a shallow-buried underground structure under varying ground motion characteristics
Core Problem: Understanding how seismic characteristics affect the internal force response of shallow underground structures.
Key Innovation: Centrifuge shaking table tests were conducted on a shallow-buried, two-story, three-span structure, using sinusoidal waves and natural earthquake records to evaluate the effect of input amplitude and frequency on seismic wave propagation and structural response.
114. Seismic response analysis of underground structures considering soil spatial variability under different site classes
Core Problem: Quantifying the impact of soil spatial variability on the seismic response of underground structures across various site conditions for performance-based seismic design.
Key Innovation: Integrates random field theory (Karhunen–Loève expansion) with statistical analysis to evaluate the uncertainty in the seismic response of underground structures, considering soil spatial variability.
115. DEM investigation of realistic particle shape and particle breakage on the mechanical characteristics of geogrid-reinforced calcareous sand under cyclic loading
Core Problem: Calcareous sand, used in ocean engineering, degrades under cyclic loading. Understanding the macro- and micro-mechanical characteristics of geogrid-reinforced calcareous sand is crucial.
Key Innovation: Uses 3D laser scanning to capture actual shapes of calcareous sand particles and calibrates particle strength through single-particle breakage experiments. Conducts numerical triaxial cyclic shear tests with geogrids to investigate mechanical characteristics.
116. Seismic behavior of caisson quay walls with fiber-reinforced calcareous sand backfill
Core Problem: Caisson quay walls in oceanic regions using calcareous sand are prone to liquefaction during earthquakes, causing deformation or instability. Enhancing liquefaction resistance is needed.
Key Innovation: Evaluates the seismic performance of calcareous sand caisson quay walls with fiber reinforcement through cyclic simple shear and shaking table tests, focusing on fiber content and shaking intensity.
117. Influence of water table rise on the seismic performance of subway stations in clay
Core Problem: Rising water tables alter the load state of underground structures in clay, affecting their seismic performance. Understanding this impact is crucial for design and safety.
Key Innovation: Develops a single bounding surface constitutive model for clay and a 3D numerical model of a subway station to investigate the internal forces and seismic response due to water table rise.
118. Assessment of wind-wave-earthquake misalignment for offshore wind turbines in 3D geomorphological complex soil conditions, with soil-monopile interaction analysis
Core Problem: Current design guidelines for offshore wind turbines (OWTs) under combined wind, wave, and seismic loading often overlook the dynamic effects of intermediate directional misalignment.
Key Innovation: Integrates Pareto analysis and the Response Surface Method (RSM) with a validated 3D finite element model to identify effectual wind-wave-earthquake directional misalignment angles for enhanced turbine performance in complex soil conditions.
119. Seismic performance of simply supported hot rolled shape steel–UHPC composite girder bridges under near-fault ground motions
Core Problem: Quantifying the seismic advantages of a novel hot rolled shape steel (HRSS)–ultra high performance concrete (UHPC) composite girder bridge, particularly its reduced superstructure mass, under near-fault ground motions.
Key Innovation: Uses Incremental Dynamic Analysis-based fragility analysis to compare the seismic response of a HRSS-UHPC bridge with a conventional prestressed concrete (PSC) bridge, identifying Effective Peak Acceleration (EPA) as the optimal intensity measure.
120. Nonlinear response analysis of seismic wave scattering characteristics for three-dimensional real terrain
Core Problem: Accurately characterizing the influence of complex terrain on seismic wave scattering and site amplification effects for improved seismic design.
Key Innovation: Proposes a nonlinear seismic response calculation method using a three-dimensional geometric model based on a digital elevation model (DEM) and the improved Drucker-Prager elastoplastic constitutive model within the finite element method (FEM) framework.
121. Seismic fragility assessment of 100 m3 elevated water tanks on shallow foundation considering simplified fluid–structure–soil interaction models
Core Problem: Assessing the seismic vulnerability of reinforced concrete elevated water tanks (EWTs) considering fluid-structure interaction (FSI) and soil-structure interaction (SSI).
Key Innovation: Employs a nonlinear modeling framework that incorporates confined material behavior, fluid–structure interaction (FSI), and soil–structure interaction (SSI) to derive fragility curves for EWTs under varying conditions.
122. Shaking table test and energy dissipation mechanism of cable-seismic bolt-viscous damper composite energy dissipation system for wind turbine towers
Core Problem: Improving the damping efficiency of cable systems for vibration control in wind turbine towers under seismic loads.
Key Innovation: Proposes a Cable–Seismic Bolt–Viscous Damper System (CSVDS) with a dual energy dissipation mechanism, using seismic bolts to reduce bending stiffness and dissipate energy through shear deformation, combined with a viscous damper.
123. Seismic response of pile group foundations in deep saturated sand sites under strong earthquakes
Core Problem: Investigating the seismic performance of high-rise buildings on liquefiable sites, focusing on the coupled interactions among ground acceleration, pore water pressure, and structural dynamic response.
Key Innovation: Uses centrifuge shaking table tests with a scaled model featuring saturated sand, a pile-raft foundation, and a detailed superstructure to examine liquefaction development and structural response.
124. Behavior of piled rafts crossing reverse fault zones: Experimental and numerical evidence for setback strategies
Core Problem: Characterizing the failure mechanisms of piled rafts in fault-affected areas and developing targeted setback strategies to mitigate damage from fault dislocation.
Key Innovation: Combines centrifugal testing and numerical modeling to analyze the response of a piled raft at key surface exposure locations of free-field fault rupture zones, identifying distinct deformation zones and setback criteria.
125. Experimental investigation on the dynamic characteristics of marine soft soil under temperature effects
Core Problem: Understanding the dynamic characteristics of marine soft clay under the synergistic influences of hydration heat from lime piles and dynamic loads from continuous traffic flow.
Key Innovation: Evaluates the temperature effects on the dynamic properties of both undisturbed and reconstituted marine soft clay using temperature-controlled dynamic triaxial and resonant column test systems.
126. Soil-damped structure interaction analysis considering deep reinforcement learning-driven soil mesh optimization
Core Problem: Balancing accuracy and computational efficiency in modeling soil behavior near the structure-foundation interface for seismic response analysis.
Key Innovation: Proposes a deep reinforcement learning (DRL)-based mesh-seeking method for critical regions in soil modeling, enabling local mesh refinement in critical zones while coarsening other regions.
127. A novel method for efficient generation of 3D large-scale random fields via MEOLE
Core Problem: Addressing the computational load and efficiency issues in conducting research on large-scale three-dimensional (3D) random fields (RFs) in slope engineering.
Key Innovation: Proposes a new method based on a modified expansion optimal linear estimation method (MEOLE) for efficiently simulating 3D large-scale RFs, incorporating 3D rotational anisotropy to simulate inclined layered slopes.
128. Enhanced CRR prediction for liquefaction analysis using advanced machine learning techniques
Core Problem: Accurate prediction of cyclic resistance ratio (CRR) for liquefaction assessment in granular soils subjected to seismic loading.
Key Innovation: A hybrid machine learning framework integrating ensemble learning, model-agnostic explainability, and symbolic regression for direct and interpretable CRR prediction, validated against historical earthquake case studies.
129. A wind-driven device can improve the cooling efficiency of clogged crushed-rock embankments in permafrost regions
Core Problem: Crushed-rock embankments in permafrost regions suffer from poor cooling efficiency due to snow or sand clogging, leading to settlement deformation.
Key Innovation: A fully automatic wind-driven air pumping device is developed to enhance convective heat transfer, improving the cooling performance of clogged embankments and reducing settlement deformation.
130. A DEM creep contact model with damage evolution for frozen soil
Core Problem: Existing creep contact models in PFC fail to accurately simulate the non-attenuating creep behavior of frozen soil, which is critical for predicting settlement in cold region subgrades.
Key Innovation: A new discrete element creep contact model incorporating damage evolution is proposed, using a variable-stiffness damage element to capture the third-stage creep behavior of frozen soil.
131. CFD-DEM investigation into multi-mode evolutionary mechanisms of underground seepage erosion
Core Problem: Seepage erosion induced by damage to underground structures can lead to a transformation from suffusion to leakage (suffusion catastrophe), but the underlying mechanisms are not well understood.
Key Innovation: CFD-DEM method is used to simulate erosion unit tests, revealing three erosional modes (stable suffusion, catastrophic suffusion, and continuous leakage) and establishing governing equations for these modes.
132. Triaxial creep test and damage model study of coarse-grained materials from red-bed soft rocks under freeze–thaw cycles: A multiscale analysis
Core Problem: Coarse-grained materials (CGMs) from red-bed soft rocks used in subgrades subjected to freeze-thaw cycles lack understanding of their creep behavior, which is crucial for long-term performance analysis.
Key Innovation: Comprehensive triaxial creep tests and X-ray CT tests are conducted to analyze the creep behavior and mesostructural alterations of red-bed soft rock CGMs, proposing an extended Nishihara model considering freeze-thaw cycles and stress-induced damage.
133. Mechanical properties and microscopic mechanisms of freeze–thaw sand under traffic loading
Core Problem: Traffic loading and freeze-thaw cycles degrade the long-term stability and durability of sand subgrades in seasonal frozen regions, but the degradation mechanisms are not fully understood.
Key Innovation: Laboratory static and dynamic triaxial tests, SEM, and fractal theory are used to analyze the mechanical properties and microscopic mechanisms of freeze-thawed sands under traffic loading, elucidating the role of particle size distribution and pore structure.
134. Landslides with multi-slip surfaces in reservoir areas: Occurrence and geomorphic and geological controls
Core Problem: Understanding the regional occurrence and controlling factors of multi-slip surface landslides (MSSLs) for improved hazard prediction and mitigation.
Key Innovation: Comprehensive inventory of MSSLs in the Three Gorges reservoir area, evaluating geomorphic and geological controls, and proposing conceptual models for multistage evolution.
135. Durability and crack resistance of clayey soils treated with soybean induced carbonate precipitation under cyclic wetting–drying conditions
Core Problem: Desiccation cracking in clay soils leading to slope failure and pavement failure.
Key Innovation: Using soybean-urease induced carbonate precipitation (SICP) to enhance the properties of clayey soils and inhibit the formation of desiccation cracks under cyclic wetting–drying conditions.
136. Flyrock distance prediction using a hybrid LightGBM ensemble learning and two nature-based metaheuristic algorithms
Core Problem: Accurate and reliable prediction of flyrock is crucial for effectively managing and mitigating associated problems in open pit mining.
Key Innovation: Hybrid LightGBM model enhanced with multi-verse optimizer (MVO) and ant lion optimizer (ALO) metaheuristic algorithms for improved flyrock prediction accuracy.
137. 3D morphological characteristics of gravel bars in an engineered river using LiDAR data and aerial photographs
Core Problem: Understanding the 3D morphological characteristics of gravel bars in engineered rivers.
Key Innovation: Using LiDAR data and aerial photographs to analyze gravel bar morphology.
138. Rockfall triggering and meteorological variables in the Dolomites (Italian Eastern Alps)
Core Problem: Understanding the relationship between climate change (temperature and rainfall) and rockfall events in alpine areas.
Key Innovation: A novel approach based on the frequency of meteorological variables to understand the connection between climatic scenarios and rockfall events.
139. Mapping pan-Arctic riverine particulate organic carbon from space (1985 to 2022)
Core Problem: Quantifying changes in fluvial particulate organic carbon (POC) concentrations and fluxes in pan-Arctic rivers, which is intensifying due to climate change.
Key Innovation: Comprehensive analysis of changes in fluvial POC using satellite observations from 1985 to 2022, revealing a net rise in POC and regional contrasts in drivers.
140. Assessing landslide susceptibility with dynamic deformation monitoring and explainable machine learning: a case study in Longhua County, China
Core Problem: Landslide susceptibility assessment in Longhua County, China.
Key Innovation: Dynamic deformation monitoring combined with explainable machine learning for landslide susceptibility assessment.
141. Time Series Analysis of Fucheng-1 Interferometric SAR for Potential Landslide Monitoring and Synergistic Evaluation with Sentinel-1 and ALOS-2
Core Problem: Evaluating the performance of Fucheng-1 InSAR for landslide monitoring.
Key Innovation: Systematic evaluation of Fucheng-1 InSAR data for potential landslide identification and deformation monitoring, compared with Sentinel-1 and ALOS-2.
142. MLEWS: a series of software systems to implement machine learning for geo-hazards risk early warning based on meteorological factors
Core Problem: Developing a machine learning-based early warning system for geo-hazards.
Key Innovation: Implementation of machine learning techniques within a software system for geo-hazard risk early warning using meteorological factors.
143. Remote Sensing, Vol. 18, Pages 286: Cross-Domain Landslide Mapping in Remote Sensing Images Based on Unsupervised Domain Adaptation Framework
Core Problem: Landslide mapping using deep learning models suffers from performance degradation in new geographic areas due to the need for extensive labeled data and sensitivity to domain shifts.
Key Innovation: An unsupervised domain adaptation framework (LandsDANet) is proposed, using adversarial learning, image style transformation with a Wallis filter, rare class sampling, and contrastive loss to improve cross-domain landslide identification.
144. Remote Sensing, Vol. 18, Pages 283: An Automatic Identification Method for Large-Scale Landslide Hazard Potential Integrating InSAR and CRF-Faster RCNN: A Case Study of Ahai Reservoir Area in Jinsha River Basin
Core Problem: Manual delineation of landslide anomalies from InSAR data is labor-intensive and time-consuming, hindering large-scale landslide mapping efforts.
Key Innovation: An automated approach integrating SBAS-InSAR with an enhanced CRF-Faster R-CNN model (incorporating ResNet-50, CBAM, and FPN) for automated detection of landslide-prone areas, validated in the Ahai Reservoir area.
145. Remote Sensing, Vol. 18, Pages 273: Dynamic Monitoring and Analysis of Mountain Excavation and Land Creation Projects in Lanzhou Using Multi-Source Remote Sensing and Machine Learning
Core Problem: Mountain Excavation and Land Creation Projects (MELCPs) in mountainous regions require dynamic monitoring and risk management for sustainable urban development.
Key Innovation: An integrated monitoring framework combining Sentinel-1 SAR, Sentinel-2 optical imagery, SRTM DEM, and field data, incorporating multi-temporal change detection, random forest classification, and time-series InSAR analysis to capture spatiotemporal evolution and subsidence mechanisms associated with MELCPs.
146. Remote Sensing, Vol. 18, Pages 234: Spatiotemporal Prediction of Ground Surface Deformation Using TPE-Optimized Deep Learning
Core Problem: Modeling surface deformation time series obtained through InSAR using deep learning methods faces challenges in hyperparameter configuration and interpretability, limiting its engineering applications.
Key Innovation: Combines Tree-structured Parzen Estimator (TPE)-based Bayesian optimization with ensemble inference to enhance the reliability of deep learning models for spatiotemporal prediction of ground surface deformation, particularly in mining areas and fault zones.
147. Remote Sensing, Vol. 18, Pages 231: Sub-Canopy Topography Inversion Using Multi-Baseline Bistatic InSAR Without External Vegetation-Related Data
Core Problem: Traditional InSAR-based sub-canopy topography inversion relies on simplified models and external vegetation data, limiting accuracy in boreal forest areas with discontinuous canopies.
Key Innovation: Combines the Random Volume over Ground (RVoG) model with multi-baseline InSAR data and SAR-based dimidiate pixel model to retrieve sub-canopy topography without relying on external vegetation datasets.