TerraMosaic Daily Digest: May 28, 2026
Daily Summary
May 28 advances hazard science by tightening the link between observation, process, and decision geometry. Coastal cliff runout is measured as an empirical distribution rather than inferred from generic setbacks, while TSUSY turns historical tsunami occurrence into a traceable global data product. Thawing moraines, lava-delta retreat, rock-glacier deactivation, layered-loess erosion, and soil-rock mixture slopes all show terrain instability as a coupled response to thermal, hydrologic, structural, or material heterogeneity. The central advance is the conversion of landform change, event history, and deformation into process constraints that can be used in hazard zoning, scenario modelling, or design.
The hydrological and infrastructure papers are similarly process-centred. Flood risk is treated across mechanisms and scales, from snow-dominated catchment shifts and optical-SAR inundation mapping to neural-operator urban flood simulation and glacial-lake downstream consequences. Earthquake and underground-engineering studies emphasize friction laws, liquefaction intensity measures, spatially variable metro response, submarine-tunnel fragility, deep TBM collapse, and low-cost liquefaction mitigation. AI and remote-sensing contributions are clearest where they improve transfer under real deployment limits: multimodal change detection, distribution-shift benchmarks, onboard damage assessment, AI weather assimilation, SAR-derived 3-D displacement, and sensor-fusion methods for flood, snow, wetland, and infrastructure monitoring.
Key Trends
Five methodological movements stand out: empirical hazard-zone calibration, thermally coupled cold-region deformation, mechanism-aware flood modelling, heterogeneity-aware infrastructure risk, and deployment-tested AI for Earth observation.
- Hazard zoning is becoming distributional and process-calibrated: The coastal landslide runout record, TSUSY tsunami database, Makran synthesis, tsunami-like scour experiments, liquefaction intensity-measure analysis, and log-pile mitigation tests all replace broad assumptions with event- or mechanism-specific evidence.
- Cold-region and mountain hazards are being framed as coupled thermal-hydrologic deformation problems: Thaw-induced moraine motion, Dry Andes rock-glacier deactivation, permafrost ground-temperature prediction, freeze-thaw seepage in loess, snow-dominated flood-mechanism shifts, duct-ventilated embankment cooling, and Tibetan Plateau lake dynamics connect climate forcing to terrain or infrastructure response.
- Flood science is linking physical mechanisms, observation fusion, and fast simulation: The Miyun optical-SAR flood map, LarNO urban flood model, snow-catchment flood-mechanism study, endorheic flood mapping, glacial-lake hydrology, Lake Eyre SWOT analysis, and long-term lake-storage work show a movement from flood extent products toward state-aware flood dynamics.
- Infrastructure geohazard assessment is explicitly incorporating spatial variability and system resilience: Soil-rock mixture slope stability, metro-station seismic prediction, submarine-tunnel resilience, deep hard-rock TBM collapse, tunnel digital twins, reclaimed-ground embankment response, offshore scour, and CPT inversion all treat material heterogeneity or system fragility as central design variables.
- AI for geoscience is being tied to sensor constraints, physics, and deployment shift: OmniCD, EarthShift, onboard building-damage assessment, AI weather data assimilation, long-rollout AI forecast benchmarking, Capella 3-D displacement retrieval, temporal satellite reconstruction, semi-supervised change detection, and optical or SAR fusion methods share a common requirement: they test transferability and observation limits rather than only benchmark accuracy.
Selected Papers
This issue contains 70 selected papers from 1,770 papers analyzed. The papers are anchored by empirical coastal landslide runout, a global tsunami-event database, thaw-induced moraine deformation, optical-SAR flood mapping, neural-operator urban flood modelling, changing snow-dominated flood mechanisms, earthquake friction laws, Makran tsunami synthesis, spatially variable soil-rock mixture slope stability, deep TBM tunnel collapse, layered-loess erosion, and liquefaction mitigation. The broader set extends those themes through lava-delta retreat, rock-glacier deactivation, tsunami-like scour, permafrost temperature prediction, metro and submarine-tunnel seismic resilience, SAR-derived 3-D displacement, building-damage and gully-erosion mapping, glacial-lake hydrology, compound drought, AI weather assimilation, Earth-observation robustness, and heterogeneity-aware geotechnical design.
1. Observations of coastal cliff landslide runout in southern California from 21 years of data
Core Problem: Coastal-cliff hazard zones often rely on generic setbacks even though debris runout on beaches is controlled by local cliff geometry and failure history.
Key Innovation: Earth Surface Processes and Landforms compiles 21 years and more than 700 surveys from southern California, quantifying runout distributions and showing that one-half cliff height captures nearly all observed failures.
2. The TSUSY Database: a global database of historical tsunami events and a tsunami-occurrence criterion based on historical earthquakes
Core Problem: Tsunami hazard analysis needs a consistent global record of events and a defensible criterion for linking historical earthquakes to tsunami occurrence.
Key Innovation: Natural Hazards and Earth System Sciences presents TSUSY, a global historical tsunami database, and defines an earthquake-based occurrence criterion for reproducible event classification.
3. Deciphering thaw-induced deformation signatures of ice-rich moraines and periglacial permafrost using MT-InSAR
Core Problem: Ice-rich moraines can deform under thawing permafrost before overt failure, but their spatially variable signatures are difficult to isolate with sparse field data.
Key Innovation: The study uses multi-temporal InSAR to decipher thaw-induced deformation patterns in periglacial moraines, connecting surface motion to permafrost degradation and moraine stability.
4. Machine learning–based flood inundation mapping using fused optical and SAR remote sensing: a case study of the Miyun flood
Core Problem: Flood mapping during cloudy or rapidly evolving events is limited when optical and SAR observations are used separately.
Key Innovation: The paper fuses optical and SAR remote-sensing data in a machine-learning workflow to delineate flood inundation for the Miyun flood, improving event-scale water mapping under mixed sensor constraints.
5. Large-scale urban flood modeling and zero-shot high-resolution generalization with LarNO
Core Problem: High-resolution urban flood simulations are computationally expensive and hard to transfer across scales or unseen domains.
Key Innovation: LarNO uses a neural-operator architecture for large-scale urban flood modelling and demonstrates zero-shot high-resolution generalization, pointing toward rapid city-scale flood scenario generation.
6. Changing Flood‐Generating Mechanisms Impact Flood Characteristics in Snow‐Dominated Catchments
Core Problem: Flood frequency and magnitude in snow-dominated catchments can change because the dominant generating mechanism shifts, not only because precipitation totals change.
Key Innovation: Geophysical Research Letters links changing flood characteristics to altered flood-generating mechanisms, separating snowmelt, rainfall, and mixed-process contributions in cold-region hydrology.
7. Multi‐Scale Rate‐ and Roughness‐Dependent Frictional Constitutive Law and Dynamic Earthquake Sequence Simulation
Core Problem: Earthquake-cycle models need friction laws that reproduce both rate dependence and rough-surface effects across scales.
Key Innovation: The study formulates a multi-scale rate- and roughness-dependent constitutive law and applies it to dynamic earthquake sequence simulation.
8. 80 Years of research on tsunamigenic earthquakes in the Makran subduction zone (1945–2025): a review- Part A: MSZ features
Core Problem: The Makran subduction zone has high tsunami potential but uneven historical, geological, and seismological constraints.
Key Innovation: The review synthesizes 80 years of research on Makran tsunamigenic earthquakes, consolidating source-zone features relevant to regional tsunami hazard assessment.
9. Stability assessment of SRM slopes considering spatial randomness and cross-correlated spatial variability via a random NCDDAM
Core Problem: Slope stability in soil-rock mixtures depends on spatially variable and cross-correlated material properties that deterministic analyses often suppress.
Key Innovation: The paper evaluates SRM slope stability with a random NCDDAM framework that represents spatial randomness and cross-correlated variability.
10. Multi-stage process of stress-structure-induced collapse in a deep hard-rock TBM tunnel
Core Problem: Deep TBM tunnels can collapse through staged interaction between high in situ stress and adverse rock structure, but the process is difficult to reconstruct.
Key Innovation: The study resolves a multi-stage stress-structure-induced collapse mechanism in a deep hard-rock TBM tunnel, linking excavation response to structural controls.
11. Soil erosion processes on layered loess slopes under extreme rainfall events
Core Problem: Layered loess slopes can erode and destabilize rapidly during extreme rainfall, yet the process sequence is sensitive to stratigraphy.
Key Innovation: The paper characterizes soil erosion processes on layered loess slopes under extreme rainfall, clarifying how stratification modulates runoff, erosion, and slope degradation.
12. Shallow ground improvement by log piles for liquefaction mitigation of small residential buildings: experimental study by 1-g shaking table tests
Core Problem: Small residential buildings often need low-cost liquefaction mitigation methods that are practical for shallow ground improvement.
Key Innovation: One-g shaking-table experiments test log piles as shallow ground improvement, evaluating their ability to reduce liquefaction effects beneath small residential buildings.
13. Rapid changes of the lava‐delta coastlines formed by the 2021 volcanic eruption on La Palma, Canary Islands
Core Problem: New lava deltas can retreat rapidly because fresh volcanic fronts combine weak internal structure with energetic wave attack.
Key Innovation: Earth Surface Processes and Landforms uses sub-metre satellite imagery to track daily-to-monthly shoreline retreat after the 2021 La Palma eruption and links contrasting delta behaviour to emplacement structure.
14. The Lizoite rock glacier, Dry Andes of Northwestern Argentina: A deactivation case in progress?
Core Problem: Dry Andes rock glaciers are widespread but poorly constrained in their thermal and kinematic transition from active to inactive states.
Key Innovation: The study combines GNSS velocity, ground-temperature monitoring, and geomorphic mapping to diagnose progressive deactivation of the Lizoite rock glacier.
15. Fluid‐Induced Magnetic Enhancement in Sandstone Friction Experiments: Implications for Coseismic Fault Temperature Estimates
Core Problem: Magnetic proxies for coseismic heating can be altered by fluids and frictional processes, complicating earthquake temperature estimates.
Key Innovation: The experiments show fluid-induced magnetic enhancement in sandstone friction tests, refining interpretation of magnetic signals in fault-heating studies.
16. Impoundment Depth Effects on Bore Hydrodynamics and Scour around a Berm-Mounted Structure during Tsunami-Like Inundation and Drawdown
Core Problem: Scour around coastal structures during tsunami inundation and drawdown depends on bore depth and flow reversal, but the coupled response is difficult to parameterize.
Key Innovation: The study quantifies impoundment-depth effects on bore hydrodynamics and scour around a berm-mounted structure during tsunami-like inundation and drawdown.
17. Integrating physics-informed data augmentation and ensemble learning for ground temperature prediction in permafrost regions
Core Problem: Permafrost ground-temperature prediction is limited by sparse observations and strong seasonal thermal dynamics.
Key Innovation: The paper integrates physics-informed data augmentation with ensemble learning to improve ground-temperature prediction in permafrost regions.
18. Seismic stability prediction of metro stations considering soil spatial variability: A stacked ensemble machine learning approach
Core Problem: Seismic stability of metro stations is sensitive to spatially variable soil conditions that are difficult to propagate through routine design.
Key Innovation: A stacked ensemble machine-learning model predicts metro-station seismic stability while accounting for soil spatial variability.
19. Probabilistic seismic resilience assessment integrating optimal IM selection and system-level fragility analysis: Case study of submarine tunnel
Core Problem: Submarine tunnel resilience depends on choosing intensity measures that link seismic demand to system-level fragility.
Key Innovation: The study integrates optimal ground-motion intensity-measure selection with probabilistic system-level fragility analysis for a submarine tunnel case.
20. Ground Motion Intensity Measures at Liquefaction Field Case History Sites
Core Problem: Liquefaction triggering models depend on the intensity measures assigned to historical field case histories.
Key Innovation: The paper evaluates ground-motion intensity measures at liquefaction field case-history sites, supporting better calibration of empirical liquefaction assessment.
21. Mapping Flood in Endorheic Depressions Using Multitemporal and Multiresolution Remote Sensing Data—Example of Chotts Merouane and Melrhir, Algeria
Core Problem: Flood mapping in closed basins is complicated by shallow water, changing salinity, and intermittent inundation.
Key Innovation: GeoHazards maps flooding in Algerian endorheic depressions using multitemporal and multiresolution remote-sensing data.
22. Joint Hail Detection from Satellite and Radar Observations with Spatially Adaptive Alignment and Wavelet-Gated Refinement
Core Problem: Hail detection needs to reconcile satellite context with radar structure under spatial misalignment and multi-scale storm texture.
Key Innovation: Remote Sensing combines satellite and radar observations with spatially adaptive alignment and wavelet-gated refinement for hail detection.
23. Retrieval of 3-D Ground Displacement Time Series From Multitemporal/Multiangle Capella Space SAR Data Acquired From Mid-Inclination Orbits
Core Problem: Single-view SAR deformation products cannot fully resolve three-dimensional ground motion.
Key Innovation: The study retrieves 3-D ground-displacement time series from multitemporal and multiangle Capella Space SAR data acquired from mid-inclination orbits.
24. DSDRN: A Dual-Stream Dual-Refinement Network With Superpixel and Location Semantics for Building Damage Classification
Core Problem: Post-disaster building-damage mapping must preserve both object boundaries and spatial context under heterogeneous damage patterns.
Key Innovation: DSDRN combines superpixel and location semantics in a dual-stream dual-refinement network for building damage classification.
25. A Hybrid CNN-Transformer Architecture for Gully Erosion Extraction in Northeast China Using High-Resolution Images
Core Problem: Gully erosion mapping from high-resolution imagery requires both local texture recognition and longer-range spatial context.
Key Innovation: The paper develops a hybrid CNN-transformer architecture to extract gully erosion features in Northeast China.
26. Downstream hydrological consequences of existing and potential glacial lakes in a changing climate
Core Problem: Expanding glacial lakes can alter downstream hydrology as well as outburst-flood exposure.
Key Innovation: Journal of Hydrology quantifies downstream hydrological consequences of existing and potential glacial lakes under a changing climate.
27. Seepage evolution in loess driven by freeze-thaw cycling: a multiscale investigation
Core Problem: Freeze-thaw cycling can restructure loess pore networks and change seepage pathways that influence slope stability.
Key Innovation: The paper investigates seepage evolution in loess under freeze-thaw cycling using a multiscale framework.
28. Land use and land cover change have reduced meteorological-hydrological compound drought on a global scale
Core Problem: Compound drought risk reflects both climate forcing and land-cover effects on water partitioning.
Key Innovation: Journal of Hydrology reports that land use and land-cover change have reduced meteorological-hydrological compound drought on a global scale.
29. Ensemble‐Based Assimilation of Sounding Observations With AI Weather Models
Core Problem: AI weather forecasts need practical data-assimilation strategies that can ingest sparse but information-rich atmospheric soundings.
Key Innovation: Geophysical Research Letters develops ensemble-based assimilation of sounding observations for AI weather models.
30. OmniCD: A Foundational Framework for Remote Sensing Image Change Detection Guided by Multimodal Semantics
Core Problem: Change-detection systems often fail when visual appearance shifts across sensors, locations, or event types.
Key Innovation: OmniCD introduces a foundational remote-sensing change-detection framework guided by multimodal semantics.
31. EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation
Core Problem: Remote-sensing models used in hazards can degrade sharply when deployed under real-world distribution shifts.
Key Innovation: EarthShift provides a benchmark for measuring robustness to real-world distribution shifts in Earth observation.
32. Can AI Weather Models Predict Beyond Two Weeks? A Quantitative Benchmark and Analysis of Long Rollouts
Core Problem: Hazard applications need to know where AI weather models lose skill beyond the short-range window.
Key Innovation: The benchmark quantifies whether AI weather models can predict beyond two weeks and analyzes long-rollout degradation.
33. Optimizing Latent Representations for Robust Building Damage Assessment Onboard Earth Observation Satellites
Core Problem: Disaster response can be delayed when raw satellite imagery must be downlinked before damage assessment.
Key Innovation: The paper optimizes latent representations for robust building-damage assessment onboard Earth-observation satellites.
34. Virtual testbed for multi-risk assessment: defining RETURNVILLEs to support the analysis and testing of DRM and CCA solutions in realistic urban contexts
Core Problem: Urban disaster-risk methods need realistic virtual environments where DRM and climate-adaptation solutions can be stress-tested.
Key Innovation: The paper defines RETURNVILLEs as virtual testbeds for multi-risk assessment in realistic urban contexts.
35. Disentangling Management and Climate Drivers in an Anthropogenic Transitional Mediterranean Coastal Groundwater-Dependent Ecosystem
Core Problem: Coastal groundwater-dependent ecosystems respond to both management and climate forcing, which are difficult to disentangle observationally.
Key Innovation: Remote Sensing separates management and climate drivers in an anthropogenic transitional Mediterranean coastal groundwater-dependent ecosystem.
36. Reconstruction of Global 0.25° Land Lightning Density from 1979 to 2025 based on an ensemble machine learning
Core Problem: Lightning risk analysis needs long, spatially consistent global density records that predate modern satellite coverage.
Key Innovation: The paper reconstructs global 0.25-degree land lightning density from 1979 to 2025 using ensemble machine learning.
37. Volumetric analysis of a playa lake using SWOT data: An improved understanding of the inflows to Kati Thanda-Lake Eyre
Core Problem: Intermittent playa lakes are difficult to quantify volumetrically because water extent and elevation change rapidly.
Key Innovation: Journal of Hydrology uses SWOT data for volumetric analysis of Kati Thanda-Lake Eyre, improving understanding of inflows.
38. Temporal scale dictates the dominant driver of lake dynamics on the Tibetan Plateau
Core Problem: Dominant controls on lake dynamics can change with the temporal scale of analysis.
Key Innovation: The study shows that temporal scale dictates the dominant driver of lake dynamics on the Tibetan Plateau.
39. Laser bathymetry on rough riverbed channels: State‐of‐the‐art and future prospects
Core Problem: Rough, shallow, turbulent rivers remain difficult for bathymetric mapping despite their importance for sediment transport and restoration.
Key Innovation: Earth Surface Processes and Landforms reviews laser bathymetry for rough riverbeds and identifies sensor and deployment limits for future UAV-borne surveys.
40. Denudation rates of carbonate coasts: Insights from in situ cosmogenic 36CL from Cuban coastal terraces
Core Problem: Rocky carbonate coasts record both marine and continental erosion, but the relative controls on denudation vary across terrace position and age.
Key Innovation: Earth Surface Processes and Landforms combines in situ cosmogenic 36Cl with coral dating to quantify denudation rates on Cuban coastal terraces.
41. Temporal dune growth dynamics based on a 3‐year monitored dune featuring marram grass and brushwood fences
Core Problem: Nature-based dune interventions for flood protection require evidence on how vegetation, brushwood fences, and management choices affect sand accumulation over multiple years.
Key Innovation: Earth Surface Processes and Landforms uses three years of drone surveys to quantify dune growth and sand-trapping efficiency under different intervention designs.
42. Global Fair‐Weather Bias in Remotely Sensed Coastal Suspended Sediment Concentration
Core Problem: Satellite sediment records can under-sample stormy conditions that control coastal erosion and sediment transport.
Key Innovation: Geophysical Research Letters quantifies global fair-weather bias in remotely sensed coastal suspended-sediment concentration.
43. Highland Pathways Shape Global Dust Vertical Transport and Its Climate Effects
Core Problem: Dust-climate effects depend on how terrain pathways inject particles vertically into the atmosphere.
Key Innovation: Geophysical Research Letters shows how highland pathways shape global dust vertical transport and climate effects.
44. An improved modelling chain for bias-adjusted high-resolution climate and hydrological projections for Norway
Core Problem: Hydrological risk studies require climate projections that are bias-adjusted and downscaled coherently through a modelling chain.
Key Innovation: Geoscientific Model Development presents an improved high-resolution climate and hydrological projection chain for Norway.
45. Informing Thin-Layer Placement for Coastal Wetland Restoration Through Remote Sensing and Community Outreach
Core Problem: Wetland restoration by thin-layer sediment placement needs spatial evidence for where elevation and vegetation response justify intervention.
Key Innovation: Remote Sensing uses remote sensing and community outreach to inform thin-layer placement for coastal wetland restoration.
46. Post-Stack Seismic Inversion with Non-Convex Total Generalized Variation Regularization
Core Problem: Post-stack seismic inversion must recover sharp subsurface structure while controlling noise and ill-posedness.
Key Innovation: Remote Sensing applies non-convex total generalized variation regularization to post-stack seismic inversion.
47. Lithospheric Thermal Structure Beneath East Antarctica Derived from Aeromagnetic Anomaly Analysis
Core Problem: Ice-sheet and geodynamic models depend on lithospheric thermal structure that is difficult to measure beneath East Antarctica.
Key Innovation: Remote Sensing derives lithospheric thermal structure beneath East Antarctica from aeromagnetic anomaly analysis.
48. CMF-SnowNet: Cross-Modal Fusion With Attent-Guided GAN and Temporal ConvNeXt for Long-Term Snow Cover Prediction
Core Problem: Long-term snow-cover prediction requires combining complementary sensors across cloudy and data-limited periods.
Key Innovation: CMF-SnowNet uses cross-modal fusion with an attention-guided GAN and temporal ConvNeXt for long-term snow-cover prediction.
49. Point-DCA-Enhanced Deep Learning Network for Photon Signal Classification in UAV-Based Photon-Counting LiDAR Bathymetry
Core Problem: Photon-counting bathymetric LiDAR needs robust separation of signal and noise in shallow-water UAV surveys.
Key Innovation: The paper introduces a Point-DCA-enhanced deep network for photon signal classification in UAV-based photon-counting LiDAR bathymetry.
50. A Bézier Curve-Based Progressive TIN Densification Filtering Algorithm for Airborne LiDAR Data in Complex Transmission Corridor Terrains
Core Problem: Terrain extraction in transmission corridors is difficult where vegetation, infrastructure, and complex relief overlap.
Key Innovation: The study proposes a Bezier-curve progressive TIN densification filtering algorithm for airborne LiDAR in complex transmission-corridor terrain.
51. Miniaturized Coherent Doppler Wind Lidar with Self-Compensating Harris Hawks Optimization Algorithm for Low-Altitude UAV-Borne Wind Sensing
Core Problem: Low-altitude wind monitoring for hazards and boundary-layer studies requires compact sensors with stable self-compensation.
Key Innovation: Remote Sensing develops a miniaturized coherent Doppler wind LiDAR with self-compensating Harris Hawks optimization for UAV deployment.
52. Semi-Supervised Change Detection for High-Resolution Remote Sensing Images Based on Label Extension
Core Problem: High-resolution change detection is limited by scarce labelled data across different scenes and disturbance types.
Key Innovation: Remote Sensing proposes a semi-supervised change-detection method based on label extension.
53. A Review on Super-Resolution Reconstruction of Single-Frame Remote Sensing Images via Diffusion Models
Core Problem: Single-frame remote-sensing imagery often lacks the resolution required for fine-scale hazard interpretation.
Key Innovation: Remote Sensing reviews diffusion-model approaches for single-frame remote-sensing super-resolution reconstruction.
54. Efficient spectral-temporal reconstruction of long-term satellite time series via temporal segments and mask-informed embedding
Core Problem: Long-term satellite records contain gaps and corrupted observations that reduce their value for change analysis.
Key Innovation: The study reconstructs spectral-temporal satellite time series using temporal segments and mask-informed embedding.
55. Quantifying facility-scale CO2 emissions using spaceborne hyperspectral imageries
Core Problem: Facility-scale CO2 monitoring from space requires resolving small atmospheric plumes in hyperspectral observations.
Key Innovation: The paper quantifies facility-scale CO2 emissions using spaceborne hyperspectral imagery.
56. Greening-induced soil drying mitigated by climate change in China's Loess Plateau
Core Problem: Vegetation greening can dry soils and alter slope hydrology, but climate change can partly offset that effect.
Key Innovation: The study shows that climate change mitigates greening-induced soil drying in Chinas Loess Plateau.
57. Analysis of the cooling effect of duct-ventilated embankments with different duct spacings in high-temperature permafrost regions
Core Problem: High-temperature permafrost embankments require passive cooling designs that remain effective under warming.
Key Innovation: Cold Regions Science and Technology analyzes the cooling effect of duct-ventilated embankments with different duct spacings.
58. Scales and irregularities of columnar jointed rock masses: Insights from field investigations and thermo-mechanical coupling numerical simulations
Core Problem: Columnar jointed rock masses have scale-dependent irregularities that affect mechanical response under thermal and stress loading.
Key Innovation: The paper combines field investigation and thermo-mechanical numerical simulation to assess scales and irregularities in columnar jointed rock masses.
59. Intrusion and erosion characteristics of calcium bentonite-based engineered barrier components
Core Problem: Engineered bentonite barriers can degrade through intrusion and erosion when interacting with groundwater and adjacent materials.
Key Innovation: The study examines intrusion and erosion characteristics of calcium bentonite-based engineered barrier components.
60. Mechanical degradation of bedded salt rock induced by ScCO2– brine exposure: Insights from nanoindentation tests
Core Problem: Subsurface CO2 storage can alter salt-rock mechanical properties through coupled ScCO2-brine exposure.
Key Innovation: The paper uses nanoindentation tests to quantify mechanical degradation of bedded salt rock induced by ScCO2-brine exposure.
61. A Cone-Penetration-Test Inversion Model Developed via Machine Learning of Physical and Virtual Calibration-Chamber Experiments
Core Problem: CPT-based soil characterization needs inversion models that bridge laboratory calibration and virtual numerical experiments.
Key Innovation: The study develops a machine-learning CPT inversion model trained on physical and virtual calibration-chamber experiments.
62. Laser-assisted TBM disc cutter rock-breaking: Insights from grain model and thermomechanical damage coupling
Core Problem: Hard-rock TBM efficiency can be limited by high cutter forces and insufficient understanding of thermally assisted damage.
Key Innovation: The paper studies laser-assisted TBM disc-cutter rock breaking using a grain model with thermomechanical damage coupling.
63. An integrated LiDAR-Inertial-Visual framework for tunnel digital twins under construction: 3D reconstruction, centerline sampling, and point cloud semantic segmentation
Core Problem: Tunnel digital twins require robust reconstruction and semantic segmentation under construction conditions.
Key Innovation: The paper integrates LiDAR, inertial, and visual sensing for tunnel 3-D reconstruction, centerline sampling, and point-cloud semantic segmentation.
64. Investigation of the contact pressure distribution between TBM disc cutter and rock surface using the 3D discrete element method
Core Problem: Disc-cutter performance depends on the evolving contact-pressure distribution at the cutter-rock interface.
Key Innovation: The study uses 3-D discrete-element modelling to investigate contact pressure between TBM disc cutters and rock surfaces.
65. Effects of spatial variations in physical properties of reclaimed ground consisting of rock debris on seismic behavior of embankment
Core Problem: Reclaimed rock-debris embankments can respond unevenly to seismic loading because physical properties vary spatially.
Key Innovation: The paper evaluates how spatial variations in reclaimed-ground properties affect seismic behaviour of embankments.
66. Weakening Behavior of Sandstone Subjected to Microwave Irradiation: Insights from Real-Time Monitoring of Surface Temperature and Acoustic Emission
Core Problem: Microwave preconditioning can weaken rock for excavation, but damage evolution must be tracked in real time.
Key Innovation: The study monitors surface temperature and acoustic emission to characterize sandstone weakening under microwave irradiation.
67. Real-time monitoring of micro-deformation and fluid distribution during displacement in reservoirs using distributed fiber optic sensing and X-ray CT
Core Problem: Reservoir displacement processes involve coupled micro-deformation and fluid redistribution that are hard to observe directly.
Key Innovation: The paper combines distributed fiber-optic sensing and X-ray CT for real-time monitoring of micro-deformation and fluid distribution.
68. Soil stabilization using biological CO2 sequestration and CO2-based biocementation technique
Core Problem: Ground improvement methods need to improve soil strength while reducing environmental footprint.
Key Innovation: The paper studies soil stabilization using biological CO2 sequestration and CO2-based biocementation.
69. Coarse-to-Fine Domain Incremental Learning with Attentive Distillation for Mining Footprint Segmentation in Multispectral Imagery
Core Problem: Mining footprint segmentation must adapt to new domains without forgetting earlier terrain and sensor conditions.
Key Innovation: The paper uses coarse-to-fine domain incremental learning with attentive distillation for mining-footprint segmentation in multispectral imagery.
70. A Multi-Agent Feedback System for Detecting and Describing News Events in Satellite Imagery
Core Problem: Satellite imagery can show hazardous events, but turning visual evidence into reliable event descriptions remains difficult.
Key Innovation: The paper proposes a multi-agent feedback system for detecting and describing news events in satellite imagery.