Liquefaction hazard analysis is crucial in earthquake-prone regions as it magnifies structural damage. In this study, standard penetration test (SPT) and shear wave velocity (Vs) data of Chittagong City have been used to assess the liquefaction resistance of soils using artificial neural network (ANN). For a scenario of 7.5 magnitude (Mw) earthquake in Chittagong City, estimating the liquefaction-resistance involves utilizing peak horizontal ground acceleration (PGA) values of 0.15 and 0.28 g. Then, liquefaction potential index (LPI) is determined to assess the severity of liquefaction. In most boreholes, the LPI values are generally higher, with slightly elevated values in SPT data compared to Vs data. The current study suggests that the Valley Alluvium, Beach and Dune Sand may experience extreme liquefaction with LPI values ranges from 9.55 to 55.03 and 0 to 37.17 for SPT and Vs respectively, under a PGA of 0.15 g. Furthermore, LPI values ranges from 25.55 to 71.45 and 9.55 to 54.39 for SPT and Vs correspondingly. The liquefaction hazard map can be utilized to protect public safety, infrastructure, and to create a more resilient Chittagong City.

Correlations between the mechanical properties and surface scratch resistance of polylactic acid (PLA) are investigated via tensile and scratch tests on samples after degradation in soil for various times. The results show that the tensile yield strength of PLA is inversely proportional to the natural logarithm of the degradation time, and the scratch resistance and fracture toughness of PLA and the temperature rise near the indenter all increase and then decrease. The surface crystallinity of PLA also increases and then decreases, indicating that it and the scratch resistance are closely related. These findings provide useful information about how PLA behaves under degradation conditions. (c) 2025 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC) license (https://creativecommons.org/licenses/by-nc/4.0/).

The soil strength of soft clay is influenced by strain rate effect. Models considering strain rate effect always ignore the impact of loading rate on pore pressure and have poor applicability to 3D engineering problems. Based on the classic inelastic core boundary surface model, a logarithmic rate function representing the strain rate effect of soft soil was introduced to the hardening law. A new parameter H was added to adjust the plastic modulus while another new parameter mu is introduced to account for the strain rate effect. A rate-effect boundary surface constitutive model suitable for saturated clay was subsequently proposed. Combined with the implicit integral numerical algorithm and stress-permeability coupling analysis, the innovative model was implemented in the finite element software and validated by comparing with the results of triaxial tests. By analysing the rate-effect of 11 types of soft soil, a formula to calculate the rate parameter was derived. The developed model was used to calculate the undrained vertical bearing capacity and sliding resistance of a movable subsea mudmat. The mudmat frictional coefficient from soil undrained to partial drained and finally undrained state was obtained and compared with those from the Modified Cam-Clay model. Identical results were obtained without considering the rate effect. When considering the strain rate effect on the improvement of soil strength, the friction resistance coefficient initially decreases and then increases with the decrease of the sliding speed, eventually stabilising after reaching the limit value. The rate-effect on the friction resistance coefficient is most prominent under undrained conditions with high sliding speeds. The soil strain rate effect is suggested to be considered in the design of the subsea mudmat avoid underestimating the friction resistance.

Ensuring the accuracy of free-field inversion is crucial in determining seismic excitation for soil-structure interaction (SSI) systems. Due to the spherical and cylindrical diffusion properties of body waves and surface waves, the near-fault zone presents distinct free-field responses compared to the far-fault zone. Consequently, existing far-fault free-field inversion techniques are insufficient for providing accurate seismic excitation for SSI systems within the near-fault zone. To address this limitation, a tailored near-fault free-field inversion method based on a multi-objective optimization algorithm is proposed in this study. The proposed method establishes an inversion framework for both spherical body waves and cylindrical surface waves and then transforms the overdetermined problem in inversion process into an optimization problem. Within the multi-objective optimization model, objective functions are formulated by minimizing the three-component waveform differences between the observation point and the delayed reference point. Additionally, constraint conditions are determined based on the attenuation property of propagating seismic waves. The accuracy of the proposed method is then verified through near-fault wave motion characteristics and validated against real downhole recordings. Finally, the application of the proposed method is investigated, with emphasis on examining the impulsive property of underground motions and analyzing the seismic responses of SSI systems. The results show that the proposed method refines the theoretical framework of near-fault inversion and accurately restores the free-field characteristics, particularly the impulsive features of near-fault motions, thereby providing reliable excitation for seismic response assessments of SSI systems.

The cyclic behavior of clay significantly influences the dynamic response of offshore wind turbines (OWTs). This study presents a practical bounding surface model capable of describing both cyclic shakedown and cyclic degradation. The model is characterized by a simple theoretical framework and a limited number of parameters, and it has been numerically implemented in ABAQUS through a user-defined material (UMAT) subroutine. The yield surface remains fixed at the origin with isotropic hardening, while a movable projection center is introduced to capture cyclic hysteresis behavior. Cumulative plastic deviatoric strain is integrated into the plastic modulus to represent cyclic accumulation. Validation against undrained cyclic tests on three types of clay demonstrates its capability in reproducing stress-strain hysteresis, cyclic shakedown, and cyclic degradation. Additionally, its effectiveness in solving finite element boundary value problems is verified through centrifuge tests on large-diameter monopiles. Furthermore, the model is adopted to analyze the dynamic response of monopile OWTs under seismic loading. The results indicate that, compared to cyclic shakedown, cyclic degradation leads to a progressive reduction in soil stiffness, which diminishes acceleration amplification, increases settlement accumulation, and results in higher residual excess pore pressure with greater fluctuation. Despite its advantages, this model requires a priori specification of the sign of the plastic modulus parameter cd to capture either cyclic degradation or shakedown behavior. Furthermore, under undrained conditions, the model leads pstabilization of the effective stress path, which subsequently results in underestimation of the excess pore pressure.

Surface soil moisture (SSM) is a key limiting factor for vegetation growth in alpine meadow on the Qinghai-Tibetan Plateau (QTP). Patches with various sizes and types may cause the redistribution of SSM by changing soil hydrological processes, and then trigger or accelerate alpine grassland degradation. Therefore, it is vital to understand the effects of patchiness on SSM at multi-scales to provide a reference for alpine grassland restoration. However, there is a lack of direct observational evidence concerning the role of the size and type of patches on SSM, and little is known about the effects of patches pattern on SSM at plot scale. Here, we first measured SSM of typical patches with different sizes and types at patch scale and investigated their patterns and SSM spatial distribution through unmanned aerial vehicle (UAV)-mounted multi-type cameras at plot scale. We then analyzed the role of the size and type of patchiness on SSM at both patch and plot scales. Results showed that: (1) in situ measured SSM of typical patches was significantly different (P < 0.01), original vegetation patch (OV) had the highest SSM, followed by isolate vegetation patch (IV), small bare patch (SP), medium bare patch (MP) and large bare patch (LP); (2) the proposed method based on UAV images was able to estimate SSM (0-40 cm) with a satisfactory accuracy (R-2 = 0.89, P < 0.001); (3) all landscape indices of OV, with the exception of patch density, were positively correlated with SSM at plot scale, while most of the landscape indices of LP and IV showed negative correlations (P < 0.05). Our results indicated that patchiness intensified the spatial heterogeneity of SSM and potentially accelerated the alpine meadow degradation. Preventing the development of OV into IV and the expansion of LP is a critical task for alpine meadow management and restoration.

Land surface temperature (LST) plays an important role in Earth energy balance and water/carbon cycle processes and is recognized as an Essential Climate Variable (ECV) and an Essential Agricultural Variable (EAV). LST products that are issued from satellite observations mostly depict landscape-scale temperature due to their generally large footprint. This means that a pixel-based temperature integrates over various components, whereas temperature individual components are better suited for the purpose of evapotranspiration estimation, crop growth assessment, drought monitoring, etc. Thus, disentangling soil and vegetation temperatures is a real matter of concern. Moreover, most satellite-based LSTs are contaminated by directional effects due to the inherent anisotropy properties of most terrestrial targets. The characteristics of directional effects are closely linked to the properties of the target and controlled by the view and solar geometry. A singular angular signature is obtained in the hotspot geometry, i.e., when the sun, the satellite and the target are aligned. The hotspot phenomenon highlights the temperature differences between sunlit and shaded areas. However, due to the lack of adequate multi-angle observations and inaccurate portrayal or neglect of solar influence, the hotspot effect is often overlooked and has become a barrier for better inversion results at satellite scale. Therefore, hotspot effect needs to be better characterized, which here is achieved with a three-component model that distinguishes vegetation, sunlit and shaded soil temperature components and accounts for vegetation structure. Our work combines thermal infrared (TIR) observations from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the LEO (Low Earth Orbit) Sentinel-3, and two sensors onboard GEO (geostationary) satellites, i.e. the Advanced Himawari Imager (AHI) and Spinning Enhanced Visible and Infrared Imager (SEVIRI). Based on inversion with a Bayesian method and prior information associated with component temperature differences as constrained, the findings include: 1) Satellite observations throughout East Asia around noon indicate that for every 10 degrees change in angular distance from the sun, LST will on average vary by 0.6 K; 2) As a better constraint, the hotspot effect can benefit from multi-angle TIR observations to improve the retrieval of LST components, thereby reducing the root mean squared error (RMSE) from approximately 3.5 K, 5.8 K, and 4.1 K to 2.8 K, 3.5 K, and 3.1 K, at DM, EVO and KAL sites, respectively; 3) Based on a dataset simulated with a threedimensional radiative transfer model, a significant inversion error may result if the hotspot is ignored for an angular distance between the viewing and solar directions that is smaller than 30 degrees. Overall, considering the hotspot effect has the potential to reduce inversion noise and to separate the temperature difference between sunlit and shaded areas in a pixel, paving the way for producing stable temperature component products.

The Land Surface Temperature (LST) is well suited to monitor biosphere-atmosphere interactions in forests, as it depends on water availability and atmospheric/meteorological conditions above and below the canopy. Satellite-based LST has proven integral in observing evapotranspiration, estimating surface heat fluxes and characterising vegetation properties. Since the radiative regime of forests is complex, driven by canopy structure, components radiation properties and their arrangement, forest radiative temperatures are subject to strong angular effects. However, this depends on the scale of observation, where scattering mechanisms from canopy-to satellite-scales influence anisotropy with varying orders of magnitude. Given the heterogeneous and complex nature of forests, multi-angular data collection is particularly difficult, necessitating instrumentation distant enough from the canopy to obtain significant canopy brightness temperature and concurrent observations to exclude turbulence/atmospheric effects. Accordingly, current research and understanding on forest anisotropy at varying scales (from local validation level to satellite footprint) remain insufficient to provide practical solutions for addressing angular effects for upcoming thermal satellite sensors and associated validation schemes. This study presents a novel method founded in the optical remote sensing domain to explore the use of microcanopies that represent forests at different scales in the footprint of a multi-angular goniometer observing system. Both Geometric Optical (GO) and volumetric scattering dominated canopies are constructed to simulate impacts of anisotropy in heterogeneous and homogeneous canopies, and observed using a thermal infrared radiometer. Results show that heterogeneous canopies dominated by GO scattering are subject to much higher magnitudes of anisotropy, reaching maximum temperature differences of 3 degrees C off-nadir. Magnitudes of anisotropy are higher in sparse forests, where the gap fraction and crown arrangement (inducing sunlit/shaded portions of soil and vegetation) drive larger off-nadir differences. In dense forests, anisotropy is driven by viewing the maximum portion of sunlit vegetation (hotspot), where the soil is mostly obscured. Canopy structural metrics such as the fractional cover and gap fraction were found to have significant correlation with off-nadir differences. In more homogeneous canopies, anisotropy reaches a lower magnitude with temperature differences up to 1 degrees C, driven largely by volumetric scattering and components radiation properties. Optimal placement of instrumentation at the canopy-scale (more heterogeneous behaviour due to proximity to the canopy and small pixel size) used to validate satellite observations (more homogeneous behaviour due to larger pixel size) was found to be in cases of viewing maximum sunlit vegetation, for dense canopies. Given upcoming high spatial resolution sensors and associated validation schemes needed to benchmark LST and downstream products such as evapotranspiration, a better understanding of anisotropy over forests is critical to provide accurate, long-term and multi-sensor products.

Satellite-derived land surface temperature (LST) is a directional variable and has significant angular anisotropy. This characteristic contributes to enhance the differences among different satellite-derived LST products, and therefore increases the challenge of using multi-sensor and multi-decadal data to provide a long-term and angleconsistent LST climate data record. The kernel-driven model can balance the interpretability and operability well, so that it is suitable for angular normalization of LST products. The calibration of the kernel-driven model depends on multi-angle data which is difficult to obtain due to the spatial-temporal heterogeneity of LST. In this study, a novel LST angular normalization method based on the kernel-driven model was proposed to correct the angular effect of satellite-derived LST product by constructing multi-angle LST dataset from one geostationary satellite (GOES-R/ABI) and four polar-orbiting satellites (Terra/MODIS, Aqua/MODIS, Metop/AVHRR, and SNPP/VIIRS). The dataset gathered more abundant angle information, i.e., LSTs from three different observation geometries for the same pixel. The kernel-driven model was calibrated using the multi-angle LST dataset in the Continental United States (CONUS) during the year 2020. The discrepancies of the root mean square difference between LST before and after angular normalization range from 0.14 K to 1.10 K over nine land cover types in the four seasons. Similar results are obtained when the calibrated kernel-driven model was further expanded to other years and areas (i.e., the CONUS in 2021 and East Asia in 2020). The LST angular normalization method was applied to correct the angular effect of MODIS LST product. The results indicate that there is a strong correlation between the spatial distribution of LST differences (LST before and after angular normalization) and view zenith angle (VZA). MODIS LSTs before and after angular normalization were compared with Landsat 8 LST and Sentinel-3 A LST in near-nadir viewing for January, April, July, and October 2020. The angular normalization reduced the root mean square error (RMSE) between MODIS LST and Landsat 8 LST by 0.94-2.06 K in different months and by 0.13-2.61 K over various land cover types. For Sentinel-3 A, the RMSE decreased by 0.30-0.64 K in different months. The accuracies of MODIS LST before and after angular normalization were further validated using in situ measurements at the six SURFRAD sites. There are large discrepancies between the RMSE of MODIS LST before and after angular normalization versus in situ LST for VZA >= 45 degrees. The largest discrepancy is up to approximately 1.3 K at the GWN site. The LST angular normalization method has the potential to provide an angle-consistent LST climate data record.

Soil-rock mixtures (SRMs) are characterized by heterogeneous structural features that lead to multiscale mechanical evolution under varying cementation conditions. However, the shear failure mechanisms of cemented SRMs (CSRMs) remain insufficiently explored in existing studies. In this work, a heterogeneous threedimensional (3D) discrete element model (DEM) was developed for CSRMs, with parameters meticulously calibrated to examine the role of matrix-block interfaces under different volumetric block proportions (VBPs). At the macroscopic scale, significant influences of the interface state on the peak strength of CSRMs were observed, whereas the residual strength was found to be largely insensitive to the interface cementation properties. Pronounced dilatancy behaviour was identified in the postpeak and residual phases, with a positive correlation with both interface cementation and VBP. Quantitative particle-scale analyses revealed substantial heterogeneity and anisotropy in the contact force network of CSRMs across different components. A highly welded interface was shown to reduce the number of interface cracks at the peak strength state while increasing the proportion of tensile cracks within the interface zone. Furthermore, the welding degree of the interface was found to govern the formation and morphology of shear cracking surfaces at the peak strength state. Nevertheless, a reconstruction method for the shear slip surface was proposed to demonstrate that, at the same VBP, the primary roughness of the slip surfaces remained consistent and was independent of the interface properties. Based on the extended simulations, the peak strength of the weakly welded CSRMs progressively decreased with increasing VBP, whereas further exploration of the enhanced residual strength is needed.