This study integrates a dynamic plant growth model with a three-dimensional (3D) radiative transfer model (RTM) for maize traits retrieval using high spatial-spectral resolution airborne data. The research combines the Discrete Anisotropic Radiative Transfer (DART) model with the Dynamic L-System-based Architectural maize (DLAmaize) growth model to simulate field reflectance. Comparison with the 1D RTM SAIL revealed limitations in representing row structure effects, field slope, and complex light-canopy interactions. Novel Global Sensitivity Analyses (GSA) were carried out using dependence-based methods to overcome limitations traditional variance-based approaches, enabling better characterization of hyperspectral sensitivity to changes in leaf biochemistry, canopy architecture, and soil moisture. GSA provided complementary results to assess estimation uncertainties of the proposed traits retrieval method across growth stages. A hybrid inversion framework combining DART simulations with an active learning strategy using Kernel Ridge Regression was implemented for traits estimation. The approach was validated using ground data and HyPlant-DUAL airborne hyperspectral images from two field campaigns in 2018 and achieved high retrieval accuracy of key maize traits: leaf area index (LAI, R2=0.91, RMSE=0.42 m2/m2), leaf chlorophyll content (LCC, R2=0.61, RMSE=3.89 mu g/cm2), leaf nitrogen content (LNC, R2=0.86, RMSE=1.13 x 10-2 mg/cm2), leaf dry matter content (LMA, R2=0.84, RMSE=0.15 mg/cm2), and leaf water content (LWC, R2=0.78, RMSE=0.88 mg/cm2). The validated models were used to generate two-date 10 m resolution maps, showing good spatial consistency and traits dynamics. The findings demonstrate that integrating 3D RTMs with dynamic growth models is suited for maize trait mapping from hyperspectral data in varying growing conditions.

The horizontal displacement of monopile under cyclic loading is subject to uncertainty due to variations in metocean conditions and soil parameters at offshore wind farms. However, the current design for cyclically loaded monopiles relies on the p-y method recommended by API and DNV, which does not accurately capture the horizontal displacement of the monopiles. In this study, finite element simulations are performed using ABAQUS, where the soil is modeled with the Einav-Randolph model to account for soil softening effects. The impact of parameter uncertainties, such as soil stiffness, undrained shear strength, and the pile-soil friction coefficient, on the reliability index of the monopile's horizontal displacement for different length diameter (L/D) ratios is investigated. A case study is provided to assess the horizontal displacement reliability of a monopile under cyclic loading. The results show that the horizontal displacement reliability index decreases as the coefficient of variation (COV) of the random variables, the correlation coefficient, and the monopile's L/D ratio increase. Conversely, the reliability index increases with an increase in the allowable horizontal displacement. The horizontal displacement reliability index is most sensitive to soil stiffness, followed by undrained shear strength and pile-soil friction coefficient. The findings of this study offer valuable insights into how parameter uncertainties influence the horizontal displacement of monopiles under cyclic loading.

The generation of excess pore water pressure (EPWP) and liquefaction characteristic of soils under seismic loading have long been topics of interest and ongoing discussion. Based on the structural state exhibited in the liquefaction process, the mechanical property of saturated coral sand is divided into solid, pseudo-fluid, and liquid phases. New indices, zeta q (generalized deviator strain evolution) and zeta(y)q (generalized deviator strain evolution rate), are proposed to evaluate the evolution and evolution rate of complex deformation. In the solid phase, the saturated coral sand primarily exhibits the properties of a continuous solid medium, the peak EPWP ratio (rup) shows a power correlation with generalized deviator strain evolution amplitude (zeta qa). While in the pseudo-fluid phase, the saturated coral sand primarily exhibits mechanical behavior characteristic similar to that of a fluid, and the rup shows a significant arctangent function relationship with generalized deviator strain evolution rate amplitude (zeta(y)qa). The correlation of rup with zeta qa and zeta' qaduring liquefaction is significantly affected by loading conditions (cyclic stress ratio, CSR, loading direction angle, alpha sigma, and loading frequency, f). To quantify the impact of these loading conditions on the generation of rup in different phases, unified indicators delta S (for the solid phase) and delta L (for the pseudo-fluid phase) are defined. Eventually, An EPWP model based on mechanical property exhibited in different phases is developed, which has normalized the effects of loading conditions. It provides a comprehensive framework to predict the rup of saturated coral sand under complex geological activities, and this model facilitates the understanding and simulation of the mechanical properties and behavior of saturated coral sand during the liquefaction process.

Understanding slope stability is crucial for effective risk management and prevention of slides. Some deterministic approaches based on limit-equilibrium and numerical methods have been proposed for the assessment of the safety factor (SF) for a given soil slope. However, for risk analyses of slides of earth dams, a range of SFs is required due to uncertainties associated with soil strength properties as well as slope geometry. Recently, several studies have demonstrated the efficiency of artificial neural network (ANN) models in predicting the SF of natural and artificial slopes. Nevertheless, such techniques operate as black-box models, prioritizing predictive accuracy without suitable interpretability. Alternatively, multivariate polynomial regression (MVR) models offer a pragmatic interpretability strategy by combining the analysis of variance with a response surface methodology. This approach overcomes the difficulties associated with the interpretability of the black-box models, but results in limited accuracy when the relationship between independent and dependent variables is highly nonlinear. In this study, two models for a quick assessment of slope SF in earth dams are proposed considering the MVR and the ANN models. Initially, a synthetic dataset was generated considering different soil properties and slope geometries. Then, both models were evaluated and compared using unseen data. The results are also discussed from a geotechnical point of view, showing the impact of each input parameter on the assessment of the SF. Finally, the accuracy of both models was measured and compared using a real-case database. The obtained accuracy was 78% for the ANN model and 72% for the MVR one, demonstrating a great performance for both proposed models. The efficacy of the ANN model was also observed through its capacity to reduce false negatives (a stable prediction when it is not), resulting in a model more favorable to safety assessment.

Climate change has led to increased frequency, duration, and severity of meteorological drought (MD) events worldwide, causing significant and irreversible damage to terrestrial ecosystems. Understanding the impact of MD on diverse vegetation types is essential for ecological security and restoration. This study investigated vegetation responses to MD through a drought propagation framework, focusing on the Yangtze River Basin in China, which has been stricken by drought frequently in recent decades. By analyzing propagation characteristics, we assessed the sensitivity and vulnerability of different vegetation types to drought. Using Copula modeling, the occurrence probability of vegetation loss (VL) under varying MD conditions was estimated. Key findings include: (1) The majority of the Yangtze River Basin showed a high rate of MD to VL propagation. (2) Different vegetation types exhibited varied responses: woodlands had relatively low sensitivity and vulnerability, grasslands showed medium sensitivity with high vulnerability, while croplands demonstrated high sensitivity and moderate vulnerability. (3) The risk of extreme VL increased sharply with rising MD intensity. This framework and its findings could provide valuable insights for understanding vegetation responses to drought and inform strategies for managing vegetation loss.

As an important coastal protective structure, the breakwater is prone to failure due to foundation damage under seismic actions. However, the seismic performance evaluation of breakwaters has received little attention. This study conducts a seismic fragility analysis of composite breakwaters constructed on liquefiable foundations. By adopting a performance-based seismic design (PBSD) approach and considering the record-to-record (RTR) variability of ground motions, the seismic performance of the breakwaters is assessed over their entire lifecycle. Based on the results of the parameter sensitivity analysis, the reinforcement schemes were proposed in terms of delaying foundation liquefaction and limiting the lateral displacement of liquefied soil. The results of the seismic intensity measure (IM) parameter selection indicate that the commonly used peak ground acceleration (PGA) exhibits a weak correlation with the seismic response of the breakwater, whereas the cumulative absolute velocity (CAV) has a strong correlation. The comparison of the reinforcement schemes shows that the Dense Sand Column (DC) scheme provides significant reinforcement effects, while the Concrete Sheet Pile (CSP) scheme is more suitable for reinforcing existing breakwaters. The seismic performance assessment framework can also be applied to other structures where structural damage is closely related to foundation deformation, such as caisson quays and embankments.

During tunnel excavation in a soft soil stratum, a transparent model test can present the whole failure process, and a similar transparent material with stable physical and mechanical properties is essential for obtaining valid experimental results. Therefore, a new type of similar transparent material was developed in which fused quartz sand served as the coarse aggregate, nanoscale hydrophobic fumed silica powder acted as the binder, and a mixture of n-dodecane and 15# white oil was used as the pore fluid. The key parameters of the developed similar transparent material, including unit weight, internal friction angle, cohesion, and compression modulus, were evaluated. Furthermore, the consistency between the similar transparent material and natural soft soil was verified in three aspects, namely, physical properties, compressive strength characteristics, and shear properties. Finally, appropriate adjustment measures were proposed based on the results of the analysis of variance (ANOVA) and the analysis of range (ANOR) to meet the similarity requirements of parameters under different engineering conditions.

The raw-material mix ratio and preparation of similar materials are crucial for the success of physical model tests and for accurately reflecting prototype properties. In this study, an optimum similar material for plateau alluvial and lacustrine (PAL) round gravel was developed based on similarity theory. The similar materials were subjected to sensitivity factor analysis and microscopic analysis. Subsequently, the optimum similar material was applied to a three-dimensional (3D) physical model test of an ultradeep foundation pit (FP). The findings show that the similar material prepared with gypsum, LD, bentonite, water, barite powder, and DS at a ratio of 1:1:1.4:3.5:8.8:13.2 was the best for a 3D physical model test of the ultradeep FP in PAL round gravel strata. The sensitivity-factor analysis revealed that barite powder had the greatest impact on gamma, that c and phi were primarily affected by bentonite, and that the LD-gypsum ratio controlled E. A nonuniform particle-size distribution as well as the presence of edge-to-face contacts and small pores between particles constituted the microphysical factors affecting the mechanical properties of the optimum similar material. Using dolomite with a Mohs hardness of 3.5-4 instead of traditional quartz sand with a Mohs hardness of 7 as the raw material can produce a similar material for the target soil with mechanical parameters closer to those of the ideal similar material. The application of the optimum similar material in physical model tests has revealed the stress field response law of ultra deep foundation pit excavation. This study could provide reference and inspiration for the development of similar materials in gravel formations with weaker mechanical properties.

Field capacity (F.C.) is a crucial parameter in soil analysis, defining the limits of plant-available moisture content (M.C.). Integrating this concept into sensing technology provides valuable information for optimizing irrigation scheduling by determining the appropriate timing and quantity of irrigation, thereby preventing crop damage. This article presents a fractal-based microwave planar sensor (MPS) designed to estimate soil-moisture characteristics related to F.C. The proposed sensor utilizes a self-similar fractal (SSF) approach, operating in the ISM frequency band at 2.4 GHz, achieving high return losses of approximately -47.94 dB and enhanced sensitivity in material characterization. The sensor's performance is evaluated by varying F.C. values from 0% to 100% for similar textured soils with organic matter content (OMC) variations. The results demonstrate that variations in OMC significantly impact the dielectric properties of soil with moisture variations. Specifically, Sample-1, which has a low OMC, exhibits a lower epsilon(r) values than Sample-2 at all F.C. levels. The data suggest that the proposed sensor is sensitive to detect the impact of OMC variations on soil-moisture characteristics concerning F.C. A mathematical model has been formulated as a second-order polynomial equation, exhibiting coefficient of determination (R-2) value of 0.9771. This model has been developed specifically to evaluate F.C. values, demonstrating a strong correlation with the observed data. The performance of the proposed sensor confirms its potential application in agricultural fields for efficient irrigation scheduling and water resource conservation.

Predicting slope movement has become a great challenge, especially in the Himalayan region, as such natural hazards cause great damage. Machine Learning (ML) models can help in the prediction of landslide hazards. Despite the capabilities of ML models in predicting landslide hazards, most existing approaches are deficient in capturing changes in weather conditions at day, hour, or minute scales, thus affecting their accuracy in real-time scenarios. These models also generally have difficulties in generalizing predictions due to limited data availability, and they cannot frequently provide multi-step ahead predictions that are crucial for effective disaster preparedness and timely response. We introduced the hierarchical architecture ML model, specifically the hierarchical transformer prediction autoencoder (H-TPA), which is capable of predicting slope movement with high temporal resolution and enhanced generalization capabilities. This study was based on a rich dataset from sixty-four landslide locations over five years. In this work, we utilize 1,066,009 samples for the training set, which were balanced down to 23,328 samples in order to address class imbalance. The validation set contained 100,000 samples, while the test set was made up of 164,082 samples. This work also presents a VSA methodology for determining threshold values of environmental attributes that trigger slope movements. The performance evaluation of the H-TPA model using this dataset demonstrates very good performance with an F1 score of 0.889, 0.760, and 0.746 for the training, validation, and test datasets, respectively, in predicting slope movements 10 min in advance. Moreover, the present study focused on the analyses of weather condition factors and soil moisture affecting the landslide triggers, which indicated the role of temperature, humidity, barometric pressure, rainfall, and sunlight intensity in small or large slope movements according to certain threshold values. This study generally contributes to the present understanding and enhances the knowledge of landslide prediction in the Himalayan region, besides providing recommendations for geo-scientific knowledge improvement and mitigation strategies.