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.

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.

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.

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.

Soil salinization in arid and coastal areas poses a significant threat to crop production, which is further aggravated by climate change and the over-exploitation of aquifers. Cultivation of salt and drought-tolerant crops such as quinoa represents a promising adaptation pathway for agriculture in saline soils. Quinoa (Chenopodium quinoa Willd.) is a salt-loving plant, known for its tolerance to drought and salinity using complex stress responses. However, available models of quinoa growth are limited, particularly under salinity stress. The objective of this study was to calibrate the crop growth, and salinity and drought stress parameters of the SWAP - WOFOST model and evaluate whether this model can represent quinoa's stress tolerance mechanisms. Field experimental data were used from two quinoa varieties: ICBA-Q5 grown under saline conditions in Laayoune, Morocco, in 2021, and Bastille grown under rainfed, non-saline conditions in Merelbeke, Belgium, from 2018 to 2023. Calibration and parameter uncertainty was performed using the DiffeRential Evolution Adaptive Metropolis (DREAMzs) algorithm on key parameters identified via sensitivity analysis using the Morris method. The resulting crop parameters provide insights into the stress tolerance mechanisms of quinoa, including reduction of transpiration and uptake of solutes. The salinity stress function of SWAP effectively represents these tolerance mechanisms and accurately predicts the impact on yield, under arid conditions. Under Northwestern European climate, the model replicates the impact of drought stress on yield. The calibrated model offers perspectives for evaluating practices to reduce soil salinization in arid conditions and for modeling crop performance under water-limited conditions or future salinization in temperate regions.

Numerical modeling of permafrost dynamics requires adequate representation of atmospheric and surface processes, a reasonable parameter estimation strategy, and site-specific model development. The three main research objectives of the study are: (i) to propose a novel methodology that determines the required level of surface process complexity of permafrost models by conducting parameter sensitivity and calibration, (ii) to design and compare three numerical models of increasing surface process complexity, and (iii) to calibrate and validate the numerical models at the Yakou catchment on the Qinghai-Tibet Plateau as an exemplary study site. The calibration was carried out by coupling the Advanced Terrestrial Simulator (numerical model) and PEST (calibration tool). Simulation results showed that (i) A simple numerical model that considers only subsurface processes can simulate active layer development with the same accuracy as other more complex models that include surface processes. (ii) Peat and mineral soil layer permeability, Van Genuchten alpha, and porosity are highly sensitive. (iii) Liquid precipitation aids in increasing the rate of permafrost degradation. (iv) Deposition of snow insulated the subsurface during the thaw initiation period. We have developed and released an integrated code that couples the numerical software ATS to the calibration software PEST. The numerical model can be further used to determine the impacts of climate change on permafrost degradation.

Bio-inspired probes have emerged as a promising solution for in-situ site characterisation, particularly in challenging terrains and extraterrestrial exploration. This study presents a viable and computationally efficient Material Point Method (MPM) framework for studying Bio-Inspired Cone Pressuremeter (BICP) probe mechanism. With its inherent advantage of particle and continuum frameworks, MPM allows seamless simulation of multi-staged BICP probe propulsion that involves large deformation. A novel implementation strategy was developed for this study to simulate the complex movement of the BICP probe in three sequential stages, including penetration, pressuremeter module expansion, and tip advancement. Sensitivity analysis was conducted to achieve an objective solution and determine the optimum mesh size and mass scaling factor for the BICP probe within the realms of current state-of-the-art MPM formulation. Furthermore, investigations were performed on the established MPM framework to study the influence of probe geometry, material state, and layered soil strata. The findings reveal that in probes with longer pressuremeter modules, larger zone of stress relaxation was observed around the cone tip during module expansion stage than their shorter or double-module counterparts. Meanwhile, the BICP probe's response during all stages in different material states corroborates its sensitivity to the soil's mechanical properties. Although the layered strata significantly influenced the BICP probe's response during the penetration and module expansion stages, it had minimal impact during the tip advancement stage.

In deep foundation pit engineering, the soil undergoes a complex stress path, encompassing both loading and unloading phases. The Shanghai model, an advanced constitutive model, effectively accounts for the soil's deformation characteristics under these varied stress paths, which is essential for accurately predicting the horizontal displacement and surface settlement of the foundation pit's enclosure structure. This model comprises eight material parameters, three initial state parameters, and one small-strain parameter. Despite its sophistication, there is a scarcity of numerical studies exploring the correlation between these parameters and the deformation patterns in foundation pit engineering. This paper initially establishes the superiority of the Shanghai model in ultra-deep circular vertical shaft foundation pit engineering by examining a case study of a nursery circular ultra-deep vertical shaft foundation pit, which is part of the Suzhou River section's deep drainage and storage pipeline system pilot project in Shanghai. Subsequently, utilizing an idealized foundation pit engineering model, a comprehensive sensitivity analysis of the Shanghai model's multi-parameter values across their full range was performed using orthogonal experiments. The findings revealed that the parameter most sensitive to the lateral displacement of the underground continuous wall was kappa, with an increase in kappa leading to a corresponding increase in displacement. Similarly, the parameter most sensitive to surface subsidence outside the pit was lambda, with an increase in lambda resulting in greater subsidence. Lastly, the parameter most sensitive to soil uplift at the bottom of the pit was also kappa, with an increase in kappa leading to more significant uplift.