Flash floods are often responsible for deaths and damage to infrastructure. The objective of this work is to create a data-driven model to understand how predisposing factors influence the spatial variation of the triggering factor (rainfall intensity) in the case of flash floods in the continental area of Portugal. Flash floods occurrences were extracted from the DISASTER database. We extracted the accumulated precipitation from the Copernicus database by considering two days of duration. The analysed predisposing factors for flooding were extracted considering the whole basin where each occurrence is located. These factors include the basin area, the predominant lithology, drainage density, and the mean or median values of elevation, slope, stream power index (SPI), topographic wetness index (TWI), roughness, and four soil properties. The Random Forest algorithm was used to build the models and obtained mean absolute percentage error (MAPE) around 19%, an acceptable value for the objectives of the work. The median of SPI, mean elevation and the area of the basin are the top three most relevant predisposing factors interpreted by the model for defining the rainfall input for flash flooding in mainland Portugal.

Canopy reflectance (CR) models describe the transfer and interaction of radiation from the soil background to the canopy layer and play a vital role in the retrieval of biophysical variables. However, few efforts have focused on estimating soil background scattering operators, resulting in uncertainties in CR modelling, especially over sloping terrain. This study developed a canopy reflectance model for simulating CR over sloping terrain, which combines the general spectral vector (GSV) model, the PROSPECT model, and 4SAIL model coupled with topography (GSV-PROSAILT). The canopy reflectance simulated by GSV-PROSAILT was validated against two datasets: discrete anisotropic radiative transfer (DART) simulations and remote sensing observations. A comparison with DART simulations under various conditions revealed that the GSV-PROSAILT model captures terrain-induced CR distortion with high accuracy (red band: coefficient of determination $\lpar {\rm R 2} \rpar = 0.731$(R2)=0.731, root-mean-square error (RMSE) = 0.007; near infrared (NIR) band: $\rm R2 = 0.8319$R2=0.8319, RMSE = 0.0098). The results of remote sensing observation verification revealed that the GSV-PROSAILT model can be successfully used in CR modelling. These validations confirmed the performance of GSV-PROSAILT in soil and canopy reflectance modelling over sloping terrain, indicating that it can provide a potential tool for biophysical variable retrieval over mountainous areas.

The foundation soil below the structure usually bears the combined action of initial static and cyclic shear loading. This experimental investigation focused on the cyclic properties of saturated soft clay in the initial static shear stress state. A range of constant volume cyclic simple shear tests were performed on Shanghai soft clay at different initial static shear stress ratios (SSR) and cyclic shear stress ratios (CSR). The cyclic behavior of soft clay with SSR was compared with that without SSR. An empirical model for predicting cyclic strength of soft clay under various SSR and CSR combinations was proposed and validated. Research results indicated that an increase of shear loading level, including SSR and CSR, results in a larger magnitude of shear strain. The response of pore water pressure is simultaneously dominated by the amplitude and the duration of shear loading. The maximum pore water pressure induced by smaller loading over a long duration may be greater than that under larger loading over a short duration. The initial static shear stress does not necessarily have a negative impact on cyclic strength. At least, compared to cases without SSR, the low-level SSR can improve the deformation resistance of soft clay under the cyclic loading. For the higher SSR level, the cyclic strength decreases with the increase of SSR.

In performance-based design, it is crucial to understand deformation characteristics of geocell layers in soil under footing loads. To explore this, a series of laboratory loading tests were carried out to investigate the influence of varying parameters on the strain levels within the geocell layer in a sandy soil under axial strip footing loading. The results were analyzed in terms of maximum strain levels, strain variation along the geocell layer and the correlation between horizontal and vertical strains. In this study, the maximum observed strain levels for geocellreinforced strip footing systems reached 2.3 % for horizontal (tensile) strain and 1.4 % for vertical (compressive) strain. Furthermore, most strain levels were concentrated within a distance of 1.5 times the footing width from the axis of strip footing. In geocell-reinforced footing systems, the interaction between horizontal and vertical strains becomes a key factor, with the ratio of horizontal to vertical cell wall strains ranging approximately from 1 to 2.5. The outcomes of this study are expected to contribute to the practical applications of geocell-reinforced footing systems.

This paper presents a rigorous, semi-analytical solution for the drained cylindrical cavity expansion in transversely isotropic sand. The constitutive model used for the sand is the SANISAND-F model, which is developed within the anisotropic critical state theory framework that can account for the essential fabric anisotropy of soils. By introducing an auxiliary variable, the governing equations of the cylindrical expansion problem are transformed into a system of ten first-order ordinary differential equations. Three of these correspond to the stress components, three are associated with the kinematic hardening tensor, three describe the fabric tensor, and the last one represents the specific volume. The solution is validated through comparison with finite element analysis, using Toyoura sand as the reference material. Parametric analyses and discussion on the impact of initial void ratio, initial mean stress level, at-rest earth pressure coefficient and initial fabric anisotropy intensity are presented. The results demonstrate that the fabric anisotropy of sand significantly influences the distribution of stress components and void ratio around the cavity. When fabric anisotropy is considered, the solution predicts lower values of radial, circumferential and vertical stresses near the cavity wall compared to those obtained without considering fabric anisotropy. The proposed solution is expected to enhance the accuracy of cavity expansion predictions in sand, which will have significant practical applications, including interpreting pressuremeter tests, predicting effects of driven pile installation, and improving the understanding of sand mechanics under complex loading scenarios.

Buried pipelines are essential for the safe and efficient transportation of energy products such as oil, gas, and various chemical fluids. However, these pipelines are highly vulnerable to ground movements caused by geohazards such as seismic faults, landslide, liquefaction-induced lateral spreading, and soil creep, which can result in potential pipeline failures such as leaks or explosions. Response prediction of buried pipelines under such movements is critical for ensuring structural integrity, mitigating environmental risks, and avoiding costly disruptions. As such, this study adopts a Physics-Informed Neural Networks (PINNs) approach, integrated with a transfer learning technique, to predict structural response (e.g., strain) of both unreinforced and reinforced steel pipes subjected to Permanent Ground Displacement (PGD). The PINN method offers a meshless, simulation-free alternative to traditional numerical methods such as Finite Element Method (FEM) and Finite Difference Method (FDM), while eliminating the need for training data, unlike conventional machine learning approaches. The analyses can provide useful information for in-service pipe integrity assessment and reinforcement, if needed. The accuracy of the predicted results is verified against Finite Element (FE) and Finite Difference (FD) methods, showcasing the capability of PINNs in accurately predicting displacement and strain fields in pipelines under geohazard-induced ground movement.

A realistic prediction of excess pore water pressure generation and the onset of liquefaction during earthquakes are crucial when performing effective seismic site response analysis. In the present research, the validation of two pore water pressure (PWP) models, namely energy-based GMP and strain-based VD models implemented in a one-dimensional site response analysis code, was conducted by comparing numerical predictions with highquality seismic centrifuge test measurements. A careful discussion on the selection of input soil parameters for numerical simulations was made with particular emphasis on the PWP model parameter calibration which was based on undrained stress-controlled/strain-controlled cyclic simple shear (CSS) tests carried out on the same sand used in the centrifuge test. The results of the study reveal that the energy-based model predicts at all depths peak pore water pressures and dissipation behaviour in a satisfactory way with respect to experimental measurements, whereas the strain-based model underestimates the PWP measurements at low depths. Further comparisons of the acceleration response spectra illustrate that both the strain- and energy-based models provide higher computed spectral accelerations near the ground surface compared with the recorded ones, whereas the agreement is reasonable at middle depth.

Corn rootworms (CRW) are among the most destructive pests in corn production across the Corn Belt, causing considerable damage through larval feeding on roots. While crop rotation and Bt technologies are widely adopted management strategies, their effectiveness is increasingly compromised by the pest's evolution of resistance and behavioral adaptability. Chemical insecticides applied at planting to target larvae directly serve as an additional tool for corn rootworm control. In this study, we evaluated the performance of various insecticides, applied in-furrow, for managing corn rootworms by assessing Node Injury Scale (NIS), lodging rates, and grain yields from 2020 to 2024. We found that Mode of Action (MOA) 3A insecticides (sodium channel modulators), such as Force Evo (tefluthrin) and Capture LFR (bifenthrin), did not provide substantial efficacy in reducing NIS and lodging rates. In contrast, MOA 1B+3A insecticides (acetylcholinesterase (AChE) inhibitors + sodium channel modulators), such as INDEX (chlorethoxyfos + bifenthrin) and AZTEC HC (tebupirimphos + cyfluthrin), significantly reduced CRW larval damage, particularly under high pest pressure in 2020, 2021 and 2023. Differences in insecticide concentrations did not significantly impact larval control efficacy. Additionally, seasonal rainfall during larval hatching and variation in cumulative corn growing degree days (GDD) strongly influenced the root injury and lodging outcomes. Lower GDD likely limits root regeneration, increasing lodging risk under CRW pressure. These findings demonstrate the values of in-furrow insecticides in managing corn rootworms, particularly under high pest pressure and provide valuable insights for developing integrated pest management strategies to sustain effective CRW larval control and improve crop productivity.

A set of direct shear tests on the soil-geotextile interface (SGI) were conducted using a temperature-controlled constant normal stiffness (CNS) direct shear apparatus. This was done in order to evaluate the effects of normal stiffness, initial normal stress, soil water content, and temperature on SGI shear behavior and microdeformation patterns. The observations indicate that all shear stress-shear displacement curves demonstrate strain-hardening characteristics, with SGI cohesion and friction angle increasing at higher normal stiffness and lower temperatures. At freezing conditions, water content significantly affects the interface friction angle, while this effect is minimal at positive temperatures. Normal stress increases with higher water content, lower temperatures, and higher normal stiffness. Shear stress initially rises with normal stress before decreases, with a more pronounced rise under sub-zero conditions. Normal stress shrinkage shows a positive correlation with normal stiffness. Micro-deformation analysis of soil particles at the interface indicates significant strain localization within the shear band, which is less pronounced under sub-zero temperatures compared to positive temperatures. These patterns of normal displacement vary across analysis points within the shear band, with the macroscopic normal displacement reflecting a cumulative effect of these microscopic variations.

Anisotropic soils exhibit complex mechanical behaviours under various loadsing conditions, e.g., reversible dilatancy, three-dimensional failure strength, fabric anisotropy, small-strain stiffness, cyclic mobility, making it difficult to comprehensively capture these characteristics within a single constitutive model. Failure to capture anisotropic soil behavious may result in poor predictions in geotechnical engineering. Hence, to provide a unified prediction for the mechanical responses of anisotropic sand and clay under both monotonic and cyclic loading conditions, a fabric-based anisotropic constitutive model, i.e., the CASM-CF, is developed within the framework of the standard Clay and Sand Model (CASM) in this paper. Effects of small-strain stiffness and anisotropic elasticity are incorporated into the stiffness matrix to capture the stiffness variation over a wide strain range and reversible dilation. The fabric tensor defined by particle orientation and its evolution law are integrated into the CASM-CF model through the Anisotropic Transformed Stress (ATS) method. The plastic modulus is modified by considering cyclic loading history and stress reverse to better predict the mechanical responses of soils when subjected to cyclic loadings. The newly proposed model is employed to predict the mechanical behaviours of clay and sand under various strain scales and stress paths, including monotonic, cyclic, proportional, and non-proportional loading conditions, in the literature. Conclusions can be drawn that the model performs satisfactorily under various stress paths, and it has the potential to be used in the analysis of practical geotechnical applications of wide range.