The coupled thermo-hydro-mechanical response caused by fire temperature transfer to surrounding rock/soil has a significant impact on tunnel safety. This study developed a numerical simulation model to evaluate the effects of fire on tunnel structures across different geological conditions. The heat transfer behavior varied with the mechanical properties and permeability of the geotechnics, concentrating within 1.0 m outside the tunnel lining and lasted for 10 days. Significant differences in pore water pressure changes were observed, with less permeable geologies experiencing greater pressure increases. Tunnel deformation was more pronounced in weaker geotechnics, though some tunnels in stronger geologies showed partial recovery post-fire. During the fire, thermal expansion created a bending moment, while a negative bending moment occurred after the fire due to tunnel damage and geotechnical coupling. The entire process led to irreversible changes in the bending moment. The depth of tunnel burial showed varying sensitivity to fire across different geological settings. This study provides important references for fire protection design and post-fire rehabilitation of tunnels under diverse geological conditions.

Using local materials with low environmental impact is essential in building living spaces, combining energy efficiency, environmental respect, and user well-being. However, despite advances in using natural materials, few studies have focused on integrating spathe fibers into earth bricks to optimize their thermal, mechanical, and hydric performance. The study aims to develop an innovative approach to using spathe fibers as natural reinforcement in manufacturing soil bricks while analyzing their impact on thermal, mechanical, and hydric properties. Several soil bricks reinforced with spathe fibers at different concentrations (0%, 1%, 2%, 3%, 4%, and 5%) were developed. Thermal performance was assessed using the hot disk method, while mechanical strength was measured in compression and flexure with capillary absorption tests. Based on fiber content, the brick density ranged from 1719.75 to 1247.6 kg/m3. The thermal conductivity of the materials ranges from 0.621 to 0.327 W/m. K, indicating good insulating performance. Maximum capillary water absorption values range from 170 to 287%, revealing a difference in water permeability depending on fiber content. Compressive strengths range from 1.4 to 3.6 MPa, and flexural strengths range from 1.6 to 1.91 MPa, suggesting potential for structural applications. Physico-chemical and geotechnical analyses confirm the suitability of the soil for the production of spathe fiber-stabilized bricks. This study offers an alternative to conventional bricks, contributing to the promotion of ecological and sustainable building materials suitable for arid and semi-arid climates.

Monopiles are the most popular type of foundation for offshore wind turbines. Although capturing the effects of cyclic loading is critical to the design of monopiles, there is no recommended approach in the main design standards and no consensus in the literature as to how this can be achieved. Hence, this paper presents the step-by-step methodology and validation of a new cyclic model in sand and clay. The model consists of the degradation of monotonic soil reaction curves based on soil cyclic contour diagrams. The model is found to provide a very satisfactory match with the PISA field tests in Dunkirk dense marine sand and Cowden stiff glacial till.

This study utilizes a combined approach of Finite Element Method (FEM) simulation and Artificial Neural Network (ANN) modeling to analyze and predict the load-displacement relationship of bored piles in clayey sand. FEM is applied to simulate the nonlinear relationship between load and vertical displacement, with input parameters including load and the mechanical properties of the soil. The results obtained from FEM are used as input data for the ANN model, enabling accurate predictions of vertical displacement based on these parameters. The findings of this study show that the predicted ultimate bearing capacity of the bored piles is highly accurate, with negligible error when compared to field experiments. The ANN model achieved a high level of accuracy, as reflected by an R2 value of 0.9992, demonstrating the feasibility of applying machine learning in pile load analysis. This research provides a novel, efficient, and feasible approach for analyzing and predicting the bearing capacity of bored piles, while also paving the way for the application of machine learning in geotechnical engineering and foundation design. The integration of FEM and ANN not only minimizes errors compared to traditional methods but also significantly reduces time and costs when compared to field experiments.

The present document presents a review on the use of the finite element software package CODE_BRIGHT to simulate reinforced soil structures (RSS). RSS are composed of longitudinal steel or polymeric materials, placed orthogonal to the main stress direction in a soil mass, acting as tension-bearing elements. A common application of RSS is in retaining structures, in the form of reinforced soil walls (RSWs). RSW are usually designed with analytical methods, which have limited capabilities when predicting a structure's deformation response. To improve on this, the use of numerical tools allows to quantify the stress-strain response of complex, compound structures, such as RSWs. Several factors must be considered when modelling RSS, including reinforcement response, which can be non-linear under several circumstance (including time- and temperature-dependencies), soil-reinforcement interaction, soil-structure interaction, and soil response, all of which can be affected by the presence of moisture. Using laboratory measured data, the individual response of reinforcements (e.g., creep elongation), as well as the compound behaviour of soil-reinforcement material (e.g., pullout response) can be simulated to explore individual and compound response. Depending on the modelled phenomena, numerical simulations may include 2D and 3D representations. For full-scale reinforced soil walls, the stress-strain response within the soil mass, reinforcements, concrete facing panels, and connections can be studied in magnitude and distribution. Details regarding special considerations of how to model such structures with CODE_BRIGHT and other commercially available software are provided. Insights on the thermo-hydraulic repone of RSWs are covered. Advantages, limitations and future lines of research in the use of CODE_BRIGHT are explored.

This direction paper explores the evolving landscape of physics-informed machine learning (PIML) methodologies in the field of geotechnical engineering, aiming to provide a comprehensive overview of current advancements and propose future research directions. Recognising the intrinsic connection between geophysical phenomena and geotechnical processes, we delve into the inter of physics-based models and machine learning techniques. The paper begins by elucidating the significance of incorporating physics-informed approaches, emphasising their potential to enhance the interpretability, accuracy and reliability of predictive models in geotechnical applications. We review recent applications of PIML in soil mechanics, hydrology, geotechnical site investigation, slope stability analysis and foundation engineering, showcasing successes and challenges. Furthermore, we identify promising avenues for future research in geotechnical engineering, including the integration of domain knowledge, model explainability, multiphysics and multiscale problems, complex constitutive models, as well as digital twins and large AI models within PIML frameworks. As geotechnical engineering embraces the paradigm shift towards data-driven methodologies, this direction paper offers valuable insights for researchers and practitioners, guiding the trajectory of PIML for sustainable and resilient infrastructure development.

A series of cyclic triaxial tests were conducted on marine soft clay deposits to establish and validate a predictive model for cumulative plastic strain. Additionally, a numerical model of particle flow code in cyclic triaxial tests was developed. The effects of confining pressure, moisture content, and dynamic stress ratio on the dynamic properties of marine soft clay were examined, considering factors such as volume deformation and microscopic failure patterns. The results indicated that both the predictive model and numerical model showed strong consistency with the experimental data. The plastic strain of marine soft clay was influenced by moisture content, stress ratio, and confining pressure in a consistent manner, with moisture content being the primary factor. A predictive model for the cumulative plastic strain of marine soft clay was successfully established, allowing for the evaluation of dynamic properties from the perspective of cumulative plastic strain. During the loading process in the numerical model, microcracks within the soil structure gradually compacted, and the main displacement of the specimen extended from the vertical center axis to the sides, ultimately resulting in shear damage.

Geotechnical seismic isolation (GSI) is a new concept that has been proposed recently. The injection of polyurethane into the soil layer (non-intrusive GSI) reduces seismic fragility without altering the original structure, which may provide an effective seismic isolation solution for existing bridge structures. The purpose of this study was to investigate the seismic isolation effect and isolation mechanism of non-invasive GSI applied to existing bridges. First, a noninvasive GSI site modeling method is described based on the results of existing soilpolyurethane resonance column tests and the OpenSees computational platform. Subsequently, a refined dynamic analysis model of site-existing bridge interactions was established by combining the rusting theory. The seismic isolation effect of the non-invasive GSI and its effect on the seismic response of the bridge were explored using a nonlinear dynamic time-course analysis. The results showed that non-invasive GSI soils can change the characteristic period of ground motion, thus reducing the site effect. The seismic isolation effect was positively correlated with the percentage of injected polyurethane. Altering the characteristic period of the site and avoiding as many of the preeminent periods of ground motion as possible is the result of noninvasive GSI. The non-invasive GSI soil layer reduces the structural response and provides seismic isolation throughout the life cycle of corroded piers, and its fragility is significantly reduced. Especially, the old piers have significant seismic isolation effect, effectively limiting serious damage or even collapse under earthquakes. The results of this study provide a reference for noninvasive GSI design of existing bridge structures.

Fly ash, a by-product of coal combustion, enhances the geotechnical properties of soil, primarily through its two types: class F and class C, known for their pozzolanic and cementitious properties, respectively. Numerous studies have explored the benefits of both types offly ash in stabilizing problematic expansive soils, which are characterized byweak strength, high compressibility, and significant volume changes that can damage infrastructure. However, direct comparisons between class F and class C fly ashes in improving expansive soils are limited. This study aims to fill this gap by conducting a critical review of research from the past 20 years, focusing on the impact of class F and class C fly ashes on the geotechnical properties of expansive clayey soils. Key parameters examined include Atterberg limits, free swell, unconfined compressive strength (UCS), and California bearing ratio (CBR). The findings indicate that both fly ash types reduce liquid limits and plasticity indices of clayey soils, with class C fly ash showing more pronounced effects. Additionally, class C fly ash significantly reduces soil swelling and enhances UCS and CBR, especially due to its higher CaO content. The study provides novel formulas to aid future researchers in predicting the behavior and performance of clayey soils stabilized with these specific fly ash types, offering a comprehensive examination of their geotechnical parameters.

Biopolymer-based soil treatment (BPST) enhances soil strength through biofilm matrix formation within soil voids. This study investigates the effects of biopolymer concentration, porosity, and soil packing conditions on biopolymer distribution and connectivity after dehydration. Laboratory experiments assessed the degree of biopolymer filling (DoBF), final condensed biopolymer concentration, and biopolymer film connectivity under simple cubic and rhombohedral packing conditions. The results show that higher initial biopolymer concentrations increase final biopolymer volume, though not proportionally due to threshold effects. Rhombohedral packing results in higher final condensed biopolymer concentrations than simple cubic packing, despite having lower DoBF values, while biopolymer connectivity peaks at an optimal porosity (n approximate to 0.35). Further analysis revealed a strong correlation between biopolymer matrix formation and soil mechanical properties, including uniaxial compressive strength (UCS), cohesion, and friction angle. UCS was found to decrease with increasing porosity, and a predictive model was developed using experimental data. The rhombohedral and simple cubic packing conditions respectively define the upper and lower bounds of the shear parameters. A back-calculation approach confirmed that DoBF provides the most accurate estimation of friction angle and UCS, reinforcing its importance as a key parameter in soil stabilization. These findings emphasize the need for optimized biopolymer concentration and soil structure adjustments to enhance reinforcement efficiency. The study offers valuable guidance for geotechnical applications, enabling the development of optimized biopolymer injection strategies that enhance mechanical performance and promote efficient material utilization.