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 ability to predict the soil mechanical parameters swiftly is critical for off-road vehicle mobility. This paper introduces a novel interpretation methodology for determining critical soil mechanical parameters by impact penetration tests, enabling rapid and remote assessment of terramechanics properties. Initially, the method employs the Mohr-Coulomb constitutive model and the Coupled Eulerian-Lagrangian (CEL) finite element method to generate a dataset of soil impact penetration resistance and acceleration responses. Subsequently, a Radial Basis Function (RBF) neural network is employed as a surrogate model and integrated with the Nondominated Sorting Genetic Algorithm II (NSGA-II) to accurately interpret parameters such as density, cohesion, internal friction angle, elastic modulus, and Poisson's ratio. Experimental validation using sand and silty clay from Yangbaijing, Tibet, confirmed the accuracy and robustness of the method. The results indicate that the mean absolute percentage error for interpreted values was below 25%, with relative errors for some key parameters even below 10%. Furthermore, each single-condition calculation was completed on a standard computer in less than one minute. Comparative analyses with other algorithms, including MIGA and POS, demonstrated the superior performance of NSGA-II in avoiding local optima. The proposed interpretation framework offers a rapid, reliable, and remote solution for identifying the soil mechanical properties. Its potential applications range from disaster mitigation and emergency response operations to extraterrestrial soil exploration and other scenarios where in-situ investigations are challenging.

Waves can cause significant accumulation of pore water pressure and liquefaction in seabed soils, leading to instability of foundations of marine hydrokinetic devices (MHKs). Geostatic shear stresses (existing around foundations, within slopes, etc.) can substantially alter the rate of pore pressure buildup, further complicating the liquefaction susceptibility assessments. In this study, the development of wave-induced residual pore water pressure and liquefaction within sandy seabed slopes supporting MHK structures is evaluated. Unlike most earlier studies that excluded the impact of shear stress ratios (SSR) on the residual pore pressure response of sloping seabeds, asymmetrical cyclic loadings are considered herein for a range of SSRs. To obtain wave-induced loading in the seabed (and cyclic shear stress ratios, CSRs), the poroelasticity equations governing the seabed response, coupled with those for fluid and structure domains, are solved simultaneously. Utilizing an experimental model based on anisotropic cyclic triaxial test data that includes CSR and SSR impacts, an equation for the rate of pore pressure buildup is developed and added as a source term to the 2D consolidation equation. Numerical investigations were performed by developing finite element models in time domain. The models were calibrated using particle swarm optimization method and validated against wave flume experimental data. The results indicate that the consideration of static shear stresses has led to sudden rise in residual pore pressures followed by fast dissipations at early and late time steps, respectively, beneath the structure. The exclusion of SSR is shown to cause significant overestimation of pore pressure accumulations at late cycles, potentially causing significant overdesign of MHK foundations. The impact of proximity to the free drainage boundary, CSR amplitude, and loading frequency on the accumulation of residual pore pressure is illustrated. The residual liquefaction susceptibility of the seabed is shown to decline by increase of the seabed slope angle.

The present work introduces an analytical framework based on the limit-equilibrium method for the determination of the local factor of safety (FS) and global factor of safety (FSG), and local displacements along the critical slip surface using the Morgenstern-Price (MP) method of slices. This proposed work computes displacements along the critical slip surface in addition to a single FSG. The unsaturated shear strength models, in conjunction with the soil-water characteristic curve (SWCC), are considered. The MP-based equilibrium equations to determine FSG are utilized as an objective function in the metaheuristic search algorithm of particle swarm optimization to determine the critical center, critical radius, and minimum FSG for unsaturated finite slopes. It is recommended to use a particle size of 75 and conduct 50 iterations for optimal results. The effects of SWCC fitting parameters on the critical slip surface, FSG, point FS, and point displacements are also investigated. Two distinct benchmark slope scenarios with and without negative pore water considerations are utilized in the current study. This approach enables a detailed investigation into the influence of various unsaturated soil parameters, such as af (related to the air-entry value), nf (related to the slope of the SWCC), and mf (related to the residual water content), as well as constitutive model parameters including the linear shear modulus (G) and the fitting parameter (rho). The maximum displacement occurs at the slope's top crest. Under benchmark conditions, the first scenario shows a reduction in point displacement by 3.30%, 1.98%, and 10.23% for SWCC-1, SWCC-2, and SWCC-3, respectively. However, in the second scenario with SWCC-3, the critical slip surface's position changes, affecting local displacements. This results in an increase of 32.72% (i.e., from 21.45 to 28.47 mm) in point displacement at the top when comparing SWCC-3 with no SWCC consideration. The current study advocates that the effect of fitting parameters of the SWCC should be used to better understand the local FS and displacement, because the critical slip surface is contingent on the values of the SWCC. Ignoring SWCC parameters can lead to an underestimation of slope displacement, because they significantly influence the critical slip surface position and displacement magnitude. Their inclusion is essential for accurately assessing slope stability and preventing errors in displacement prediction.

To address the challenge of the complex and extensive seismic design elements of tunnels, which are difficult to be accurately described using mathematical functions, a novel model combining convolutional neural networks (CNN), gated recurrent units (GRU), and an attention mechanism is proposed. Firstly, based on actual engineering examples, the tunnel dimensions and site soil information are determined to establish a numerical model of tunnel seismic response and verify its reliability. Then, the soil parameters, seismic motion amplitude, tunnel depth, and overlying water depth are selected for systematic analysis of the displacement momentum (DM) and time of maximum damage occurrence (TMDO). The parameters with higher influence are chosen as input variables, while the calculated DM and TMDO from the reliable numerical model are selected as the output variables to be predicted. Next, integrating the GRU model to capture long-term dependencies in time series, the CNN model to extract spatial features, and the attention mechanism to handle complex relationships among multiple variables, the CNN-GRU-Attention prediction model was established. By generating dataset samples through numerical simulation, accurate predictions of DM and TMDO were achieved. Finally, using the proposed model to establish the objective function relationship between input and output parameters, employing the NonDominated Sorting Genetic Algorithm II (NSGA-II) to find the optimal input design features, achieving the optimal design of tunnel seismic performance. The results show that: (1) The calculation results of the numerical model for tunnel seismic response conform to general research findings, indicating sufficient reliability. (2) The error compensation and dynamic updating mechanisms improved prediction accuracy. The R2 values for the training set reach 0.973 and 0.982 respectively. (3) Optimizing DM and TMDO using the NSGA-II algorithm leads to a 23.42% reduction in DM and a 18.71% increase in TMDO. After optimization, tunnel displacement is reduced, damage is delayed, and seismic performance is significantly improved.

Linqing bricks, a critical material in Chinese Ming-Qing Dynasty royal architecture, face performance deficiencies in modern production compared to historical counterparts, mainly due to uneven temperature fields in kilns and fluctuations in firing quality caused by empirical raw material ratios. Based on a real brick kiln, this study systematically investigates the effects of material composition and firing conditions on brick performance using locally sourced Linqing clay and laterite. Controlled firing experiments were conducted with varying laterite proportions (0-100 wt%), loess proportions (0-100 wt%), clay additions (0-10 wt%), and temperatures (1020-1058 degrees C), followed by comprehensive analyses of physical properties, phase composition, microstructure, and thermal behavior. According to the experimental results, increasing laterite content enhances compressive strength (from 11.9 to 38.1 MPa) and bulk density (from 1.45 to 1.65 g/cm3), with pure laterite achieving optimal performance. A clay content of 5 wt% maximizes mechanical properties, while elevating firing temperature to 1058 degrees C significantly improves strength (increased 13Mpa over 1020 degrees C). Using the CRITIC weighting method, we propose an optimized formulation (50-60 wt% laterite, 40-50 wt% loess, 5 wt% clay) fired at 1058 degrees C. This research not only promotes the standardization and scientific approach of modern Linqing brick production processes but also better controls the overall consistency of the quality of Linqing bricks in kilns. Additionally, it provides a more authentic and reliable material guarantee for the restoration of ancient architectural heritage.

Energy pile is a green, constant-temperature utilization technology with dual functions of heat exchange and load bearing. Improving its heat transfer efficiency has always been one of the main directions of scholars' research. This study discussed the optimization of heat transfer buried pipe parameters, modification of pile materials, and improvement of working fluid performance within the pipes. Additionally, based on the research achievements of the research team in recent years regarding heat transfer enhancement in energy piles, a comprehensive heat transfer enhancement system is summarized, aiming to provide new ideas and methods for the study of heat transfer enhancement in energy piles. The optimization status of different buried pipe types and pipe parameters is also summarized. The heat transfer performance and mechanical properties of different modified concrete materials are studied. A comparison and analysis of the heat transfer performance and flow characteristics of different types of circulating mediums with nanofluids are conducted, providing new approaches to improve the heat transfer performance of circulating mediums. Finally, discussions and prospects were made on the external environmental conditions around the pile, thermal interference phenomena of pile groups, energy storage concrete, the long-term stability of nanofluids, benefit assessment, and ecological evaluation. These efforts aim to promote research on energy piles both domestically and internationally.

Soil liquefaction is a major contributor to earthquake damage. Evaluating the potential for liquefaction by conventional experimental or empirical methods is both time-intensive and laborious. Utilizing a machine learning model capable of precisely forecasting liquefaction potential might diminish the time, effort, and expenses involved. This research introduces an innovative predictive model created in three phases. Initially, correlation analysis determines essential elements affecting liquefaction. Secondly, predictions are produced using Convolutional Neural Networks (CNN) and Deep Belief Networks (DBN), verified by K-fold cross-validation to guarantee resilience. Third, Ant Colony Optimization (ACO) improves outcomes by increasing convergence efficiency and circumventing local minima. The suggested EC + ACO model substantially surpassed leading approaches, such as SVM-GWO, RF-GWO, and Ensemble Classifier-GA, attaining a very low False Negative Rate (FNR) of 2.00 % when trained on 90 % of the data. A thorough performance evaluation shown that the model achieved a cost value of 1.133 % by the 40th iteration, exceeding the performance of other models such SVMGWO (1.412 %), RF-GWO (1.305 %), and Biogeography Optimized-Based ANFIS (1.7439 %). The model exhibited significant improvements in convergence behavior, with a steady decline in cost values, especially between the 20th and 50th iterations. Additional validation using empirical data from the Tohoku-oki, Great East Japan earthquake substantiated the EC + ACO model's enhanced accuracy and dependability in mirroring observed results. These findings underscore the model's resilience and efficacy, providing a dependable method for forecasting soil liquefaction and mitigating its seismic effects.

The cohesion and internal friction angle of loess are important macro-mechanical parameters for evaluating the safety and stability of engineering construction. Traditional laboratory measurement methods are time-consuming and difficult to conduct on-site. This study aims to compare the effectiveness of five Machine Learning (ML) methods, namely Random Forest (RF), Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), BPNN optimized by Particle Swarm Optimization (PSO-BPNN) and BPNN optimized by Genetic Algorithm (GA-BPNN), in predicting the macro-mechanical properties of loess. To this end, the study collected data from 89 undisturbed loess samples and 229 remolded loess samples to construct training and testing datasets, and used three correlation analysis methods to analyze the influence of physical parameters on mechanical properties. The study found that the water content has the most significant impact on the mechanical properties of loess. In terms of prediction ability, SVM performs the best among the ML methods used, and the determination coefficient for cohesion of undisturbed loess reaches 0.857. Although the training data is limited, the prediction performance of BPNN is significantly improved after being optimized by PSO or GA. The research results show that ML provides an effective way to study the complex mechanical behavior of loess.

To ensure the sustainable development of the surrounding environment and the sustainable operation of landfills, detecting landfill leakage is of great significance. In landfills lacking a leakage monitoring system, the inability to detect and locate damaged High-Density Polyethylene (HDPE) membranes can lead to the contamination of soil and groundwater by landfill leachate. To address this issue, this study proposes a resistivity tomography inversion model based on the external-electrode power supply mode. Utilizing the resistivity difference between the leakage zone and the surrounding soil, electrodes are arranged symmetrically for both power supply and measurement. Upon applying direct current (DC) excitation, potential data are collected, with the finite volume method employed for inversion and the Gauss-Newton method integrated with an adaptive particle swarm optimization algorithm for parameter fitting. Experimental results show that the combined algorithm provides better clarity in edge recognition of low-resistance models compared with single algorithms. The maximum deviation between inferred leakage coordinates and the actual location is 10.1 cm, while the minimum deviation is 6.4 cm, satisfying engineering requirements. This method can effectively locate point sources and line sources, providing an accurate solution for subsequent leakage point filling and improving repair efficiency.