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.

Zinc-ion capacitors (ZICs) are viewed as a promising energy storage solution for portable electronics and biocompatible devices. Nevertheless, current ZICs technology faces challenges such as restricted specific capacitance, suboptimal cycling performance, and ongoing validation efforts regarding their biocompatibility. Herein, hierarchical porous carbon materials were prepared through a two-step carbonization-activation method using kapok fiber biomass as the precursor. The kapok fibers-based cathodes contain abundant micropores and mesopores, which provide abundant active sites for Zn2+ storage and optimize reaction kinetics. The ZICs demonstrate an ultra-high cycling life exceeding 240,000 cycles. Meanwhile, theoretical calculations verify that large micropores exhibit a reduced diffusion energy barrier for [Zn(H2O)6]2+, which accelerates [Zn(H2O)6]2+ adsorption/desorption and increases the available reversible capacitance. Furthermore, the ZICs exhibit excellent biodegradability in soil, simulated human body fluids and real seawater, and low cytotoxicity to human cells and minimal tissue damage in animal. This research presents a potential pathway for the advancement and verification of biocompatible ZICs, thereby contributing to their prospective practical utilization in biomedical and environmental field.

To investigate the potential application of geopolymer materials in pile foundation post-grouting engineering, this study utilized industrial solid wastes such as fly ash (FA), slag (SL), and steel slag (SS) to prepare geopolymer grouting materials (GGMs) with various mix proportions. The fluidity, setting time, bleeding rate, and mechanical properties of these materials were evaluated to determine the optimal mix proportions for pile foundation grouting. Furthermore, the influence mechanisms of different maintenance conditions on material performance were investigated, including unconfined compressive strength, flexural strength, and microstructural changes. The results indicated that when the SL-to-FA ratio was 1:1, the GGMs satisfied the requirements for pile foundation grouting, and their mechanical properties significantly improved with extended curing time. Under Yellow River water maintenance conditions, the materials formed a dense three-dimensional network of hydrated products, notably enhancing their mechanical characteristics. Additionally, field tests confirmed that GGMs effectively improved the shear strength of the pile-soil interface. The grout distribution pattern on the pile side exhibited a compaction-splitting mechanism. These research findings provide theoretical support for applying geopolymer materials in pile foundation grouting engineering.

Propionic acid, benzoic acid, and sorbic acid are common preservatives in processed foods, but their natural concentrations in raw foods are not well understood. In Korea, if propionic acid or benzoic acid exceeds certain levels in processed plant-based foods, manufacturers must prove they are naturally occurring to avoid recalls. This regulation applies to both local and imported products, with non-compliance leading to economic costs and damage to brand reputation. To address this, further research is needed to build a database of naturally occurring preservatives in raw foods and develop food safety standards. This study quantified the levels of propionic, benzoic, and sorbic acids in 37 raw foods stored at different temperatures for 2 weeks, using gas chromatography and high-performance liquid chromatography. The changes in acid concentrations during storage were examined, with preservatives identified by mass spectrometry. Sensory evaluation and total viable count were used to assess spoilage in seafood and meat samples. The matrix effects were evaluated, and the quantification methods were validated using seven food matrices. The intra- and interday accuracies and precisions (% relative standard deviation, %RSD) values met the Codex guidelines. In general, the propionic acid contents of most seafood and meat samples increased during storage, with the highest content being found in manila clams. Interestingly, the fresh sea squirt sample contained a higher propionic acid content (456 mg/kg) than the other samples. However, benzoic acid and sorbic acid were not detected in most samples, with the exception of the jujube and apple samples.

This study aims to improve the forecasting performance of slope stability for impacting environmental sustainability and infrastructure safety predictions by using the Binary Particle Swarm Optimization BPSO technique is utilized to select relevant features from the dataset, thereby improving the overall effectiveness of the predictive models. The research includes 108 slope stability examples, with the dataset split between 70% training and 30% validation. The dataset comprises seven input parameters: cohesiveness, slope angle, unit weight, angle of internal friction, slope height, pore water pressure coefficient, and factor of safety. The objective is to classify the slope status, turning the problem into a classification task. To obtain optimal hyper-parameters for the SVM model, Grid Search was exploited. The accuracy of the slope stability predictions given by several models was assessed using receiver operating characteristic (ROC) curves. The results indicate that the BPSO-SVM model outperforms the standalone SVM and BPSO models, serving as a robust computational tool capable of accurately predicting slope stability to enhance the environmental sustainability.

When an underground structure passes through a liquefiable soil layer, the soil liquefaction may pose a significant threat to the structure. A centrifuge shaking table test was performed to research the seismic response of underground structures in liquefiable interlayer sites, and a valid numerical model was obtained through simulation model test. Finally, the calibrated numerical model was used to perform further research on the influence of various distribution characteristics of liquefiable interlayers on the seismic reaction of underground structures. The key findings are as follows. The structure faces the most unfavorable condition once a liquefiable layer is located in the middle of the underground structure. When a liquefiable layer exists in the middle of the structure, the seismic reactions of both the underground structure and model site will increase with the rise of the thickness of the liquefiable interlayer. The inter-story drift of the structure in the non-liquefiable site is much smaller than that in the liquefiable interlayer site. The inter-story drift of the structure is not only associated with the site displacement and the soil-structure stiffness ratio but also closely associated with the slippage of the soil-structure contact interface under the condition of large deformation of the site.

Ensuring construction safety and promoting environmental conservation, necessitate the determination of the optimal jacking force for rectangular pipe jacking projects. However, reliance on empirical calculations for estimating jacking force often resulted in overly conservative results. This study proposed a modified Protodyakonov ' s arch model to calculate the soil pressure around the jacked pipe considering the critical damage boundary. A three-dimensional log-spiral prism model, based on limit equilibrium method was applied to analyze the resistance on the shield face. The determination of jacking force integrated factors such as soil pressure around jacked pipes, friction coefficient between pipe and soil, and shield face resistance. By utilizing Suzhou ' s jacking-pipe engineering as a practical context, the accuracy was validated against field monitoring data and existing jacking force calculation models of varying specifications. Parametric analysis indicated the jacking force is linearly correlated with the soil unit weight and pipe-soil friction coefficient. However, the jacking force decreases significantly with increasing internal friction angle. As the internal friction angle rose from 25 degrees to 50 degrees , the soil arch height gradually diminished from 8.91 to 2.59 m. Notably, a complete arch structure failed to form above the jacked pipe when the cover depth ratio was less than 0.5. The heightened predictive precision of the proposed model enhanced its suitability for practical shallow buried tunnel jacking force predictions.

Worldwide, an increasingly huge number of end-of-life tires (ELTs) are disposed of in landfills, illegally dumped, or otherwise unaccounted for, which causes significant environmental and socioeconomic issues. Finding sustainable engineering solutions to recycle and reuse ELTs, which transform them from unwanted waste into useful resources, has become a priority. In geotechnical engineering, researchers have performed laboratory and field tests to determine the mechanical properties of innovative geomaterials that consist of soil-rubber mixtures (SRMs) [i.e., gravel-rubber mixtures (GRMs)] that are obtained using recycled ELT-derived granulated rubber aggregates. Suitable engineering properties and low installation cost encourage the use of GRMs and SRMs in many applications, such as in free-draining energy-adsorption backfill material for retaining walls, underground layers for liquefaction mitigation and geotechnical seismic isolation systems for structures and infrastructures. However, due to the heterogeneity of SRMs, their ultimate adoption as geomaterials must be supported by constitutive relationships that can accurately describe their mechanical behavior under typical field loading conditions. The aim of the paper is to evaluate the effectiveness and limits of the hardening soil model with small strain stiffness (HS-small), which is present in many finite-element (FE) codes, to model the behavior of GRMs in geotechnical engineering applications. An extensive finite-element method simulation of drained triaxial tests was performed.

This case study explores the utilization of distributed fiber optic sensors (DFOS) in wooden foundation piles, for assessing and monitoring the stress distribution along their length. Three spruce and three pine foundation piles instrumented with DFOS were driven into the soil in a testing field in Amsterdam and axially loaded in compression. Since DFOS provided strain information, calculating the stress distribution in the piles required knowledge of their stiffness properties, which inherently vary from the head to the tip. Consequently, the piles were extracted and their overall wet dynamic elastic modulus (E-c,E-0,E-dyn,E-wet) was determined through frequency response measurements. Subsequently, the piles were segmented, transported to the TU Delft Laboratory and subjected to mechanical testing. For each segment, the mechanical properties were determined and their variability along the pile was studied, in particular for the static modulus of elasticity (E-c,E-0,E-stat,E-wet). This enabled a comprehensive assessment of the actual in-situ stress distribution (Delta sigma(actual,stat) and Delta sigma(actual,dyn)) along the length of the piles, calculated with DFOS strains and the pile stiffness (E-c,E-0,E-stat,E-wet and E-c,E-0,E-stat,E-dyn). Given the novelty of the DFOS application to timber piles, a validation of the accuracy was conducted on 3 pile segments equipped with DFOS. These segments underwent laboratory compression testing, allowing for a direct comparison between DFOS strain readings and strains measured with linear potentiometers attached to the pile segments. The results revealed good accuracy of DFOS in controlled lab conditions, with a maximum stress deviation of 0.65 MPa. Since the testing field featured a 6-meter-deep predrilled layer, where negligible shaft friction was mobilized, the no-friction stress (Delta sigma(no-friction)) approximately aligned with Delta sigma(actual,stat) on the piles. At pile tips, the maximum applied 300-350 kN compressive load (i.e. Delta sigma(no-friction) = 20-26 MPa), resulted in Delta sigma(actual,stat) = 4-7 MPa, highlighting shaft friction effect. The calculated Delta sigma(actual,dyn) with a single E-c,E-0,E-stat,E-dyn for the whole pile, led to 3 MPa stress overestimation at pile tip. Although this calculation is conservative, the detailed knowledge of the variation of stiffness properties along the pile would result in a more efficient structural use.

Liquefaction has caused severe damage to buildings and infrastructure during numerous earthquakes, leading researchers to develop constitutive models that can capture complex soil behaviour in liquefaction-induced phenomena. Constitutive models require validation against laboratory or real-world data to assess their capability. This paper first discusses the recent implementation of the stress-density (S-D) model in the OpenSees finite element platform. Subsequently, calibration and validation phases evaluate the performance of the S-D model against two previously conducted centrifuge tests. Single-element simulations of cyclic simple shear tests inform the parameter calibration for the Nevada sand, which comprises the two main layers in the centrifuge tests. The validation phase consists of eight 1-D site response analyses in OpenSees compared to the centrifuge tests in terms of accelerations, spectral accelerations, pore water pressures, and settlements. The current study shows that the model reasonably predicts the soil behaviour in terms of acceleration and pore water pressure, particularly in the liquefiable layer.