Yi-Li Yuan

Surface erosion of loess slopes in arid and semi-arid regions of China remains a critical geotechnical issue, requiring green and low-carbon stabilization techniques. This study investigated the effectiveness of enzyme-induced carbonate precipitation (EICP) for the surface spraying reinforcement of Q3 loess collected from a high-fill engineering site at Yan’an University. Single-factor tests, response surface methodology (RSM), surface strength tests, CT-based three-dimensional pore reconstruction, and scanning electron microscopy (SEM) were conducted to evaluate the effects of cementation solution concentration and spraying dosage. The cementation solution was prepared by mixing analytical-grade urea and anhydrous calcium chloride at a 1:1 molar ratio, and the specimens were compacted to a dry density of 1.4 g/cm³. The results showed that surface strength first increased and then decreased with increasing cementation solution concentration and spraying dosage. Spraying dosage had a more pronounced influence than cementation solution concentration; excessive spraying above 9 L/m² reduced surface strength because of the high water sensitivity of loess. Five replicate tests at the central point were conducted to evaluate experimental error. The optimal parameters were 1.5 mol/L for cementation solution concentration and 9 L/m² for spraying dosage. CT and SEM results showed that CaCO₃ precipitation filled large pores and cemented soil particles, reducing total porosity from 6.7% to approximately 4.0%. These findings indicate that EICP improves loess surface strength mainly through pore filling and particle cementation, providing guidance for the ecological protection of loess slopes.

This study presents a comprehensive treatment system for addressing leakage challenges in underground structure construction within complex karst terrains, demonstrated through the case of Baiyun Station in Guangzhou. Integrating advanced geological investigation, dynamic grouting techniques, and adaptive structural remediation strategies, this methodology effectively mitigates water inflow risks in structurally heterogeneous karst environments. Key innovations include the “one-trench two-drilling” exploration-grouting system for karst cave detection and filling, a multi-stage emergency water-gushing control protocol combining cofferdam sealing and dual-fluid grouting, and a zoned epoxy resin injection scheme for structural fissure remediation. Implementation at Baiyun Station achieved quantifiable outcomes: karst cave filling rates increased from 35.98% to 82.6%, foundation pit horizontal displacements reduced by 67–68%, and structural seepage repair rates reached 96.4%. The treatment system reduced construction costs by CNY 12 million and shortened schedules by 45 days through optimized pile formation efficiency (98% qualification rate) and minimized rework. While demonstrating superior performance in sealing > 0.2 mm fissures, limitations persist in addressing sub-micron fractures and ensuring long-term epoxy resin durability. This research establishes a replicable framework for underground engineering in karst regions, emphasizing real-time monitoring, multi-technology synergy, and environmental sustainability.

This study proposes an RBF-PSO hybrid framework for efficient inversion analysis of hydration heat parameters in mass concrete temperature fields, addressing the computational inefficiency and accuracy limitations of traditional methods. By integrating a Radial Basis Function (RBF) surrogate model with Particle Swarm Optimization (PSO), the method reduces reliance on costly finite element simulations while maintaining global search capabilities. Three objective functions—integral-type (F₁), feature-driven (F₂), and hybrid (F₃)—were systematically compared using experimental data from a C40 concrete specimen under controlled curing. The hybrid F₃, incorporating Dynamic Time Warping (DTW) for elastic time alignment and feature penalties for engineering-critical metrics, achieved superior performance with a 74% reduction in the prediction error (mean MAE = 1.0 °C) and 2 and avoiding F₁’s prohibitive computational costs (498 FEM calls). Comparative benchmarking against non-surrogate optimizers (PSO, CMA-ES) confirmed a 2.8–4.6× acceleration while maintaining accuracy. Sensitivity analysis identified the ultimate adiabatic temperature rise as the dominant parameter (78% variance contribution), followed by synergistic interactions between hydration rate parameters, and indirect coupling effects of boundary correction coefficients. These findings guided a phased optimization strategy, as follows: prioritizing high-precision calibration of dominant parameters while relaxing constraints on low-sensitivity variables, thereby balancing accuracy and computational efficiency. The framework establishes a closed-loop “monitoring-simulation-optimization” system, enabling real-time temperature prediction and dynamic curing strategy adjustments for heat stress mitigation. Robustness analysis under simulated sensor noise (σ ≤ 2.0 °C) validated operational reliability in field conditions. Validated through multi-sensor field data, this work advances computational intelligence applications in thermomechanical systems, offering a robust paradigm for parameter inversion in large-scale concrete structures and multi-physics coupling problems.

Loess, predominantly distributed in arid and semi-arid regions of central and western China, exhibits low shear strength and structural instability, rendering it prone to geological hazards such as landslides and collapses, which pose significant threats to local infrastructure and safety. This study evaluated the urease activity of soybean and sword bean at different temperatures to screen the optimal enzyme source for enzyme-induced carbonate precipitation (EICP). Methods including single EICP, EICP combined with nano-SiO₂, and EICP combined with both nano-SiO₂ and soil stabilizer (SS) were adopted to enhance the surface strength of loess. The results showed that the EICP technique significantly improved the surface strength of loess, especially with the addition of nano-SiO₂ and soil stabilizer. This study confirmed that using sword bean urease treated at −20 °C for 24 h in combination with 1.5% nano-SiO₂ was both cost-effective and efficient in reinforcement. The incorporation of 5% soil stabilizer further enhanced the surface strength, and the accuracy was further verified by combining the results of SEM and XRD. Future research will focus on optimizing the material ratio to maximize the improvement of surface strength, providing an economical and feasible solution for rapid loess solidification, and evaluating the long-term durability under cyclic wet and dry conditions.

Sampling of training data is the most important step in active learning slope reliability analysis, which controls the analysis accuracy. In this study, a novel surrogate-assisted normal search particle swarm optimization (SANSPSO) was proposed to enhance the accuracy and robustness of existing methodologies. In SANSPSO, the sampling process was considered a minimum problem with an objective function defined as the absolute value of the performance function. Initiated with a normal search paradigm and supplemented by three algorithm strategies, this approach seeks to preserve the continuity of the solution while refining the algorithm’s efficacy and efficiency. To reduce computation cost, surrogate-assistance was used, in which a surrogate model substitutes the objective function in most iterations. This surrogate model evolves during the iteration process and ultimately replaces the actual performance function within Monte Carlo simulation. Finally, this study presents a comparative study with five state-of-the-art methods across four explicit problems and three engineering cases, where test data suggest that the SANSPSO methodology yields a 20% improvement in accuracy and a 30% rise in stability under different dimensional problems relative to the most efficacious of the alternate methods assessed because of the improved and more consistent prediction of limit state function. These findings substantiate the validity and robustness of the SANSPSO approach.

In active learning reliability methods, an approximation of limit state function (LSF) with high precision is the key to accurately calculating the failure probability (P_f). However, existing sampling methods cannot guarantee that candidate samples can approach the LSF actively, which lowers the accuracy and stability of the results and causes excess computational effort. In this paper, a novel candidate samples-generating algorithm was proposed, by which a group of evenly distributed candidate points on the predicted LSF of performance function (either the real one or the surrogate model) could be obtained. In the proposed method, determination of LSF is considered as an optimization problem in which the absolute value of performance function was considered as objective function. After this, a normal search particle swarm optimization (NSPSO) was designed to deal with such problems, which consists of a normal search pattern and a multi-strategy framework that ensures the uniform distribution and diversity of the solution that intends to cover the optimal region. Four explicit performance functions and two engineering cases were employed to verify the effectiveness and accuracy of NSPSO sampling method. Four state-of-the-art multi-modal optimization algorithms were used as competitive methods. Analysis results show that the proposed method outperformed all competitive methods and can provide candidate samples that evenly distributed on the LSF.

The stability of geogrid-reinforced soil structure is closely related to the interface characteristics between geogrid and soil. However, the creep behavior of the soil–geogrid interface is still unrevealed. In this study, using a modified stress-controlled pullout device, influence of the normal pressure, dry density, and water content on creep behavior of interface of compacted loess and high-density polyethylene (HDPE) geogrid is investigated. A three-parameter empirical model and a Merchant element model were established through fitting analysis. Analysis results show that the normal pressure, dry density, and water content have significant effects on the creep shear displacement of the reinforced soil interface. Under the same pullout level, creep displacement of the interface increases with the increase of water content and decreases with the increase of dry density and normal pressure. Both the three-parameter empirical model and Merchant element model can describe the creep characteristics of the reinforced soil interface. The Merchant model is more accurate in the early stage, while the three-parameter empirical model is more suitable for predicting the long-term creep deformation of the interface of compacted loess and geogrid.

Property of geotechnical materials has inherent uncertainty due to the complex formation process and inevitable test error. However, existing long-term deformation prediction methods for geotechnical structure such as a filling embankment are deterministic, which ignores the uncertainty of soil property. In this study, the uncertainty of creep behavior of compacted loess was investigated through repetitive creep tests and statistical analysis. Five different loading levels and two loading modes were considered in the tests. The creep test was repeated 45 times for each condition. Through a statistical analysis for the test results, a modified Merchant creep model was established to improve the accuracy of long-term deformation prediction. An empirical transformation equation between staged loading and separated loading mode of the creep test results was also introduced to improve applicability of the method. On this basis, a non-deterministic predication method for post-construction settlement of loess fill embankment was proposed. Furthermore, the proposed method was applied to the prediction of the post-construction of a 61.5 m loess filling embankment. The measured on-site post-construction settlement value falls within the 95% confidence interval of the predicted range which proves the efficiency and practicability of the proposed non-deterministic predication method. Compared to deterministic methods, the proposed method can describe the predicted deformation in a probabilistic way in the form of contour plot. The proposed method provides a basic approach for the probabilistic design and reliability assessment of filling engineering.