bluuebunny/crossref_metadata_2025_split huggingface dataset:
Numerical parametric study of embedded geosynthetic-reinforced soil abutment
Authors: Zuo Binli , Xu Chao 
Published: May 2024

Abstract
Three-dimensional numerical analysis was conducted to investigate the performance of embedded geosynthetic-reinforced soil abutments. The numerical model was validated using field-monitoring data. A parametric study was conducted to investigate the key factors affecting the performance of the embedded abutment under the working conditions, including the pile offset distance, pile diameter, pile spacing, and bridge load. The piles exhibited a retaining rather than a pushing effect in these cases due to the relatively high stiffness and negative skin friction of the piles, thus reducing the loads exerted on the facing wall. The lateral displacement of the facing wall significantly increased as the pile offset distance decreased mainly due to the reduced anchor length of the reinforcement and enhanced retaining effect of the piles. As the pile diameter increases, the deflection of the facing wall is constrained by factors such as increased bearing width, limited displacement, and enhanced retention effect of thepiles. This is an efficient way to increase the pile density near the abutment centerline to control thedeformation of the facing wall. An increase in the vertical bridge load resulted in a decrease in negative skin friction, leading to increased foundation settlement and lateral displacement of the facing wall.
Prediction and optimization of heat extraction in ground source heat pump system using surrogate model
Authors: Chaoran Wang , Chanjuan Han 
Published: May 2024

Abstract
Ground source heat pump (GSHP) system, which extracts underground heat for building heating and cooling, uses renewable energy to decrease CO2 emissions for the goals of carbon peaking and carbon neutrality. Hence, the prediction and optimization work is significate especially for existing GSHP system. This study focuses on the prediction of heat extraction and optimization of operational strategy based on a three-story house installed with GSHP system located in Cleveland, Ohio, USA. Firstly, the numerical model is built to simulate operation of ground heat exchanger (GHE). Then, Latin hypercube sampling (LHS) is employed to generate different parameter combinations that are calculated to get the heat extraction results for training XGBoost-based surrogate model. Finally, the particle swarm optimization (PSO) is used to realize the optimization of operational strategy. The results indicate that the numerical model can simulate the GHE well, and the accuracy of surrogate model can reach 0.990. The COP of GSHP system is maximized to protect the soil heat balance and reduce the electrical energy consumption. The optimization method also makes it possible to evaluate the heat extraction capability, and prepare for the extra heat demand in advance.
Phosphate removal by acid-activated and chitosan-composite clayey soils
Authors: Verma Gaurav , Reddy Krishna R 
Published: May 2024

Abstract
Phosphate is a vital nutrient for aquatic organism growth, but excessive levels can lead to eutrophication, with detrimental effects on receiving water bodies. Adsorption using clay soils can address phosphate release and has gained attention owing to its cost-efficiency and environmental friendliness. To overcome the limited phosphate adsorption capacity of clay soils due to surface charges, the combined effects of acid activation and composite formation with chitosan were investigated. Kaolin and bentonite were selected for acid activation, while chitosan varieties with low, medium, and high molecular weights were chosen for composite formation. Batch tests were individually conducted on acid-activated clay soils and composites of acid-activated clay soils with chitosan over 24 h, varying the initial phosphate concentrations. For acid activation, clay soils were treated with 6N hydrochloric acid, whereas composite formation was achieved via combination with each chitosan variant. Acid-activated kaolin and bentonite individually exhibited phosphate removal efficiencies ranging 99.8–79.8% and 97.1–47.2%, respectively. Among their respective composites, acid-activated kaolin with medium-molecular-weight chitosan and acid-activated bentonite with low-molecular-weight chitosan had the highest phosphate removal efficiencies. Therefore, in contrast to kaolin, acid activation or composite formation of acid-activated bentonite with chitosan can improve its phosphate removal efficiency.
Influence of extraction and injection conditions on the performance of thermal-enhanced soil vapor extraction
Authors: Yang Chun-Bai-Xue , Feng Shi-Jin , Zheng Qi-Teng 
Published: May 2024

Abstract
Thermal-enhanced soil vapor extraction (T-SVE) is an in-situ remedial technique that can address the limitations of contaminant mass transfer and soil permeability. The relative humidity of the injected air, heating temperature of the thermal well, extraction pressure, and extraction mode are important design parameters of a T-SVE system that determine the temporal and spatial distributions of soil temperature and removal rate. Using a thermal–hydraulic–chemical coupled model with the interphase mass transfer of contaminants and water evaporation/condensation, this study investigated the effects of relative humidity, heating temperature, extraction pressure, and extraction mode on T-SVE performance. The results showed that with an increase in relative humidity, the heating temperature of the contaminated soil significantly improved; however, the removal rate of contaminants decreased, particularly in the late stage of remediation. Considering the heating temperature of the thermal well, a critical air relative humidity value that significantly affects the removal rate of contaminants was observed. The heating temperature of the thermal well must be considered in engineering design; however, the evaluation of the relative humidity of the air cannot be ignored.
Cavern spacing of CAES cavern group in hard rock based on plastic zone and deformation analysis
Authors: Sun Zeyuan , Zhao Cheng , Qian Yuan , Xing Jinquan , Niu Jialun 
Published: May 2024

Abstract
Existing research on layout parameters of compressed air energy storage (CAES) cavern groups in hard rock has indicated that the key influential factors for group stability include buried depth, diameter, and cavern spacing. Among these, in contrasting to other factors, the research on cavern spacing is limited. This paper focuses on the issue of safe spacing and proposes a numerical method to determine it. By establishing a series of finite element models, the stress state of surrounding rock, changes in shape, equivalent plastic strain of plastic zone, and cavern wall deformation under different spacing have been studied. When a group of air storage caverns in rows is subjected to high internal pressure, for a small spacing, the plastic zone of the surrounding rock between two caverns is connected. Here, the size of the plastic zone, equivalent plastic strain value, and deformation of the cavern wall are relatively large, making the surrounding rock highly susceptible to damage. As the spacing increases, the horizontal width of the plastic zone, the maximum equivalent plastic strain value, the displacement changes of the cavern wall remain stable. This spacing is virtually the same as that when the plastic zone of the surrounding rock is no longer connected and can be selected as a safe spacing. This study explores the design of cavern safe spacing of underground CAES cavern groups and discusses several influencing factors, which can provide references for follow-up energy storage engineering and research.
Numerical simulation of fingering in the underground hydrogen storage
Authors: Ren T , Shen X , Zhang F 
Published: May 2024

Abstract
Underground hydrogen storage has garnered interest in recent years owing to the considerable demand for clean energy. Hydrogen is more diffusive than air and has relatively low density and viscosity. These unique properties induce distinct hydrodynamic phenomena during hydrogen storage. Cushion gas has been proven to be a potential remedy for attenuating the adverse impacts of underground hydrogen storage. To investigate the influence of the cushion gas, a microscopic numerical simulation was performed with Fluent software using the Eulerian multi-fluid model. Carbon dioxide, nitrogen, and methane are usually used as the preferred candidates for cushion gases in underground hydrogen storage systems. In this study, nitrogen was used as the cushion gas and was injected along with hydrogen into heterogeneous porous media with volume fractions ranging from 0% to 70%. A parameterization study was then performed to elucidate the influences of the injection rate and viscosity of the fluid on the fingering pattern. Two representative types of fingering, viscous fingering and capillary fingering, were observed under different gas mixtures and boundary conditions. After the simulation, an image analysis was performed to capture the evolution of the fingering pattern. The specific fingering area, number of branches, and fractal dimensions are proposed as geometric indices to describe the shape of the fingering pattern. The results showed that there was a remarkable enhancement in saturation due to the injection of the cushion gas, depending on the concentration of the gas mixture. This study offers insight on the design of gas mixture injection in underground hydrogen storage and can be further extended to the hydrochemo–mechanical coupled numerical simulation of multiphase gas injection in porous media.
Investigating combined effects of saturation–desaturation cycles and cyclic stress resistance of reinforced biopolymer-treated soil
Authors: Aneke Frank I. , Kalumba Denis 
Published: May 2024

Abstract
Throughout their service life, subgrades endure significant stress from cyclic traffic and seasonal moisture fluctuations. This study aims to evaluate the moisture variation and cyclic stress resistance of reinforced and biopolymer-treated soils, which were treated with varying percentages (0.25%, 0.5%, 0.75%, and 1.0%) of xanthan gum (XG) and sisal fiber, to determine the level of tolerance a subgrade can sustain. Wetting–drying (W-D) cycle tests, unconfined compressive strength (UCS) tests, and dynamic resilient modulus (DRM) tests were conducted to assess the resistance of the treated soils to moisture variation and cyclic stress. The findings indicate that biopolymer-treated specimens retained over 95% of their original mass after 15 cycles, whereas fiber-reinforced soil exhibited a 9.1% loss in mass. Furthermore, the DRM of the reinforced soil improved, demonstrating greater resistance to cyclic stress compared to biopolymer-treated soils. Fiber-reinforced soils exhibited strain-hardening responses at low cyclic stress levels and maintained stress tolerance even at high cyclic stress levels without signs of strain deformation. Conversely, the UCS of the biopolymer-treated soil surpassed that of the fiber-reinforced soil due to the brittleness of the specimens.
Slope stability analysis of cement and silica fume stabilized expansive soil slope
Authors: Chiu Abraham C.F. , Mamoudou Ibrahim Moumouni , Al-Ajamee Mohammed , Mary Baguma Mbabazi 
Published: May 2024

Abstract
The objective of this study was to assess the impact of chemical stabilization on slope stability using water retention test data. The analysis focused on hypothetical expansive soil with a 10 m height and a 34° angle. Two critical unsaturated hydraulic properties were considered: (1) soil water retention curve and (2) hydraulic conductivity function. Chemical stabilizers were introduced at varying percentages relative to cement content, with silica fume (SF) partially replacing portions of the cement. The influence of stabilizers on slope stability was evaluated by examining changes in the factor of safety (FOS) with different stabilizer concentrations. Generally, the FOS increased with higher cement content, with further improvement observed when cement was partially replaced by silica fume. Additionally, the addition of silica fume enhanced water retention capacity, as evidenced by altered pore pressure distribution and water table levels post-rainfall infiltration.
Probabilistic three-dimensional finite element analysis of geosynthetic-reinforced pile-supported embankments
Authors: Agarwal E , Luo N 
Published: May 2024

Abstract
The current study focuses on a probabilistic three-dimensional finite element analysis of geosynthetic-reinforced pile-supported embankments. The probabilistic analysis utilizes a finite element technique and was executed using Python scripting in Abaqus. A deterministic model is established for both unreinforced and geosynthetic-reinforced pile-supported embankments, employing a basic linear elastic perfectly plastic Mohr-Coulomb constitutive model. This numerical model adopted a simplified methodology that eliminates the need to simulate actual piles and subsoil. The results are validated across different embankment heights and mesh sizes. Based on results from Monte-Carlo simulations, we aimed to show how results can vary even by considering only one random variable. Probability density functions for various parameters, including pile efficacy, maximum differential settlement, and maximum strain in the reinforcement, are shown. The findings illustrate the ability of probabilistic approaches to predict the most likely occurrences for different governing parameters of geosynthetic-reinforced pile-supported embankments. They also highlight the potential of probabilistic approaches to expand further into a full-scale comprehensive study which can offer deeper insights into the stability of these embankments considering various sources of uncertainties.
Design, construction, and monitored behavior of two large-diameter cylindrical diaphragm walls on an offshore reclamation island
Authors: Zhai J Q , Wang Q , Wang S S , Zhang Z B , Wan J C 
Published: May 2024

Abstract
Two semi-underground liquefied natural gas (LNG) storage tanks with an excavation depth of 25.6 m were constructed on an offshore reclamation island in Dapeng Bay, Shenzhen, China. Two cylindrical retaining diaphragm walls with a diameter of 100 m were adopted for the deep excavation. This paperpresents detailed information on the design and construction of these two cylindrical retaining diaphragm walls. As the elevation changes in the soil and rock layers were very severe, the non-axisymmetric distribution of the lateral pressure was significant. Diaphragm walls with different embedded depths were designed to cut off the seepage paths inside and outside the retaining structure. The strength of the rock was extremely high; therefore, a combination of predrilling using a rotary excavator and grooving using a milling machine was adopted in the trenching construction of the diaphragm wall. The monitored soil and rock pressures acting on the diaphragm wall are discussed. The lateral deformation of the diaphragm wall was described, and the hoop force and vertical bending moment of the retaining diaphragm wall were analyzed, which could be a reference for similar projects.
Research and engineering practice of 70-m long-distanceaxial-force servo steel support technology
Authors: Xie Xiaolin , Fang Yingang , Jia Jian , Zhai Jiequn , Yang Ke 
Published: May 2024

Abstract
For deep excavation projects in areas with soft soil and a sensitive surrounding environment, an axial force servo steel support is usually adopted to control the deformation of retaining structures; however, the traditional axial force servo steel support can only be applied to deep excavations with a width of approximately 20 m owing to its technological limitations. In this study, considering the deep excavation project of Shanghai Xuhui Riverside Area A as an example, relevant technical research on a 70-m long-distance axial force servo steel support was conducted and the effect of this steel support in engineering practice was analyzed. Through the successful application of the project, the engineering safety reliability and deformation control effectiveness of the 70-m long-distance axial force servo steel support were verified, which can provide a reference for the design of similar projects.
Mechanical performance and application of retaining structures doubling as permanent structures in deep excavations
Authors: Xie Jinli , Xu Qianwei , Yan Hui , Han Yufeng , Lu Linhai 
Published: May 2024

Abstract
This study examines the effectiveness of the prefabricated pile-wall composite retaining structure (PW-CRS), which is a cutting-edge construction technique that combines prefabricated retaining piles with internal structural walls and is being increasingly utilized in excavation engineering projects. To comprehensively assess its performance, the research adopts a simulated loading approach, using equivalent concentrated loads to mimic the actual conditions faced by the PW-CRS. The experimental analysis highlights inclined tension failure as the predominant form of structural failure, accompanied by extensive cracking in both the pile and wall components. Additionally, the study points to slip failure of the laminated surfaces as a significant factor influencing the load-bearing capacity of the structure. The strain assessments on both concrete and reinforcement elements further enrich the findings. This investigation enhances the understanding of the structural dynamics of the prefabricated PW-CRS and offers valuable insights for its effective application in excavation engineering projects.
Mechanism of the piping phenomenon in a granite residual soil foundation pit
Authors: Zhao Yu , Wang Chengfeng 
Published: May 2024

Abstract
In the construction of a subway foundation pit in a granite stratum, soil gushing in the foundation pit and a large settlement of the surrounding strata can occur. To explore the mechanism and treatment methods, in this study, seepage tests and numerical simulations of the granite residual soil were conducted. The results showed that the main reason for foundation settlement was that the residual soil pipes weakened under a large hydraulic slope. According to the research conclusion, a method of controlled pumping of foundation pits was proposed, and subsequent construction achieved good results.
Prediction of dredged soil settlement based on improved BP neural network
Authors: Li P P , Li J P , Liu G Y , Zhou P 
Published: May 2024

Abstract
Based on the measured settlement data from the Reclamation Project of Eastern Hengsha Shoal, two improved BP neural networks optimized by the particle swarm optimization (PSO) algorithm and genetic algorithm (GA) respectively are employed to predict the long-term consolidation settlement of the dredged soil in the reclamation area. By comparing the prediction results of the improved neural networks with those obtained from standard BP neural network and various curve fitting methods including hyperbola method, Hoshino method, and Asaoka method, as well as field-measured data, the improved neural network methods are validated to have a better predictive performance. The error analysis results indicate that compared with the standard BP neural network and curve-fitting prediction methods, the improved BP neural networks exhibit smaller prediction errors in terms of MAE, MSE, RMSE, MAPE, etc., and have a closer match between the predicted settlement values and the actual values. The selected GA-BP and PSO-BP neural network prediction methods in this study demonstrate higher prediction accuracy. Meanwhile, GA-BP demonstrates higher prediction accuracy and generalization ability than PSO-BP, but PSO-BP shows faster convergence in terms of prediction speed. The preferred methods presented in this paper exhibit good robustness and high reliability, which can achieve accurate prediction of dredged soil settlement. Additionally, unlike the traditional curve-fitting methods, the selected methods can effectively reduce the influence of subjective factors during the settlement prediction, and provide a new approach for settlement prediction, which can be effectively utilized as a substitute for long-term settlement monitoring and give reference for the design of settlement control of foundation of further construction projects.
Determining the number of soil layers using the surface wave method based on decision tree and random forest
Authors: Zhou Zan , Lok Thomas M H 
Published: May 2024

Abstract
The surface wave method is an efficient non-invasive technique to infer shear wave velocity profiles and has wide applications in civil engineering, earthquake engineering, and geophysics. Inversion analysis is a key step in this method. However, inferring the soil layer number is a challenging problem in inversion analysis and needs improvement. Machine-learning algorithms, including decision tree and random forest, are used in this study to infer the number of soil layers based on the dispersion curve of the surface wave. This process includes two steps for obtaining the model. In Step 1, a large set of synthetic dispersion curves is produced, and in Step 2, a model using machine-learning algorithms is trained based on the synthetic data. The analysis results showed that the obtained model could gain more than 75% accuracy in the test data. Comparing the results from the two machine learning models indicated that the random forest method produced a more favorable prediction. Furthermore, investigating the importance of features implied that the longest and shortest wavelengths were more important than other features.
Data-driven approach for intelligent tunnel dust concentration prediction
Authors: Yang Tongjun , Wu Chen , Chen Jiayao , Zhou Mingliang , Huang Hongwei 
Published: May 2024

Abstract
The precise evaluation of tunnel dust concentration (TDC) stands as a primary concern within engineering practices. However, comprehensive and accurate prediction of TDC becomes increasingly challenging due to the expanding multi-source datasets and cumulative errors. This study employs innovative data-driven ensemble learning methodologies, specifically random forest (RF) and gradient boosting regression tree (GBRT), to delineate the intricate relationships between TDC values and various rock tunnel characteristics. Five diverse variables sourced from multiple origins are examined and employed as inputs within the database, while the TDC values derived from the dust concentration meter serve as the target outputs. Subsequently, a Bayesian optimization approach, the Tree-structured Parzen Estimator (TPE), is introduced to automatically ascertain the optimal hyper-parameters for the ensemble models. A comprehensive comparison is conducted between the two ensemble learning models and a singular machine learning algorithm, the classification and regression tree (CART), concerning predictive accuracy and resilience via 10-fold cross-validation (CV). The findings reveal the superior performance of the hybrid ensemble learning models over the individual ML models. Notably, the TPE-GBRT algorithm adeptly captures the measurement evolution, showcasing the lowest prediction errors and the highest correlation coefficient. These predicted outcomes significantly contribute to enhancing the engineering comprehension of the interrelation between rock tunnel parameters and TDC values.