Abstract
Argillaceous siltstone's mechanical–chemical coupling damage mechanism under acidic solutions is
complex and crucial for engineering safety. This study selected typical argillaceous siltstone. Through immersion
tests, mechanical property tests, and micro- and mesostructure determination tests, along with equipment such as ion
concentration meters, scanning electron microscopy (SEM), and X-ray diffraction (XRD), the damage characteristics
in different acidic solutions were explored. The results show that acidic solutions trigger complex ion exchange in
argillaceous siltstone, significantly altering its mineral composition, mesostructure, and mechanical properties. The
H⁺ concentration notably affects cation release, and the increase in Ca concentration indicates the key role of
carbonate mineral dissolution in the ion exchange process. Stronger acidity and longer immersion lead to more
severe mesostructure damage and more obvious mechanical property degradation. Using the strain - equivalence
hypothesis and Weibull distribution, a damage statistical constitutive equation for rock samples under acidic solutions
and immersion time was established, clarifying relationships between model parameters and relevant factors. This
study reveals the intrinsic relationship among mineral composition changes, micro–mesostructural evolution, and
macroscopic mechanical degradation of argillaceous siltstone under acidic conditions, and clarifies its damage
evolution mechanism from a multi-scale perspective. The established model can effectively characterize the damage
evolution process of argillaceous siltstone.
Key Words
acidification; argillaceous siltstone; constitutive equation; damage mechanism; mechanical–
chemical coupling
Address
Ning Liang, Jingjing Zhang, Tao Jin: School of Civil Engineering and Architecture, Guangxi University of Science and Technology, No. 2
Wenchang Road, Chengzhong District, Liuzhou City, Guangxi, China;
Guangxi Zhuang Autonomous Region Engineering Research Center of Geotechnical Disaster and Ecological
Control, Guangxi University of Science and Technology, No. 2 Wenchang Road, Chengzhong District,
Liuzhou City, Guangxi, China
Abstract
This study presents a comprehensive vibration analysis of isotropic porous functionally graded material
(FGM) spherical shells using a refined higher-order shear deformation theory (HSDT). Four distinct porosity
distribution patterns even, uneven, logarithmic-uneven, and mass-density based are investigated to determine their
influence on the dynamic behavior of FGM shells. The material gradation is modeled using power-law,
trigonometric, and Viola-Tornabene four-parameter functions, while five different micromechanics models (Voigt,
Reuss, Tamura, Mori-Tanaka, and LRVE) are employed to calculate effective material properties. Analytical
solutions are obtained using Navier's technique for simply supported boundary conditions. The effects of gradient
index, porosity coefficient, radius of curvature, and vibration mode numbers on the fundamental frequencies are
systematically analyzed. Results reveal that each porosity distribution pattern uniquely affects the dynamic response,
with mass-density porosity showing the strongest positive correlation to frequency enhancement. The findings
provide valuable insights for designing FGM shell structures with tailored dynamic characteristics for various
engineering applications.
Address
Billel Rebai, Messas Tidjani, Touam Lakhemissi: Faculty of Sciences & Technology, Department of Civil Eng., University Abbes Laghrour, Khenchela, Algeria
Mustapha Meradjah: Department of Civil Engineering, Faculty of Technology, University of Sidi Bel Abbes, Sidi Bel Abbes, Algeria;
Multiscale Modeling and Simulation Laboratory, University of Sidi Bel Abbes, Sidi Bel Abbes, Algeria
Equipe Mixte "Nano-biomatériaux et Ingénierie Numérique pour des Applications Pharmaceutique", Agence
Thématique de Recherche en Sciences et Technologie (ATRST), Algeria
Abdelouahed Tounsi: Materials and Hydrology Laboratory, University of Sidi Bel Abbes, Faculty of Technology, Algeria;
Department of Civil and Environmental Engineering, King Fahd University of Petroleum & Minerals, 31261
Dhahran, Eastern Province, Saudi Arabia
Ayed Eid Alluqmani: Department of Civil Engineering, Faculty of Engineering, Islamic University of Madinah, Madinah,
Saudi Arabia
Jabr Aljedani, S.R. Mahmoud8: GRC Department, Applied College, King Abdulaziz University, Jeddah, Saudi Arabia
Mohammed A. Balubaid: Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah,
Saudi Arabia
Abstract
The study optimizes enzyme-induced carbonate precipitation (EICP) using crude soybean urease to
improve calcium carbonate yield and distribution for efficient, sustainable loess stabilization. The effects of
temperature (−10 - 50C), soybean powder concentration (30 - 150 g/L), and reaction time (3 - 48 h) on urease
activity were quantified using the phenol–sodium hypochlorite colorimetric method. Calcium carbonate yield and
distribution were evaluated through acid pickling under varying calcium sources (chloride, nitrate, acetate),
concentrations (0.5–2.0 mol/L), and spraying cycles (1–4). Results show that urease activity declines most sharply
within 3–12 h and calcium carbonate yield increases linearly with spraying frequency; calcium nitrate (16.1% at 0.5
mol/L), calcium chloride (9.2%), and calcium acetate (9.3%). Surface-layer precipitation decreases with additional
sprays, while middle and deep layers exhibit significant gains (chloride: 195.35%, nitrate: 219.32%, acetate:
224.56%). Calcium acetate is best for deep reinforcement, calcium chloride for rapid surface stabilization, and
calcium nitrate for balanced performance. Since measuring urease activity in water is easier and more accurate than
in soil, this study develops a regression model to predict soil urease activity from water-based tests. These findings
guide optimal material and method selection for effective EICP-based loess stabilization.
Address
Dequan Kong, Muhammad Muneer, Xinyang Liu,
Masood ur Rahman, Shihao Gu, Mouhong Wang: School of Civil Engineering, Chang'an University, Xi'an, 710061, China
Muhammad Shahbaz: College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, 518060, China
Abstract
Reinforced concrete retaining walls (RCRWs) are among the most widely used earth-retaining structures
in civil engineering. Traditional preliminary design of RCRWs relies on experience-based assumptions and iterative
procedures, which may result in suboptimal dimensions and inefficient material utilization. Existing artificial
intelligence (AI)-based research predominantly focuses on safety factor estimation, but accurately estimating crosssectional
dimensions is more critical to achieve an efficient design. This study presents a novel data-driven
framework combining differential evolution algorithm (DEA) with artificial neural network (ANN) model
evaluation for predicting optimal cross-sectional dimensions of RCRWs on strong soils. Unlike previous studies, this
research systematically explores four distinct ANN model structures and examines the effects of the variation in the
selected output parameters on the performance of the models. A comprehensive dataset comprising 175 optimally
designed RCRWs was generated via DEA, considering wall height, surcharge load, and backfill internal friction
angle as key variables. Systematic model comparisons revealed that Model-1 achieved the best performance (R2 =
0.9997, MaxAE = 0.0285 m). Additionally, SHAP (SHapley Additive exPlanations) analysis was conducted to
interpret the decision-making process of the model, confirming that the predictions are physically consistent with
geotechnical design principles. Comparative analyses with traditional and regression-based approaches revealed that
the ANN model consistently outperforms existing methods in both accuracy and generalization capability. This work
provides near-optimal initial dimension estimates by eliminating traditional iterative design inefficiencies. The results
show that intelligent preliminary design can achieve professional-level accuracy and that it is more effective to shift
from traditional approaches to AI-based approaches in geotechnical design applications.
Address
Ugur Dagdeviren, Burak Kaymak: Department of Civil Engineering, Faculty of Engineering, Kutahya Dumlupinar University, Kutahya, Türkiye
Emre Gungor: Department of Computer Engineering, Faculty of Engineering and Natural Sciences,
Kutahya Health Sciences University, Kutahya, Türkiye
Abstract
Wide-graded soil-rock mixtures (SRMs) are heterogeneous geotechnical materials with rock sizes from
millimeters to meters. The stability of embankments constructed with SRMs are largely affected by the controlled
minimum void ratio (emin). To investigate SRM deformation mechanisms, scaling methods such as equivalent
substitution, scalping, hybrid, and similar grading are commonly employed to downscale SRMs In laboratory tests.
Due to scale effects, laboratory-determined emin values differ from those measured in situ. Moreover, the quantitative
expression incorporating scale effects on the emin of wide-graded SRMs is not available yet. Thus, systematic
laboratory compaction tests were performed. Results indicate that SRM compaction performance is governed by the
interaction between voids formed by coarse particles and the content of fine particles (i.e., particle size smaller than 5
mm). The equivalent substitution method yields the highest emin values resulting from a greater proportion of coarse
particles, whereas the scalping method results in the lowest emin owing to a higher fine particle content. The emin
decreases with increasing fine content when fine content is below 20%. A semi-empirical model was developed to
capture the effects of particle gradation and maximum grain size on emin. Validation against existing datasets
demonstrates the capability of the proposed empirical model to predict prototype-scale emin values with limited data.
Key Words
minimum void ratio; original gradation; scale effect; scaling method; soil-rock mixture
Address
Caifeng Zhu, Jungao Zhu, Tao Wang: Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, Hohai University,
Xikang Road, Nanjing 210024, China
Sheng Su: China Southern Power Grid Energy Storage Corporation Limited, Fenghuang Road,
Wenshan 663099, China
Xuan Liu: Powerchina Zhongnan Engineering Corporation Limited, Xiangzhang East Road, Changsha 410014, China
Wanli Guo: Geotechnical Engineering Department, Nanjing Hydraulic Research Institute, 223 Guangzhou Road,
Nanjing 210024, China
Abstract
Collapse of particle-liquid mixtures generally occurs during geological hazards, such as debris flows,
landslides and dam breaks. Based on the developed three-dimensional coupled DEM-LBM incorporating both the
free-surface flows and non-Newtonian fluids, the collapsing process of particle-liquid mixtures is simulated, and the
numerical results are compared with experimental tests. Furthermore, the influences of initial column aspect ratio,
interstitial liquid type and initial saturation level on the granular collapse are investigated. The results show that the
numerical simulation by the three-dimensional coupled DEM-LBM can well reproduce both the motion
characteristics of particles and liquid during the collapsing process of particle-liquid mixtures. Compared with the dry
granular column collapse, the particles in the particle-liquid mixtures can reach a longer final runout distance. The
particle front in particle-water mixtures eventually travels a longer distance than that of the particle-mud mixtures.
With a higher initial granular column and a higher initial saturation level, the interstitial liquid has a more significant
effect on the particle fluidity. As for a granular column with an initial aspect ratio of 2.46 and a saturation level of 1.0,
compared with the dry granular collapse, the particle runout distance of particle-water mixtures increased by 27.7%,
and that of particle-mud mixtures increased by 13.3%. The present study provides a scientific basis for understanding
the solid-liquid interaction mechanism and assessing the disaster risk.
Address
Lei Jin, Jingjing Li: College of Civil Engineering, Jiangsu Open University, Jiangsu, China
Wenjie Xu: State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, China
