Abstract
The foundation treatment method of dynamic compaction (DC) is widely used to densify and strengthen
coarse-grained fills. Particle breakage inevitably occurs during DC impact, significantly influencing densification
mechanisms and reinforcement efficiency. However, few DC studies explicitly consider particle breakage effects. This
study employs PFC3D (Particle Flow Code in 3 Dimensions) to simulate particle breakage in coarse-grained fills under
impact loading. Comparative DC tests were conducted on breakable and unbreakable fills to analyze their cross-scale
responses: Macro-scale (crater depth) and Meso-scale (porosity distribution, displacement field, particle contact
characteristics). Results indicate that particle breakage initially reduces crater depth and compactness. However, with
repeated tamping, breakage promotes compaction and enhances DC reinforcement efficiency. Early-stage breakage
consumes tamping energy, reducing its immediate densification effect. As tamping progresses, breakage diminishes,
and the resulting finer particles fill voids between larger particles, facilitating compaction under subsequent impacts.
These findings provide novel insights into the physical and mechanical behavior of breakage-driven compaction in
coarse-grained fills under impact loading, supporting optimization of DC technology.
Key Words
coarse-grained soil; densification characteristics; discrete element method; dynamic compaction;
particle breakage; reinforcement mechanism
Address
Xi Li, Kun Liu, Guoping Qian,Wenli Hou, Junfeng Qian Zhao: School of Transportation, Changsha University of Science and Technology, Changsha 410114, China
Shuaituan Tian, Xinyan Ma: Civil Aviation Research Base (Beijing) Co., Ltd., China
Huangting Zheng: Radiation Environment Supervision and Management Station of Guangxi Zhuang
Autonomous Region, China
Zhao Qian: The Second Veteran Hospital of Shandong Province, Taian, 271000, China
Abstract
The presented study attempted to propose enhanced rainfall-induced landslide susceptibility mapping
method by using the Deep Feedforward Neural Network (DFNN) which is developed for analysis the non-liner
feature detection in landslide susceptibility analysis. To evaluate our approach, a comprehensive dataset of triggering
factors was compiled, encompassing historical landslide occurrences with total of 107 records, rainfall data,
geological information, seismicity, human-activities, and topographic attributes. Through rigorous training and
testing procedures, the DFNN demonstratedsuperior ability for generalization and superior performance. The
effectiveness of the selected method is demonstrated on the data from the Zanjan County, known for its diverse
geographical, geological, and hydrological characteristics, which are pivotal factors in mapping of landslide
susceptibility. Results showcased a substantial enhancement in the accuracy of mapping of rainfall-induced landslide
susceptibility for the Zanjan County, which is compared with benchmark learning classifiers. According to the results
of the study, it appeared that the northeastern and southwestern area of the Zanjan County can be deemed to have a
high to very-high risk of landslide occurrence, which is validated via benchmark classifiers. The western part of the
Zanjan County was observed to have a very low to low risk.
Key Words
DFNN; geohazard; landslide susceptibility; machine learning; rainfall-induced landslide
Address
Licai Zhu, Dong Jian, Yaser A. Nanehkaran: 1School of Artificial Intelligence, Yancheng Teachers University, Yancheng 224002, Jiangsu, China
Tolga Pusatli: Department of Management Information Systems, Çankaya University, Ankara, Türkiye
Amila Akagic: Department of Computer Science and Informatics, Faculty of Electrical Engineering, University of Sarajevo,
Sarajevo 71000, Bosnia and Herzegovina
Elkhan Mahmud: International School of Economics, Azerbaijan State University of Economics, Baku 6, Azerbaijan
Abstract
This paper investigates the combined buckling and bending response of sandwich plates composed of
bidirectional functionally graded (BDFG) face sheets and a metallic foam core, resting on a partial elastic foundation.
Unlike previous studies limited to single load types, the present work considers simultaneous in-plane axial loads
(compressive/tensile) and transverse loads inducing out-of-plane bending deformation. Furthermore, multiple
boundary conditions including simply supported, clamped, free, and mixed edge restraints are systematically
examined to reflect realistic support scenarios. The BDFG face sheets possess material properties that vary
continuously in both in-plane (x,z) directions, while the metallic foam core follows a porosity-dependent mechanical
distribution. A quasi-3D shear deformation theory is employed to formulate the governing equations. The principle of
virtual work is used to derive the equilibrium equations, which are subsequently solved using an analytical solution
method. After validating the present formulation against benchmark results, an extensive parametric study is
conducted to assess the influence of key parameters: foam porosity coefficient, bidirectional gradation indices, partial
foundation stiffness and location, in-plane to transverse load ratio and the type of boundary support. Results reveal
that the interplay between combined loading, foundation partialization, and edge restraints significantly alters the
critical buckling load and maximum transverse deflection. The proposed model provides a robust design tool for
lightweight sandwich structures in aerospace, civil, and marine engineering applications where non-uniform support
and combined loading are prevalent.
Address
Mohamed Sekkal: Ahmed Zabana University, Relizane, Algeria;
Department of Civil Engineering, Material and Hydrology Laboratory, University of Sidi Bel Abbes,
Faculty of Technology, Algeria
Rabbab Bachir Bouiadjra, Samir Benyoucef: Department of Civil Engineering, Material and Hydrology Laboratory, University of Sidi Bel Abbes,
Faculty of Technology, Algeria
Wafa Adda Bedia: Laboratoire de Modélisation et Simulation Multi-échelle, Université de Sidi Bel Abbés, Algeria
Abdelouahed Tounsi: Department of Civil Engineering, Material 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
Abstract
To address the limitations of Gaussian process regression (GPR) in predicting ground surface settlement
induced by foundation pit dewatering, namely inadequate predictive accuracy, limited generalizability, and reliance
on empirical kernel selection, this study proposes a novel predictive framework, FOA-GPR. The model employs the
fruit fly optimization algorithm (FOA) to optimize the weight allocation of a composite kernel comprising radial
basis function (RBF) and Matérn kernels, thereby capturing complex, nonlinear spatiotemporal dynamics. Using six
multidimensional features, including water table drawdown, cutoff curtain depth, and permeability coefficient, the
proposed FOA-GPR framework is systematically evaluated against conventional machine learning models (BPNN,
SVM, and GPR) as well as an Empirical Spatial Attenuation Model (ESAM). The results show that FOA-GPR
delivers outstanding predictive performance, reducing the mean absolute error (MAE) by 48.2% and 76.8% relative
to BPNN and ESAM, respectively, and lowering the mean squared error (MSE) by 83.8% compared with BPNN. In
addition, the model achieves a high coefficient of determination (R2) of 0.975 and improves peak probability density
by 14.3% over standard GPR. Finally, Shapley Additive Explanations (SHAP) analysis indicates that water table
drawdown and spatial distance to the sump well are the dominant mechanical drivers of settlement. Overall, the
proposed framework demonstrates superior accuracy, robustness, and physical interpretability, offering a highly
reliable tool for risk management in deep excavation projects.
Key Words
foundation pit dewatering; fruit fly optimization algorithm; gaussian process regression; ground
surface settlement
Address
Guangyin Wang: School of Materials Science and Engineering, Shenyang University of Technology, Shenyang,
Liaoning 110807, China;
Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang,
Liaoning 110807, China;
School of Architecture and Civil Engineering, Engineering, Shenyang University of Technology, Shenyang,
Liaoning 110807, China
Yang Yu,Chunwei Zhang: Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang,
Liaoning 110807, China;
School of Architecture and Civil Engineering, Engineering, Shenyang University of Technology, Shenyang,
Liaoning 110807, China
Haixia Sun: School of Architecture and Civil Engineering, Engineering, Shenyang University of Technology, Shenyang,
Liaoning 110807, China
Abstract
In this research, the similar materials of rock that could be used to construct a large-scale test model by
using pouring method were investigated using the orthogonal experimental design, and a model of anchored rock
slope containing a weak layer was poured for the first time by casting a specimen followed by excitation on a largescale
shaking table. By doing so, the shear effects on two anchorage interfaces of a rock slope containing a weak
layer under the effect of seismic waves with different types, amplitudes, and excitation directions, and the influence
of ground-motion parameters on the shear effect were investigated. The results showed that the shear effect first
appeared on the grout–rock interface under seismic action, which caused deformation of the grout layer and the shear
effect on the bolt–grout interface; the peak shear stresses on the two anchorage interfaces of a rock slope increased
with increasing amplitude of input seismic waves and their rate of growth increased therewith; under the effect of
different types of seismic waves, the shear effects on the two anchorage interfaces of a rock slope showed disparity, in
which the peak shear stresses on the two anchorage interfaces were maximised under the effect of sinusoidal waves;
in different directions of seismic excitation, there were also different shear effects manifest on the two anchorage
interfaces: the peak shear stresses on the two anchorage interfaces when seismic waves were excited in the Zdirection
alone were lower than those under seismic excitation in the X-direction alone. The influence of seismic
excitation in the X and Z-directions combined on peak shear stresses on the two anchorage interfaces was closely
related to the type of seismic wave input. The research revealed the anchoring mechanism of the rock slope under
seismic action, which is expected to guide related theoretical research, experimental research, numerical simulation,
and seismic design.
Key Words
anchorage interface; anchored slope; ground-motion parameters; shaking table model test;
similar material of rock; peak interface shear stress
Address
Zhe Long,Juyun Zhai, Fei Liu,Mengfei Liang, Hailing Liao, Haoming Yang: School of Civil and Transportation Engineering, Henan University of Urban Construction,
Pingdingshan 467036, China
Baojian Liu: SCIVIC Engineering Corporation, Luoyang 471000, China
Zhixin Yan: School of Civil Engineering and Mechanics, Lanzhou University, Lanzhou 730000, China
Abstract
The excavation of deep foundation pits may cause surrounding strata subsidence and pipeline leakageinduced
soil loss. This study used a global transparent soil pipe-soil-water coupling test system to investigate the soil
penetration erosion failure process after pressurized pipe interface leakage. A numerical model was established on the
EDEM-Fluent platform, integrating foundation pit stress fields and pipeline deformation to simulate soil fluidization
triggered by pipeline damage. The effects of fine particle content (FPC), pipe buried depth ratio , and pipe flow rate
on permeability failure were explored. Mechanisms of fine particle migration and skeleton failure were revealed via
force chain network and fine particle bearing ratio evolution. Results show EDEM-API and Fluent dynamic mesh
enable cross-scale finite-discrete element simulation. Soil with 15% FPC exhibits strong erosion resistance. Larger
BDR and lower PFR delay erosion failure.
Key Words
coupling analysis; cross-scale; infiltrati on erosion; particle transport
Address
Jinghan Yuan: Zhejiang Engineering Research Center of Intelligent Urban Infrastructure,Zhejiang University City College,
Hangzhou 310015, China;
School of Civil and Transportation Engineering, Hohai University, Nanjing 210024, China
Xiaodong Ni: School of Civil and Transportation Engineering, Hohai University, Nanjing 210024, China
Dong Zhao: CR17 BG Municipal Construction Co., LTD., Wuxi 214000, China
