Enzyme-induced carbonate precipitation (EICP) is a potential ground improvement method that can reduce the permeability of sands. However, the traditional mathematical models are hard to accurately predict the permeability of EICP-treated sands. In this study, the mathematical model was established for predicting the permeability of EICP-treated sands based on Kozeny-Carman equation. The effects of calcium carbonate precipitation on the porosity, tortuosity, and specific surface area of the EICP-treated sands were considered in the model. To validate the model, the bio-cemented sand column tests with different grain size distributions (coarse, medium, and fine sands) and treatment numbers (6, 8, and 10 times) were conducted. The calcium carbonate content (CCC) and permeability of EICP-treated sands were measured. The validation of the model was confirmed through a comparative analysis of theoretical and experimental results. Furthermore, the impacts of porosity, particle size, CCC, and specific surface area on the hydraulic conductivity of EICP-treated sands were analyzed. The results showed that the model can reflect the hydraulic conductivity of EICP-treated sands under different particle size distributions and degrees of cementation, demonstrating broad applicability. Parametric analysis indicated the hydraulic conductivity gradually decreases with increasing CCC and specific surface area. Conversely, the hydraulic conductivity gradually increases with increasing porosity (n) and particle size (d50), with porosity exhibiting a significantly higher sensitivity than particle size. In summary, this study contributes theoretical foundations for the practical implementation of EICP technology in reducing soil permeability.

Geometric incompleteness is a common issue in rock mass point clouds acquired from field measurements using non-contact techniques such as laser scanning, where occlusions and complex terrain conditions lead to missing and fragmented surface data. This problem significantly affects the reliability of discontinuity characterization in rock engineering applications. This paper proposes a novel strategy for discontinuity characterization from incomplete rock mass point clouds. In this strategy, a geometry-aware diffusion-based point cloud completion method is introduced to reconstruct missing surface regions caused by measurement limitations, restoring structural continuity prior to discontinuity interpretation. On the reconstructed point clouds, a topology-aware fuzzy C-means (TA-FCM) clustering method and an adaptive density-based spatial clustering of applications with noise (adaptive DBSCAN) algorithm are developed and integrated for accurate discontinuity extraction. A case study is conducted using both benchmark datasets and field-acquired rock slope point clouds to evaluate and validate the proposed strategy. The results show that, compared with Poisson reconstruction and GeoFormer, the proposed method reduces the unidirectional Chamfer distance from 3.74 × 10−3 and 1.07 × 10−2 to 4.1 × 10−5, and reduces the unidirectional Hausdorff distance from 1.79 × 10−1 to 4.7 × 10−2, indicating improved geometric consistency of reconstructed rock surfaces. Furthermore, orientation estimation results under real measurement conditions show a mean deviation of approximately 5° compared with manual measurements. Validation results demonstrate stable performance in discontinuity extraction from incomplete point clouds. The proposed strategy provides a practical workflow for structural interpretation and discontinuity characterization from incomplete rock mass point clouds acquired under real field conditions.

Accurate identification of debris-flow events is essential for reliable seismic-based monitoring and early-warning systems in mountainous regions. This study proposes an optimized Random Forest (RF) model for debris-flow signal identification, with an emphasis on robustness, interpretability, and applicability across diverse geomorphological settings. A global seismic dataset containing historical debris-flow events from 12 representative regions is used to capture variations in debris-flow types, triggering mechanisms, and environmental conditions. To reduce feature redundancy and enhance physical interpretability, the Boruta algorithm is applied to select five key discriminative features from an initially high-dimensional feature space. Bayesian hyperparameter optimization is further employed to improve model stability and generalization performance. Comparative experiments show that the optimized RF model significantly outperforms the conventional RF approach, achieving an accuracy of 96.25%, a F1 score of 0.9714, and an AUC of 0.9819. The model's performance is further examined using independent seismic monitoring data from Tianmo Gully in southeastern Tibet, China, where the model shows the ability to distinguish debris-flow signals from background noise under previously unseen local conditions. Analysis of the selected features reveals clear associations with debris-flow physical processes, including initiation, energy dissipation, and spectral complexity. The proposed framework shows potential as a reliable and interpretable tool for engineering-oriented debris-flow monitoring and early-warning applications, particularly in data-limited regions.

Accurate identification of debris-flow events from seismic records is essential for developing high-resolution monitoring and early-warning systems. Here we develop an optimized Random Forest (RF) classifier designed to improve detection accuracy and, critically, to generalize across diverse geographic and environmental settings. We compile a global dataset of historical debris-flow events from 12 representative regions and construct an RF-based workflow that combines interpretable feature selection and automated model tuning. The Boruta algorithm is used to identify five informative predictors, improving interpretability while reducing redundancy in the feature set. In parallel, Bayesian optimization is employed to tune RF hyperparameters and enhance out-of-sample performance. We conduct three comparative experiments to quantify the contribution of each component. Results show that the combined Boruta–Bayesian RF consistently outperforms conventional RF approaches, achieving an accuracy of 96.25%, an F1 score of 0.9714, and an AUC of 0.9819. To further assess transferability, we apply the trained model to independent seismic data collected at Tianmo Gully in southeastern Tibet, China. The model successfully distinguishes debris-flow signals from background noise across the study period, demonstrating stable performance beyond the training regions. Overall, the proposed optimized RF framework offers an efficient, interpretable, and transferable solution for debris-flow detection using seismic signals, providing practical methodological support for the development of operational debris-flow early-warning systems.

Expanded polystyrene (EPS) shows promise for mitigating rockfall impacts, yet its brittle failure under highvelocity loading restricts engineering applications. To overcome this limitation, polyurea-EPS composite structures were fabricated by coating a custom polyurea elastomer on EPS specimen ends. Their dynamic deformation and failure characteristics were investigated using a Split Hopkinson Pressure Bar system combined with Digital Image Correlation, and compressive stress-strain curves were obtained via wave separation. Results indicate that the polyurea layer functions as a viscoelastic confinement, redistributing impact stress and delaying stress-wave transmission, thereby suppressing brittle fracture of the foam core. Forward shock oscillation in front-coated specimens markedly enhanced energy absorption, reducing the damage volume fraction from 46 % (plain EPS) to 12 %. Although both materials exhibit three compression stages, the coating promotes more uniform stress distribution, increasing elastic modulus by 139 % and plateau stress by 18 %. Energy dissipation rose by 103 % and 17 % for front-coated and back-coated composites, respectively. A nonlinear rate-sensitive constitutive model incorporating nonlinear elasticity and gas compression accurately reproduced high-strainrate behavior. These findings provide a theoretical basis for designing polyurea-coated EPS cushioning layers in rock-shed protection systems.

Oblique impacts between rockfalls and soil layers pose significant challenges to impact force prediction due to complex contact mechanics and material behavior. This study develops a unified viscoelastoplastic contact model that incorporates both normal and tangential deformation responses, explicitly introducing a viscoplastic hardening coefficient κ, to represent nonlinear soil behavior under dynamic loading. The model accounts for the transition from viscoelastic to viscoplastic regimes and distinguishes between sticking and sliding contact conditions. Small-scale impact tests were conducted using blocks of varying mass, shape, and drop height to back-calculate κ, revealing its dependence on impact energy, contact geometry, soil density, and layer thickness. Large-scale validation experiments and discrete element simulations confirm the model’s ability to capture time-varying impact forces across different soil types. Statistical analysis demonstrates that κ follows a right-skewed Weibull distribution, and its percentile values can be used to predict impact responses under different energy levels. Compared with existing models, the proposed approach significantly improves accuracy and adaptability for engineering applications in rockfall mitigation.

Seismic signals generated by rockfall-ground impacts encode critical information about source dynamics and medium response. However, establishing quantitative, physics-based relationships remains challenging because of complex contact mechanics and distortion of high-frequency waveforms. To address nonlinear variations in source behavior and wave propagation under different contact conditions, we develop a hybrid physics-AI framework that integrates a visco-elastoplastic impact simulator with a conditional variational autoencoder (CVAE)-based seismic propagation model. The source module resolves impact dynamics for three representative contact-nose geometries, while the CVAE module leverages empirical Green's functions to reproduce frequency-dependent wavefield evolution. Validation using 126 controlled single-block experiments and multi-sensor seismic recordings demonstrates that the framework reliably captures seismic signatures associated with different impact geometries. The results reveal that rock-ground contact geometry exerts a dominant control on signal characteristics, underscoring the need for physics-informed constraints on nose morphology to achieve robust dynamic parameter inversion. This study establishes a mechanics-guided inversion strategy that directly links recorded waveforms to impact kinematics, providing a new pathway for quantifying rockfall hazards from seismic observations. (c) 2026 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).

Slope instability poses significant risks to infrastructure and human safety. Characterizing the internal structure of slopes is essential for evaluating slope stability and elucidating the dynamic processes associated with deformation and failure. Conventional monitoring techniques, including inclinometers and GNSS, rely on point-based or sparsely distributed contact measurements and therefore often lack sufficient spatial coverage and vertical resolution to characterize subsurface structures comprehensively. Ambient-noise surface-wave methods provide a non-invasive and cost-effective approach for imaging shear-wave velocity (Vs) structures in slopes. In this study, two dense linear arrays were deployed at the Sanjia Village highway slope in Panzhihua City, China, to continuously acquire ambient-noise data for near-surface investigation. Rayleigh-wave phase-velocity dispersion curves were extracted from the noise records using cross-correlation-based seismic interferometry, and the corresponding phase velocities were inverted to estimate S-wave velocity models within a transdimensional Bayesian framework, where the number of subsurface layers was treated as an unknown parameter, thereby reducing dependence on prescribed initial models and improving uncertainty quantification. The S-wave velocity profiles obtained from the two linear arrays consistently identify a low-velocity sliding body (Vs < 800 m/s), an intermediate-velocity slip zone (800 m/s < Vs < 1000 m/s), and a relatively high-velocity sliding bed (Vs > 1000 m/s). The slip zone was identified near the interface between the overlying clay and the underlying strongly weathered mudstone, in agreement with borehole, deep-displacement, and groundwater observations. The results demonstrate the effectiveness and practical applicability of ambient-noise imaging combined with transdimensional Bayesian inversion for delineating internal slope structures and identifying potential slip zones in highway slopes.

This study presents a novel numerical framework for evaluating damage progression in check dams subjected to impacts from boulder-enriched debris flows. The model integrates three core components: a) a depth-averaged two-phase model to simulate debris flow dynamics accounting for fluid-solid interactions; b) a probabilistic module to represent the stochastic nature of boulder impacts, including variations in size, velocity, and frequency; and c) an elastoplastic contact-based damage model to assess structural degradation under repeated boulder impacts. A finite volume method augmented with grid-optimization techniques is utilized to solve the coupled equations efficiently and accurately resolve localized damage. Validation against experimental data, including flume tests and impact trials from the USGS and bridge pier studies, confirms the model's capability to replicate debris flow behavior and impact forces with high fidelity. Numerical case studies highlight the critical influence of boulder size and velocity (linked to debris flow viscosity) on impact forces: debris flows with lower viscosity (dilute) generate higher peak loads than their more viscous counterparts, leading to distinct patterns of dam damage. Additionally, the sequence of boulder impacts significantly affects both the onset and progression of dam failure. The study emphasizes the necessity of incorporating boulder randomness and debris flow rheology in dam design and hazard assessment. Limitations include simplified assumptions regarding boulder kinematics and dam material properties, suggesting the need for further refinement. This work offers a predictive tool for assessing check dam performance under debris flow hazards, supporting the design of resilient infrastructure in mountainous regions.

Orthogonal Experimental Design (OED) and Response Surface Methodology (RSM) optimized waste Expanded Polystyrene (EPS) bead-sand composite cushions, identifying an optimal EPS content of 38.79 % by volume for superior energy absorption and load distribution. The novel SE-S-F layered configuration (EPS-sand mixture over pure sand) reduced RC slab tension damage by 79.7 % compared to traditional sand cushions and crack width by 92.3 % relative to the EPS-sand mixture, surpassing monolithic designs. It minimized transmitted forces, accelerations, and energy dissipation while promoting flexural cracking for enhanced structural protection. Validated numerical simulations accurately modeled impact dynamics, enabling reliable performance predictions. Successive impact tests confirmed the SE-S-F configuration's multi-impact resistance, achieving a non-dimensional factor (Omega) of 1.47 by the fifth impact, outperforming geofoam-based designs. Repurposing waste EPS, this approach delivers lightweight, sustainable, and cost-effective rockfall protection systems, enhancing safety in mountainous regions and transportation corridors.

In the complex and dynamic mountainous terrains, rockfalls are a prominent and typical geological hazard. Characterized by their high - degree of suddenness, marked randomness, and wide - spread spatial distribution, rockfalls have become critical disaster - inducing factors that pose a significant threat to the safety of transportation infrastructure in mountainous areas. Among the various engineering measures designed to mitigate the risk of rockfalls, the shed - tunnel structure is one of the most essential countermeasures. However, in mountainous regions lacking rockfall movement monitoring data, directly obtaining key analysis parameters such as rockfall size, impact velocity, and impact angle is extremely difficult. Current design methods mainly rely on a combination of code - specified values, detailed field investigations, and complex numerical simulations. Nevertheless, this approach, which is based on regional average parameters, fails to comprehensively capture the complex rockfall movement characteristics influenced by the intricate interaction of terrain and geological conditions.Impact indentations, left by rockfalls on soil media, are silent recorders of these events. They contain crucial information about punching failure modes, contact - interface energy dissipation, and the evolution of elastoplastic deformation. In theory, the morphological analysis of these indentations can be used to quantitatively reconstruct key rockfall movement parameters. This innovative approach provides a new and promising method for the inversion analysis of rockfall disasters, potentially revolutionizing our understanding and management of such hazards.Scholars have conducted in - depth research on the resistance characteristics during the quasi - static and dynamic penetration of objects. Through a combination of low - speed impact granular medium experiments and sophisticated numerical simulation methods, they have explored the velocity field and packing density of granular media. As a result, they have revealed the correlation mechanism between local rheological characteristics and macroscopic mechanical responses. This achievement provides a solid microscopic theoretical basis for understanding the morphological evolution of impact indentations, bridging the gap between the micro - and macro - scale material behaviors.The formation and degradation mechanisms of the morphological characteristics of meteorite impact indentations have been applied in planetary science to invert the physical property parameters of the celestial body surface, such as porosity and strength. This application serves as an important calibration basis for the remote sensing interpretation of the planetary surface. In the context of rockfall disasters, existing research has made some progress. For example, it has focused on calculating the rockfall impact force and coefficient of restitution through the indentation depth and reconstructing the rockfall movement path based on the indentation position. However, the morphological characteristic mechanism of impact indentations remains largely unstudied, and a quantitative correlation model between the morphology and rockfall movement parameters has yet to be established.This paper proposes a novel method by utilizing the impact indentations left along the rockfall movement paths. The aim is to rapidly and accurately evaluate key parameters, including rockfall impact velocity, size, and movement angle. This, in turn, can assist in reconstructing the historical movement trajectory of rockfalls and assessing the potential risk range more precisely. To achieve this, a series of physical model tests of rockfall oblique impact on sand indentations were carried out. By integrating high - speed photography, laser scanning, and point - cloud automatic extraction technologies, a comprehensive analysis of the morphological evolution patterns and classification characteristics of rockfall indentations under different impact angles was conducted. Moreover, the three - dimensional parameters (length, width, depth, volume) that describe the indentation morphology were quantified, providing a numerical basis for further analysis.Based on these results, the influence mechanism of rockfall characteristic parameters (diameter, impact velocity, angle) on the indentation morphological parameters was revealed. Through rigorous mathematical analysis and theoretical deduction, a dimensionless correlation model was established, which is fundamental for understanding the complex relationship between rockfall behavior and indentation morphology.The main research results are as follows (1) The morphological evolution of indentations can be divided into three stages: transient compression, penetration deceleration, and collapse. The impact angle is a dominant factor that significantly affects the morphological asymmetry, adding complexity to the process. (2) The morphological parameters show a non - linear positive correlation with the impact energy. The sensitivity indices of length and width to the impact energy level are approximately equal (0.23 - 0.24), indicating a certain symmetry in their response to energy changes. Additionally, the influence of the impact angle on the morphological parameters is relatively independent in terms of the scaling coefficient, highlighting its unique role in shaping the indentation morphology. (3) Dimensionless analysis shows that the length - width ratio is mainly controlled by the angle factor. With 15° as the critical angle, its change trend transitions from a gentle to a rapid growth, indicating a fundamental change in the morphological characteristics. Moreover, the dimensionless depth and volume can be quantitatively related through the impact factor and the angle factor, further clarifying the underlying relationships in the system. (4) The correlation model was verified through a series of verification group experiments. The results show that the maximum error is within 20%, demonstrating the reliability and feasibility of inverting rockfall movement parameters based on in - situ impact indentations. This validates the practicality of the proposed method and paves the way for its wider application.

Visual recognition is a promising approach for detecting sudden rockfalls. However, environmental factors such as rain, dust, and dense vegetation can significantly degrade recognition accuracy, increasing the likelihood of false positives and missed detections. In this study, we propose a lightweight rockfall detection approach, REGM-YOLO, which leverages receptive field and attention mechanisms to enhance performance in complex environments. Our approach begins with constructing a rockfall dataset augmented by adding noise and plant shapes with green opacity to simulate challenging conditions. The method incorporates a receptive field module to extend the perceptual range of rockfall features, enhancing detection accuracy even in the presence of occlusions. Additionally, a channel attention mechanism refines feature expression by emphasizing rockfall characteristics and minimizing irrelevant background interference. A lightweight backbone network accelerates detection speed, while an advanced activation function improves the identification of rockfalls with intricate features. Experimental results indicate that REGM-YOLO improves the P-value by 7

Baffles is an effective means of preventing and controlling rock avalanches, but the blocking performance of particle splashing in rock avalanches is not yet clear. And the particle shape has a significant impact on the dynamic characteristics of particle splashing. This study reviewed the research and application of baffles in avalanches, debris flows, and rock avalanches and adopted Discrete Element Method (DEM) software to conduct numerical simulation experiments, comparing the motion characteristics and energy evolution of particle splashing in rock avalanches with various shape particles under baffles protection, analyzing the motion mode of particle splashing, and assuming the internal mechanism of shape affecting particle splashing. The results showed the splashing particle mass, peak particle velocity, maximum splashing distance, and average splashing height of particles with different shapes are roughly linearly and negatively correlated with the particle shape factor with the maximum difference of 2166.0, 25.2, 98.5 and 10.2

Galleries are commonly implemented to protect tunnel entrances from rockfall impacts. This study proposes a novel approach for the quantitative damage evaluation of galleries, leveraging a generalized Pressure-Impulse (P-I) diagram. The dynamic responses and damage evolution under the influence of different rockfall mass, velocity, shape, cushion density and thickness are analyzed. On this basis, a displacement-based damage index (D = u/ud) is formulated and correlated with the mean damage value ψ computed using the validated Concrete Damaged Plasticity (CDP) model. The damage was quantitatively categorized into four levels, with the primary independent control variables identified through dimensionless analysis. Using this method, the damage function and failure modes of the binaural integral galleries are derived. Finally, this method was validated by using single pressure galleries. The findings provide a robust framework for assessing rockfall impacts on galleries.

The forces exerted by geophysical granular flows on Earth's surface, and the resulting seismic signals, can be used to monitor natural geohazards and understand their dynamic evolution and characteristics. Substantial research has focused on linking basal force fluctuations and seismic signals to granular flow dynamics. However, the mechanisms behind the generation and evolution of seismic signals remain incompletely understood. In this study, we conducted laboratory flume experiments to gain insights into the evolution and characteristics of basal force fluctuations and seismic signals and explored their relationship with the macroscopic properties of granular flows. Our results show that the shear and normal components of basal force fluctuations exhibit different behavior during flow evolution, which are related to variations in flow velocity fluctuations. As the granular flow moves downstream, shear basal force fluctuations decrease due to weakening velocity fluctuations, whereas normal force fluctuations increase. Similar to basal force fluctuations, seismic signals follow a generalized Pareto distribution. Basal force fluctuations and seismic signals are strongly nonlinearly related to the bulk flow properties, indicating that thicker, denser and faster flows generate stronger basal force fluctuations and more intense seismic signals. However, particle size significantly influences this relationship. We demonstrate that the inertial number, characterizing the macroscopic rheological properties of granular flows, can unify basal force fluctuations and seismic signals across different particle sizes, exhibiting a negative correlation on the temporal scale. This implies that the macroscopic rheological behavior of granular flows may provide critical insights into the mechanisms of generation and evolution of seismic signals.

Accurate segmentation of rock mass discontinuities from 3-D remote sensing point clouds is a critical task for automated geological structure characterization and rock mass stability analysis. To enhance the robustness and accuracy of discontinuity detection and segmentation in complex rock outcrops, this article proposes a robust normal vector estimation method based on 3-D point clouds. This method first defines a voxel geodesic distance (VGD) to replace the traditional Euclidean distance for constructing structure-sensitive neighborhoods, which effectively avoids cross-structure interference and enhances the robustness of neighborhood selection. Then, an adaptive parameter combining local curvature and normal residuals is introduced to dynamically adjust the M-estimation weights, enhancing the robustness of normal estimation under noise and structural variation. Finally, by integrating VGD-based path weighting and adaptive M-estimation weighting, normal vectors are iteratively estimated to improve stability and continuity, particularly around boundaries and holes. Experiments on multiple real-world outcrop datasets demonstrate that the proposed method achieves superior consistency, robustness, and accuracy in detecting discontinuities, holes, fractures, and structural boundaries in complex rock surfaces. This approach provides a reliable foundation for automated discontinuity detection, orientation analysis, and subsequent 3-D structural modeling in remote sensing applications.

Effective reservoir capacity of retaining dam and impact force of debris flow are important indexes for the design of entity check dam of debris flow.Deposits or even fills in the existing entity check dam under repeated impact of debris flows,have great influence on the control ability of dam body.In this paper,based on theoretical analysis and physical model test,dynamic response of debris flow impinging entity check dam under the condition of silting behind dam is studied,dimensionless calculation formulas of velocity attenuation rate and retaining rate of dam body under the condition of silting behind dam are derived,and calculation model of impact force of debris flow under the condition of silting behind dam considering spatial distribution characteristics is established.The results show that the velocity attenuation rate and dam retaining rate of debris flow are positively correlated with the ratio of silt height to silt length and the relative density of debris flow.The combined calculation model of impact force and static and dynamic load of debris flow can better reflect the composition and distribution of impact force of debris flow under the condition of silting behind the dam.This study can provide theoretical and technical support for the design of solid debris flow retaining dam engineering.

Objective, Methods This study focus on debris flows in Huangniba gully, Muli County, Liangshan Prefecture, Sichuan Province, utilizing a mass flow numerical simulation platform. Through field investigations and the construction of numerical models, we analyze the mechanisms driving debris flow formation and evolution, aiming to invert these mechanisms.Based on this foundation, we assess debris flow hazards, develop a vulnerability model for masonry structures under different damage modes, and establish a dynamic process-based debris flow risk assessment method. Results The risk assessment indicates that, for a 20-year return period, very high- and high-risk zones for debris flow encompass 0.15×104 m2 and 1.68×104 m2, affecting 10 and 13 buildings, respectively. For a 50-year return period, the areas of very high- and high-risk zones expand by 40% and 70.8%, with 2 and 4 additional buildings affected. Moreover, for a 100-year return period, these zones increase by 113.3% and 132.1%, respectively, affecting 11 and 5 more buildings compared to the 20-year scenario. Conclusion Furthermore, the erosion-incorporating debris flow dynamics model developed in this study accurately represents the debris flow events in Huangniba gully. Additionally, the vulnerability assessment model for masonry structures was validated against other debris flow events, confirming its enhanced feasibility. These findings provide a foundation for quantitative risk prediction in Huangniba gully.

This study investigates the effectiveness of Lightweight Expanded Clay Aggregate (LECA) as a novel cushion material mitigating repeated rockfall impacts on reinforced concrete (RC) slabs in rock sheds. Small-scale impact tests and finite element simulations analyze LECA particle size, cushioning material, block shape, and impact energy level influence on the dynamic response and damage. Results show LECA outperforms sand in attenuating impact forces and transmitted loads under successive impacts, which indicates a better protection effect on the substructure. Smaller LECA particles lead to wider stress distribution angles, longer impact durations, and lower peak forces. Block shape significantly influences impact force, with higher unified nose factors increasing forces. LECA cushions exhibit a dynamic amplification factor less than 1, indicating reduced transmitted loads compared to sand. Under high-impact energy conditions, the LECA cushion limits RC slab deflection within the elastic limit across all block shapes, while sand exceeds the elastic limit, potentially leading to structural failure. LECA mitigates flexural cracking and redistributes loads more uniformly, reducing overall RC slab damage compared to sand. However, localized failure modes require further optimization. This study highlights LECA's potential for enhancing rock shed structural safety and resilience against severe rockfall events, providing insights for optimal mitigation strategies.

This study investigates the behavior of traditional concrete dams and 3D-printed concrete dams (3DPC) under the impact of boulders. To achieve this objective, a numerical analysis was conducted on flat dams utilizing the Finite Element Method (FEM). Parametric studies were carried out by varying boulder diameter and velocity to define the global performance of the flat dam. The research results, including impact pressure and elastic strain energy, were utilized to evaluate and compare the performance of both dam types. The results of the study demonstrate that 3DPC dams outperform traditional concrete dams in terms of impact force and strain energy. These results can significantly contribute to the development of more effective and efficient dam structures capable of withstanding the impact of boulders.