Engineering Structures Research Articles

Synergistic charge-transfer dynamics of novel benzothiadiazole-based donor materials for higher power conversion efficiency: From structural engineering to efficiency assessment in non-fullerene organic solar cells

In the realm of organic solar cell technology, current research is dedicated to enhancing the photovoltaic properties of donor-π-acceptor (D-π-A) materials to achieve higher power conversion efficiencies (PCE). This optimization focuses particularly on fine-tuning the conduction band and electrolytic characteristics to maximize performance. Addressing the growing demand for novel materials with enhanced optoelectronic properties in organic photovoltaic research, our proposed compound BT05, one of nine new benzothiadiazole-based D-π-A donor molecules (BT01-BT09), exhibits a power conversion efficiency (PCE) of 25%, surpassing the 18% PCE of the reference compound BTD-OMe. TD-DFT and DFT simulations illuminate how donor modifications enhance the photovoltaic characteristics of the proposed molecules. Higher open-circuit voltage (VOC) of 1.74-2.26 V, increase in binding energy (∼1.997), λmax (470-476 nm), reduction in energy gap (4.25-4.65 eV), also validates the PCE results and confirm the usefulness of designed molecules (BT01-BT09). Moreover, DHOMO and ALUMO, TDM, reorganization energy λe (0.0124-0.0134) and λh (0.0094-0.0098), and NPA results also confirm that BT01-BT09 molecules unlock the organic solar cell's potential and advance sustainable energy solutions through innovative technology. Among all developed compounds, BT05 displays higher VOC (2.26 V), 87% fill-factor, and 25% PCE; hence, it is recommended in future solar cell applications.

Efficient deep learning based models for rapid prediction of RC shear wall responses under monotonic and cyclic loading

The seismic response of reinforced concrete (RC) shear walls is of paramount importance in earthquake-resistant design. While traditional techniques such as finite-element-based modeling and experimental testing provide comprehensive insights into shear wall behavior, they often require significant computational resources and time. In response, this study investigates the efficacy of various Deep Neural Networks (DNNs), including Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LSTM) networks, and LSTM-Based Autoencoders, in predicting the behavior of RC shear walls under monotonic and cyclic loading conditions. Through training on a diverse dataset encompassing various geometric configurations, material properties, and loading scenarios, the proposed approach generalizes well to unseen cases, enabling rapid estimation of shear wall responses without extensive numerical simulations. This approach offers several advantages, including faster design iterations, improved assessment of existing structures, and potentially higher accuracy. Extensive comparative analyses against experimental tests and numerical simulations evaluate the performance of the deep learning models. Results demonstrate that the proposed approach achieves superior accuracy in prediction of hysteresis and pushover curves, effectively capturing the nonlinear structural behavior of RC shear walls subjected to static and cyclic loading conditions. Validation using a comprehensive experimental database of 160 specimens shows that the model achieves less than 10 % error in maximum lateral load predictions for specimens within the training data range; while simultaneously reducing computational time Overall, this study contributes to the evolving landscape of structural engineering by showcasing the potential of DNNs as efficient tools for predicting the behavior of RC shear walls.

Synthesis and characterization of new continuous phosphate glass fibers intended for structural engineering applications: Structure/property relationships

Nowadays, phosphate glass fibers (PGF) are considered quite competitive respective to conventional glass fibers. This work focus on the development of PGF fibers intended for structural engineering applications such as composite reinforcement for building and automotive fields. For this purpose, two series of phosphate glasses based on 52P2O5-24CaO-13MgO-(11-(X+Y+Z)) K2O-XAL2O3-YF2O3-ZTiO2; (X=1; 3; 5, Y=0; 5, Z=0; 1 mol %) were processed and transformed into phosphate glass fibers by melt spinning. The resulting fibers were characterized. XRD analysis confirmed the non-crystalline nature of phosphate glasses. In addition, the substitution of K2O by Al2O3 and by the combination of Al2O3, Fe2O3 and TiO2 in the composition of phosphate glass could lead to a significant increase in fiber density from 2,16 g/cm3 to 2,80 g/cm3. The stability of the produced phosphate glass fibers was examined using two methods: weight loss at 37°C and dissolution kinetics under different pH levels (4, 7, and 12). The results showed that the chemical resistance of the PGF fibers was improved with up to 99% increase respective to the original formulation. In addition, the mechanical properties of the spinnable phosphate glass manufactured by replacing K2O oxide by Al2O3 oxide were improved, such a substitution led to a maximum tensile strength and modulus of 2668 MPa and 140 GPa, respectively. Therefore, the tensile proprieties were improved by 75% compared to the original formulation. This comparative study between phosphate glass fibers (PGF) and traditional fibers highlight similar tensile strength but combined to notable enhancements in chemical stability through cation addition, expanding their potential use in composite and biomedical materials. Finally, a correlation analysis of mechanical performances was carried out. It was observed that the results obtained using the statistical methods were consistent with the experimental data.

Atomic-level structural engineering of La-doped CoMoP composite for enhanced overall water splitting

A series of La-doped CoMoP composites were fabricated through an in-situ phosphorization strategy with La–CoMo layered double hydroxide (LDH) as precursors. The homogenous dispersion of metal ions within the LDH structure facilitated the creation of a uniformly atomic-level La-doped CoMoP composite. Among these, the 5 % La-doped CoMoP exhibited the best performance, requiring only 41 mV overpotential for hydrogen evolution reaction (HER) and 303 mV for oxygen evolution reaction (OER) to reach a current density of 10 mA cm−2. For overall water splitting, it achieved a current density of 10 mA cm−2 at a cell voltage of just 1.61 V and maintained a stable performance over 24 h. The modulation effect of rare-earth elements, combined with the synergistic interactions among transition metal ions, contributes to its excellent bifunctional electrocatalytic performance.

Truss sizing optimum design using a metaheuristic approach: Connected banking system

Several methods have been used to solve structural optimum design problems since the creation of a need for light weight design of structures and there is still no single method for solving the optimum design problems in structural engineering field that is capable of providing efficient solutions to all of the structural optimum design problems. Therefore, there are several proposed and utilized methods to deal with optimum design issues and problems, that sometimes give promising results and sometimes the solutions are quite unacceptable. This issue with metaheuristic algorithms, which are suitable approaches to solve these set of problems, is quite usual and is supported by the “No Free Lunch theorem”. Researchers try harder than the past to propose methods capable of presenting robust and optimal solutions in a wider range of structural optimum design problems, so that to find an algorithm that can cover a wider range of structural optimization problems and obtain a better optimum design. Truss structures are one of these problems which have extremely complex search spaces to conduct search procedures by metaheuristic algorithms. This paper proposes a method for optimum design of truss sizing problems. The presented method is used against 6 well-known benchmark truss structures (10 bar, 17 bar, 18 bar, 25 bar, 72 bar and 120 bar) and its results are compared with some of the available studies in the literature. The performance of the presented algorithm can be considered as very acceptable.

A fast impact force identification method via constructing a dynamic reduced dictionary

Impact force identification is an important aspect of structural health monitoring for online assessment of the integrity of engineering structures. Increasing the number of monitoring positions is undoubtedly beneficial to accurate force localization, however, it may increase sharply the dimension of the transfer matrix and thus lead to a large-scale inverse problem. In this case, traditional methods, such as the Iterative Shrinkage Threshold algorithm (IST) and the Two-Step Iterative Shrinkage Thresholding algorithm (TwIST), suffer from low solving efficiency. In this paper, the dynamic reduced dictionary is firstly introduced according to the sparsity of impact force in both time and spatial domains so as to lower significantly the dimension of the transfer matrix. Then, a new reduced unconstrained optimization problem is established via the dynamic reduced dictionary for impact force identification. By integrating the reduced unconstrained optimization problem into each iteration step of IST/TwIST, two novel algorithms, which are the reduced Newton iteration shrinkage threshold algorithm (rNIST) and the reduced Newton two-step iteration shrinkage threshold algorithm (rNTwIST), are proposed to solve quickly the large-scale impact force identification problem using ℓ1-norm sparse regularization. Meanwhile, the adaptive setting of regularization parameters strategy is used to construct force more fast and accurately. The proposed algorithms are numerically and experimentally demonstrated through identifying impact forces at 16-point and 81-point distributions of a plate using one sensor response. The results show that rNIST/rNTwIST can more quickly and accurately identify impact forces under numerous monitoring points compared with other ℓ1-norm regularization methods and nonconvex sparse regularization methods. Moreover, the more the number of monitoring points, the more obvious the efficiency improvement of the impact force identification by rNIST/rNTwIST method.

Influence of different cooling methods on the creep characteristics and damage model of layered limestone after thermal treatment

In the actual underground energy extraction projects, the long-term stability of the well wall surrounding rock through the layered rock body often faces the influence of complex factors such as joints, thermal and cooling. The laminated limestone as a common rock type with obvious laminated structure and petroleum reservoirs, under high temperature environment can directly relate to the stability and safety of underground engineering structures. This paper investigates the creep characteristics of laminated limestone under high-temperature natural cooling and water cooling, analyzing the correlation between creep deformation, creep rate, long-term strength, and the angle of laminations, temperature, cooling mode. It then introduces a non-constant fractional-order cohesive body to construct an improved model based on these findings. The results indicate that under the same temperature and cooling method, as the lamination angle increases, the long-term strength shows a V-shaped trend, while the creep rate shows an inverted V-shaped trend. Specifically, the water cooling conditions at 500 °C (0°, 30°, 45°, 60°, and 90°) are 85.75 MPa, 71.24 MPa, 42.30 MPa, 61.52 MPa, and 87.40 MPa, respectively. For specimens with the same lamination angle, the creep mechanical properties of specimens cooled by water are more deteriorated than those cooled naturally. Taking the 100 °C action stratified limestone, long-term strength (0°, 30°, 45°, 60°, 90°) corresponding to natural cooling are 120.15 MPa, 106.12 MPa, 72.71 MPa, 91.43 MPa, and 123.32 MPa, respectively, while the long-term strengths corresponding to water cooling are 116.96 MPa, 100.97 MPa, 70.65 MPa, 85.34 MPa and 119.63 MPa, respectively. Finally, by comparing with the experimental results of nodular limestone under different cooling methods, it is found that the creep damage model proposed in this paper is not much different from the experimental curves, and it can effectively express the creep deformation and mechanical behavior of laminated limestone after high temperature. The results and findings are important for optimizing the design and prevention of potential underground energy extraction project disasters, and also provide important data support.

Digital twins for engineering structures—An Industry 4.0 perspective

AbstractThe term “digital twin” is becoming increasingly prevalent in both research and politics with regard to infrastructure structures. Industry is making significant advancements in the fields of automation and autonomy. While numerous definitions are in circulation, some of which are identical, many are similar in nature. This work examines the digital twin from its original industry perspective and considers its relevance to the constructional engineering sector. The objective of this study is to provide an overview of the current approaches in the Industry 4.0 and constructional engineering industry, examining the associated technical opportunities and challenges. The study compares the definitions, requirements and projects executed in this field. The technologies under examination are contrasted on the basis of their position within the data model. The preliminary findings suggest that some solutions for Industry 4.0 have already been developed for the construction engineering sector. However, it must be acknowledged that some solutions have yet to be fully validated.

Numerical reconstruction of atmospheric boundary layer seasonal turbulent wind field over a complex forest terrain

The wind characteristics of the atmospheric boundary layer (ABL) over complex forest terrains are inherently intricate, influenced by the interplay of rugged topography, seasonal climatic fluctuations, and periodic vegetation dynamics. These effects are especially evident in the near-ground wind field, which exhibits substantial seasonal variability. Conventional wind characterization methods, as outlined in current standards, often fail to accurately capture these seasonal variations, thereby complicating the reconstruction of the near-ground ABL turbulent wind field in complex forest terrains. Accordingly, we employ the narrow band synthetic random flow generation (NSRFG) technique within large eddy simulation (LES) to generate inflow turbulence representing the growing and baldness seasons in a complex forest terrain by adjusting parameter equations for seasonal adaptation and introducing new empirical equations for the turbulent spectrum. Subsequently, we verified the seasonal turbulent flow's statistical characteristics and flow structure to assess its feasibility and validity, ultimately establishing a ‘seasonal numerical wind field’ model. Finally, the seasonally modified LES-NSRFG method was applied to the numerical simulation of turbulent flow around the Commonwealth Advisory Aeronautical Research Council (CAARC) standard high-rise building model. A comprehensive comparison of wind effects was conducted for the CAARC model under varying incoming flow conditions. The results indicate that seasonal winds in a complex forest terrain significantly affect the building's vortex wake, increasing the irregularity and complexity of the structural wind pressure and base moment coefficients. Thus, the seasonal wind effect must be considered when designing wind-resistant engineering structures in forest regions moving forward.

Interpretable machine learning models for predicting probabilistic axial buckling strength of steel circular hollow section members considering discreteness of geometries and material

Steel circular hollow section (CHS) members are widely utilized as axial force-resisting structural members in civil engineering structures. The buckling strength under axial loads is one of the critical parameters to determine the performance of the steel CHS members, which is significantly affected by the discreteness introduced by geometries, material, and initial imperfections. However, the reduction factor employed in the modern design codes (i.e. Chinese codes and EC3) only accounts for the reduction caused by all kinds of discreteness and does not reflect the impacts of every single discreteness and imperfection. To fill the gap, this paper proposed an interpretable machine-learning method to provide the probabilistic axial buckling strength of steel CHS members prediction result in a distribution form with the consideration of detailed discreteness. The model to predict the nominal axial buckling strength of steel CHS members was first developed utilizing ten machine learning algorithms after sufficient numerical simulations, where the numerical model was verified using test results. The artificial neural network (ANN) was selected for developing the prediction model due to its highly reliable performance in testing. The developed ANN models were further interpreted utilizing Shapley Additive exPlanations (SHAP) to determine the interrelationship of different parameters. Then, the probabilistic axial bucking strength prediction model was established based on the developed ANN models, where the Latin hypercube sampling method was applied to address the discreteness of geometries, material, and initial imperfections. The generated probabilistic axial bucking strength prediction model’s effectiveness was verified by the evidence that the machine learning prediction results can highly match the numerical results' probability density function and the result from codes while significantly reducing the computation time. Finally, the design parameters’ impact on the axial buckling strength’s discreteness was evaluated using the global sensitivity analysis (GSA) method. The result shows that the discreteness of design parameters substantially influences the distribution of the axial buckling strength of the steel CHS members and the proposed prediction model can provide an accurate probabilistic distribution prediction.

Nonlinear elasticity tailoring and failure mode manipulation of functionally graded honeycombs under large deformation

Design of lattice metamaterials with tailored mechanical properties at a relatively higher (macro) length scale by architecting lower (micro) length scale geometric and material configurations leads to achieving unprecedented mechanical properties for fulfilling advanced multi-functional structural demands. In the design space of innovative microstructural configurations, we propose a novel class of lattice metamaterials with cell walls made of optimally designed functionally graded intrinsic materials. Under different modes of remotely applied mechanical stresses, two different intuitive architectures of functional gradations for the intrinsic cell wall materials are proposed. The large-deformation nonlinear lattices result in broadband modulation of effective stiffness, and an unprecedented manipulation capability of failure modes and corresponding strengths covering ductile and brittle types depending on architected material gradation. For estimating the nonlinear elasticity and microstructural stresses as a measure of failure mode for the proposed functionally graded lattices undergoing large deformation, a multi-scale mechanics-based semi-analytical framework is developed. Geometrically nonlinear functionally graded beams with generalized material gradation, integrated with unit cell architectures, are analyzed through iterative variational energy principle-based Ritz approach. Based on the developed physically insightful computational framework, effective nonlinear elastic properties and failure modes of the functionally graded honeycomb lattices are tailored as a function of the intrinsic material gradation at the lower length scale. The proposed novel class of lattices with optimally designed functionally graded intrinsic materials and coupled unit cell architectures would open up innovative avenues for designing advanced multi-functional engineering structures and mechanical systems.

Analysis of Saturated Impulse for Square Plates under Flat Slamming Impact: Experimental and Numerical Investigation

The saturated impulse is a special phenomenon in the dynamic plastic behavior of engineering structures under intensive pulse loading, such as slamming loading. In this study, slamming experiments were performed on steel plates to investigate their slamming pressure and dynamic plastic responses, as well as the saturation phenomenon, and elucidate the effect of the plate thickness and material properties on the dimensionless saturated deflection and saturated impulse in combination with the published test data. The results show that the dimensionless saturated deflection and saturated impulse of the test plates gradually increased as the dimensionless stiffness decreased. After being validated against the experimental results, a numerical method that considered the fluid–structure interaction (FSI) effect was then employed to provide comprehensive insight into the transient plastic responses and saturated impulse of the flat plates under slamming impact. Numerical simulations revealed that the compressed air layer always existed during the effective process of the flat slamming impact. Through the numerical prediction of the dynamic plastic deflection and slamming pulse loading, it was observed that the saturated impulse phenomenon always took place after the time instant of the peak value of the pressure pulse. Furthermore, the analysis of the saturated impulse based on the numerical simulations indicated that the saturation phenomenon was more likely to be achieved as the water impact velocity increased.

Effect of weathering on the stability of rock slope at a water intake canal of Simplicio Hydroelectric Power Plant

Rock and joint alteration leads to modifications in their properties, resulting in a reduction of rock mass strength and consequently a decrease in the safety of engineering structures. The alteration process changes the material and alters its geomechanical behavior. This research presents the studies conducted to evaluate the mechanical behavior of rock joints in the gneiss massifs that make up the hydraulic circuit of the Simplicio Hydroelectric Plant due to their alteration. Compilation and analysis of data from previous research were carried out to parameterize the joints at different levels of alteration. Deterministic stability analyses showed that the alteration process of gneiss joints can significantly reduce the slope safety factor by up to half of its initial value. Furthermore, probabilistic analyses revealed an increase in failure probability of approximately 10% among the studied alteration classes. The results underscore the importance of assessing the level of joint alteration in engineering projects.

الصيغ التحليلية للاهتزازات المحورية والانحناء والقص والالتواء المقترنة لعوارض تيموشينكو ذات الصفائح المركبة الغير متماثلة

This paper presents the exact closed-form solutions for the vibration analysis of extension-bending-shear-torsion coupled responses in asymmetric laminated Timoshenko beams. The study derives four governing differential equations and corresponding boundary conditions using Hamilton’s variational principle. The formulation, based on first-order shear deformation theory (FSDT), accounts for rotary inertia, Poisson’s ratio, and extensional-flexural-torsional-shear coupling effects arising from material anisotropy. The exact solutions are obtained for various harmonic bending and twisting excitations under different boundary conditions, capturing the fully coupled axial, bending, and torsional responses. This approach offers a robust analytical method for understanding the complex dynamic behavior of laminated composite beams subjected to dynamic loads, with potential applications in aerospace, mechanical, and civil engineering structures, such as aircraft components, turbine blades, and bridges. Keywords: Closed-form solution; extension-bending-shear-torsion coupled response; harmonic excitations; asymmetric laminated beam.

A strain transmissibility-based analysis approach for operational modal of concrete dam under nonstationary excitation

The transmissibility-based operational modal analysis (TOMA) method is attracting more attentions due to its relaxation of assumptions about excitation properties, making accurately identifying the modes of actual engineering structures possible. However, in specific application on distributed vibration responses of huge hydraulic structures excited by broad-spectrum non-stationary earthquake, more proper selections of data segmentations on two dimensions (along time or spatial axis) are needed. Based on the concept of distributed vibration sensing optical fiber strain transmissibility, the operational modal analysis model based on single reference and poly references transmissibility under the strain format and the corresponding solutions of model and modal parameters are studied. A false mode elimination method combined with the continuity of spatial distribution of optical fiber measurements, a response sequence set optimization method that comprehensively consider amplitude/PSD non-stationarity, and the principle about how to select (non) reference points (sets) considering the spatial distribution of measurement signal-to-ratio are further developed. The case study shows that the proposed combined method could improve the modal parameter identification accuracy of concrete dams under broad-spectrum non-stationary excitation, and the distributed optical fiber vibration sensing technology can provide a rich (non) reference point (set) selection combination for this method.

Deterioration of fire resistance of ultra-high-performance polypropylene-fibre-reinforced concrete (UHPPFRC) under long-term acid rain corrosion action

The application of ultra-high-performance polypropylene-fibre-reinforced concrete (UHPPFRC) has become widespread due to its superior mechanical properties and non-flammable characteristic. Compared to UHPC, the fire resistance of UHPPFRC, which is crucial in ensuring structural integrity and safety during fire incident, has been improved by the addition of PP fibres. In recent years, the escalation of industrial emission has resulted in a more pronounced pollution of acid rain, a major type of sulphate attack on outdoor concrete structures, leading to the aging of UHPPFRC. Given the long service life of civil engineering structures, the fire performance of aging UHPPFRC should be given significant consideration, yet few studies have taken it into account. This study, for the first time, analyses the deterioration of fire resistance of acid-rain-induced aging UHPPFRCs and compares their probability of failure under fire condition across different corrosion durations, providing guidance for the maintenance of UHPPFRC in acid-rain-prone areas. A novel coupled chemo-thermal-mechanical model is developed, which is employed to investigate the sulphate penetration process and internal stress development within UHPPFRC during the corrosion stage, and to predict temperature and internal stress distribution during the subsequent heating stage. The effects of sulphate concentration, heating rate and fibre dosage on the probability of failure of aging UHPPFRC are then studied. The results obtained from the numerical investigation indicate that the aging effect can significantly compromise the fire resistance of UHPPFRC and augment the probability of failure at elevated temperature. Also, the probability of failure of aging UHPPFRC is more sensitive to changes in sulphate concentration than changes in heating rate.

Framework for Seismic Risk Analysis of Engineering Structures Considering the Coupling Damage from Multienvironmental Factors

Framework for Seismic Risk Analysis of Engineering Structures Considering the Coupling Damage from Multienvironmental Factors

Crack arrest characteristics and dynamic fracture parameters of moving cracks encountering double holes under impact loads

Brittle materials such as rock or concrete contain a large number of micro-cracks, voids, and other defects. Under external impacting loads, the interaction between fissures and holes affects the bearing capacity and stability of Engineering structures. To study the interaction mechanism between moving cracks and circular holes, A large-size semi-circular notched with a fissure and double circular hole (SNFDH) specimen was suggested in this research, and the dynamic fracture tests were carried out on the SNFDH specimens with different double circular hole spacing (35mm, 40 mm, 45 mm, 50 mm, 55 mm, 60 mm, and 70 mm) using a drop hammer impact test device. Crack propagation gauges were employed to track the time and velocity at which the crack propagated along its trajectory. Dynamic Drucker-Prager yield criterion and the cumulative damage failure criterion were used in the numerical simulation of SNFDH concrete materials. The program AUTODYN was employed to simulate the crack growth characteristics when a crack encounters the double hole. The program ABAQUS was applied to calculate the dynamic fracture parameter of cracks. The test results manifest that the crack propagating trajectory has three different characteristics according to the double hole spacing; the double circular hole has an arresting function for a moving crack; the size of the double hole spacing has a significant effect on the crack propagating velocity, crack propagating length, dynamic stress intensity factor and dynamic energy release rate; the configuration of the SNFDH specimen can be used to investigate the crack arrest mechanism when moving crack encountering double holes.

A robust LightGBM model for concrete tensile strength forecast to aid in resilience-based structure strategies

The tensile strength (TS) of concrete is a factor in the design of civil engineering structures. Traditional methods for assessing concrete TS have notable limitations, leading to the emergence of non-destructive testing (NDT) as an innovative alternative. This study introduces a novel, optimized approach for accurate, non-destructive TS prediction by integrating Gradio, Bayesian optimization (BO), and Shapley additive explanations (SHAP). The input parameters for the model were ultrasonic pulse velocity (UPV) and electrical resistivity (ER). Using a comprehensive dataset of 3460 datapoints, a light gradient-boosting machine (LightGBM) model was developed. Through extensive hyperparameter tuning facilitated by BO, the model achieved a robust prediction accuracy of approximately 98 %. The novel application of SHAP in this study provided deep insights into model behavior, confirming UPV as the most critical variable for TS prediction. The deployment of the model using Gradio further enhances its accessibility and usability. This combined methodology not only advances the application of machine learning (ML) in civil engineering but also offers a powerful, globally applicable tool for accurately forecasting concrete TS. This supports resilience-based management strategies, essential for the sustainable development of infrastructure. The study's novel integration of BO for optimization and SHAP for interpretability marks a significant step forward in the precise prediction of TS, offering practical benefits for both researchers and industry professionals.

Development of a smoothed particle hydrodynamics model for porous media flows with enhanced volume conservation and the revisit of the mass conservation equation

Porous media exist extensively in hydraulic and coastal engineering structures, while the modeling of wave/flow interaction with porous media remains challenging. This work develops a smoothed particle hydrodynamics (SPH) model for accurately simulating wave/flow interaction with porous media. The mass and momentum conservation equations incorporating the mixture theory are adopted. The resistant forces of the solid skeleton of porous media on fluid flows are described by the nonlinear empirical formula. The research contributions of the work lie in two aspects. First, two categories of mass conservation equations for porous media flow are revisited and analyzed to examine the influences of the local time derivative term of fluid volume fraction on simulation results. Second, the Volume Conservation Shifting scheme is, for the first time, introduced into SPH to enhance volume conservation for simulating porous media flows. The developed SPH model is validated by an analytical case of seepage flows in a U-tube with porous media and then applied to study four benchmark examples involving both saturated and unsaturated porous media, i.e., dam-break flow through a crushed stone dam, rapid seepage flow through a rockfill dam, solitary wave propagation over a porous seabed, and solitary wave propagation over a submerged porous breakwater. The morphological features and dynamic pressure heads of the porous media flows have been satisfactorily predicted, demonstrating the good accuracy and enhanced volume conservation of the developed SPH model.