Aluminum pipes are widely employed in industrial applications due to their low density, corrosion resistance, and excellent mechanical properties, however, defects such as cracks and corrosion can significantly reduce their structural integrity. This study investigates the percentage variation in natural frequencies of damaged aluminum pipes repaired using composite patches made of glass, carbon, and basalt fiber-reinforced polymers (GFRP, CFRP, and BFRP). Experimental modal analysis was first conducted on intact, damaged, and repaired pipe specimens, revealing frequency reductions of up to 15-25% due to damage, depending on crack length and boundary conditions. Finite element (FE) modeling showed good agreement with experimental results, with discrepancies generally below 6% for the first two vibration modes. Parametric numerical simulations were then used to generate a comprehensive dataset incorporating crack length, patch type, number of layers, and fiber orientation. Based on these data, an artificial neural network (ANN) model was developed to predict frequency variation in repaired pipes, achieving a high prediction accuracy with correlation coefficients exceeding 0.95 and low validation errors. The results demonstrate that composite patch type and stacking sequence play a key role in stiffness recovery, with CFRP patches providing the highest frequency restoration. The proposed framework, combining experimental vibration measurements, numerical modeling, and ANN-based prediction, offers an effective and reliable tool for assessing and optimizing composite repair strategies for damaged aluminum pipes.

The primary objective of this study is to develop two novel refined plate theory (RPT) formulations, referred to as RPT-H1(z) and RPT-H2(z) for the analysis of multilayered composite plates. In these formulations, RPT denotes a refined plate theory based on a four-variable displacement field, while H1(z) and H2(z) represent two newly proposed through-thickness functions employed to characterize transverse shear deformation. Unlike conventional refined and higher-order plate theories, in which through-thickness functions are often selected empirically or solely to satisfy essential transverse shear stress boundary conditions, the proposed functions H1(z) and H2(z) are established through a systematic optimization procedure. This procedure aims to minimize the discrepancy between the proposed four-variable refined plate theory and reference solutions obtained from a five-variable higher-order shear deformation theory (HSDT). Based on the optimized through-thickness functions, a compact refined plate theory is developed for the bending and free vibration analysis of laminated and sandwich plates. The proposed formulations inherently satisfy the zero transverse shear stress conditions at the top and bottom surfaces of the plate and accurately capture the nonlinear through-thickness distribution of transverse shear stresses without requiring shear correction factors. Numerical examples are presented to verify the accuracy and robustness of the proposed formulations in both bending and free vibration analyses. Numerical examples confirm the accuracy of the proposed model in both bending and free vibration analyses, showing good agreement with reference solutions while using a reduced number of displacement variables.

This study develops a novel, data-driven family of through-thickness shape functions for higher-order shear theories of laminated plates and, more broadly, introduces an optimization-based methodology for constructing kinematic assumptions in structural mechanics. Each function combines a cosine-exponential kernel with an even-order polynomial whose coefficients are identified by an extended K-means clustering procedure, so that the resulting transverse-shear distribution is optimally fitted to exact three-dimensional elasticity solutions. Because the kernel-polynomial product has no closed-form antiderivative, all thickness integrals needed for stiffness and mass matrices are evaluated by the composite trapezoidal rule, making the approach easy to embed in existing finite-element or isogeometric codes. The higher-order formulation is implemented in an IGA framework with NURBS basis functions, ensuring exact geometry and the continuity requirements of the new series. Its accuracy is demonstrated through static bending and free-vibration analyses of simply supported laminated plates under sinusoidal and uniform loads, where mid-plane deflections, in-plane stresses, interlaminar shear stresses, and natural frequencies are benchmarked against recent kernel-based models and three-dimensional elasticity. The proposed shear-deformation functions consistently yield closer agreement at comparable computational cost. More importantly, the optimization-driven construction provides a systematic and generalizable strategy for designing through-thickness shape functions, offering a new scientific basis that can be extended to other plate and shell theories and to a wider class of mechanics problems.

This study presents a novel approach to optimizing column mass in complex reinforced concrete structures by integrating multiobjective optimization with variants of the K-means Optimizer (KO) algorithm. A key technical contribution of the research lies in the development of a two-way computational system that establishes a direct connection between MATLAB and ETABS via the Open Application Programming Interface (OAPI). This integration enables MATLAB to function as an independent computational platform capable of automatically updating design parameters within the finite element model (FEM) in ETABS, eliminating the need for manual interaction with the software's graphical interface. Building upon this automation framework, objective functions are formulated with constraints related to structural drift and interstory displacement. The search for a robust and efficient optimization algorithm is conducted through performance evaluations of KO variants across 50 benchmark test functions. To validate the effectiveness and practical applicability of the proposed methodology, two case studies are carried out: (1) a 15-story planar reinforced concrete frame subjected to both gravity and lateral loads and (2) a realistic 10-story three-dimensional structural system under comprehensive load combinations. The results from both cases confirm that the proposed method satisfies all structural constraints while achieving near-optimal mass reduction. Furthermore, the study introduces an enhanced variant of the KO algorithm, demonstrating strong potential for application not only in structural optimization but also in solving other complex engineering optimization problems.

This paper introduces a novel deep learning framework for predicting the normalized and non-dimensional deflection of functionally graded composite plates subjected to sinusoidal loading. The proposed approach integrates a deep neural network (DNN) with a novel enhanced whale optimization algorithm (EWOA) to optimize deflection predictions considering mechanical parameters as input data, including stress, strain, plate geometry, and boundary conditions. The deflection outputs are expressed in both normalized and non-dimensional forms, demonstrating a robust and generalizable prediction model applicable to various structural configurations. During the training phase, the proposed EWOA significantly enhances convergence efficiency and prediction accuracy by introducing two key improvements: chaotic initialization and an adaptive leader mechanism. The EWOA-DNN model is trained using analytically derived deflection datasets, exhibiting strong adaptability to changes in material gradation and loading scenarios. Comparative studies confirm that the suggested hybrid framework outperforms conventional optimization-based models, creating an effective and reliable artificial intelligence (AI)-driven tool for structural design, computational mechanics, and the analysis of functionally graded composite materials.

Abstract This study presents a density-based topology optimization (TO) procedure for incompressible Stokes flow on adaptive polygonal meshes. The stabilized equal-order polygonal finite element, Pe1Pe1, is applied for the Stokes–Brinkman analysis, together with a Solid Isotropic Material with Penalization-Optimality Criteria (SIMP-OC) update for the material field, and PolyTree refinement to increase the resolution near the solid-fluid interface. The Dohrmann-Bochev pressure projection is retained at all refinement stages to reduce pressure oscillations in the equal-order discretization. At a new refinement level, the code first inspects the polygon shapes before assembling the next Stokes–Brinkman system. Elements with poor geometric quality are corrected by moving selected interior nodes and by applying Laplacian smoothing. Boundary nodes are kept fixed during this step. The filter radius is then recomputed from the updated element size, and the Heaviside parameter follows the continuation schedule instead of being tied to the starting mesh. Then, the proposed method is tested on the diffuser and double-pipe benchmarks of Pereira et al , using a reproduced Pe1Pe0 PolyTop-SIMP-OC implementation as the validation reference. For the diffuser and double-pipe examples considered here, the Pe1Pe1 PolyTree formulation usually gives a lower dissipated power than the reproduced Pe1Pe0 baseline. The largest difference, about 25%, occurs in one of the double-pipe cases. Runtime follows the same trend in several examples, although not in every single run. This is expected, since the Pe1Pe1 solve and the mesh-quality steps add work locally, whereas the adaptive mesh avoids unnecessary refinement away from the active flow regions. The remaining tests are used to check that the conclusion is not limited to one setting: the parameter choices are varied, the Pe1Pe1 solution is compared with a structured-grid Q2–Q1 Taylor-Hood reference, interface measures are reported, and an obstacle domain with an internal boundary is also solved. These results support the use of the proposed polygonal-mesh procedure for Stokes-flow TO.

In this paper, a combined experimental–computational combined method for damage quantification and stiffness recovery evaluation of glass fiber-reinforced polymer (GFRP) and basalt fiber-reinforced polymer (BFRP) patch-strengthened aluminum plates is introduced. Central-hole damage was used to mimic localized loss in stiffness, and recovery potential of both patches was investigated through experimental modal analysis. The initial four natural frequencies obtained for undamaged, damaged, and patched configurations were used as input parameters for the training of a new hybrid artificial intelligence (AI) model SSA-SCA-XGBoost where the Salp Swarm Algorithm (SSA) offers effective global search and Sine Cosine Algorithm (SCA) optimizes local search for hyperparameter tuning of XGBoost. Numerical finite element modeling confirmed experimental trends and enabled composite–structure interaction interpretation. The findings demonstrate that patches of GFRP show enhanced recovery of stiffness and patches of BFRP show enhanced damping characteristics. The new hybrid SSA-SCA-XGBoost model also attained better predictive accuracy than the traditional metaheuristic–XGBoost hybrids with negligible prediction error in a large volume of damage range. The research verifies the dual role of composite patching as a high-strength structural retrofitting method and advanced hybrid AI modeling as an accurate, large-scale quantification tool for composite-strengthened systems.

This experimental study evaluates the influence of recycled metallic fibers (MF) from steel machining waste and polypropylene grids (PPG) on the flexural behavior of concrete slabs. The metallic fibers are randomly dispersed throughout the concrete, while the PPG grids (with fine and large meshes) are positioned through the slab thickness. Three-point bending tests were conducted on slabs measuring 250 × 500 × 70 mm. Results show that metallic fibers at a fiber content of 0.8 _f = 0.8

When using the continuous wavelet transform (CWT), wavelet coefficients in higher scales cannot detect damages and singularities in the structural signal; this is a weakness of CWT. This paper introduces a greedy wavelet transform (GWT), an enhanced version of the one-dimensional continuous wavelet transform, for improved damage localization. In this method, first, the CWT wavelet transform is applied to the damaged structural signal. Then the standard deviation of each scale is calculated from the wavelet coefficients to obtain the standard deviation vector of the wavelet coefficients. A Greedy algorithm finds the scale corresponding to the optimal wavelet coefficients with the lowest standard deviation, so that in the next step, these optimal wavelet coefficients are re-entered into the CWT. This process is iterated until the desired solution is obtained. Experimental validation on a polyurethane sandwich beam under various damage scenarios and mode shapes, using modal analysis for signal acquisition, demonstrates the GWT’s effectiveness in damage detection. The GWT consistently outperforms the conventional wavelet transform across all tested mode shapes and damage scenarios.

The primary objective of this study is to investigate the contact behavior between soil and super-long bored piles (SLBPs). To address this problem, a three-dimensional simulation–optimization framework (3DSOF) is developed by integrating a meta-heuristic algorithm, a finite element (FE) model, and the Python programming language. First, a series of empirical methods is employed to obtain soil engineering parameters. Subsequently, an in-situ pile load test under static axial compressive loading was carried out in accordance with ASTM D1143. The resulting load–settlement curve showed good agreement with the field test results, indicating that the method can reliably capture key soil characteristics and provide parameter estimates close to actual in-situ values. Based on these results, the soil–pile contact behavior, represented by R_inter , is examined. The results show that R_inter decreases with depth and depends on soil type, with clear differences between sand and clay around super-long bored piles. This study proposes representative ranges of R_inter for both soil types and provides useful guidance for its selection, while indicating the importance of further study.

In this paper, a comprehensive modal analysis is conducted, utilizing experimental and numerical approaches to investigate and predict the influence of the damage size on structural behavior. A finite element (FE) model is created from different loading conditions of the glass fiber reinforced polymer (GFRP) composite pipe with a fixed damage size. The model is subjected to three pressure levels (10, 20, and 30 bar). Next, a dynamic frequency load is then added to the FE model to visualize the different frequency mode shapes and their values under different loading conditions on a pressurized and unpressurized composite pipe. The results were collected for the proposed training models. A Kolmogorov Arnold model is employed to develop a robust predictive model that accurately estimates these geometrical and mechanical parameters by analyzing vibration data from various scenarios. The results show a significant correlation between the actual and predicted results across a wide range of test settings and scenarios, and the precision of the predictions is increased by combining data-driven methodologies with classical modal analysis.

For the first time, this paper introduces the Chebyshev Finite Element (CFE) method to analyse the static bending and free vibrations of tridirectional functionally graded porous (TFGP) doublycurved nanoshells on a Winkler elastic foundation. The governing equations of motion for the shell are formulated based on Hamilton's principle, higher-order shear deformation theory, and nonlocal stress theory. The material properties of the structure, as well as the nonlocal coefficient, vary in three directions - thickness, width, and length of the TFGP doubly-curved nanoshell. Chebyshev polynomials are used to create high-order shape functions that satisfy the interpolation criterion at the nodes. The complete Gauss quadrature rule was employed to compute the stiffness matrix, mass matrix, and load vector. Compared to conventional FEM methods, this method provides superior accuracy, as evidenced by a series of benchmark comparisons with established numerical and analytical publications. In addition, the influence of input parameters including: nonloal parameter, porosity coefficient, curvature radius, grading indexes, boundary conditions, etc. on the vertical deflection values, stress and natural frequency of TFGP doublycurved nanoshell was detected. This study offers readers valuable insights into nanoscale shell mechanics with multidirectionally graded material properties. Furthermore, the results are highly applicable for the practical design and analysis of MEM/NEMs structures.

In this work, a newly numerical approach for the global and local analysis of laminated composite plates, incorporating size-dependent effects through refined zigzag kinematics and nonlocal strain gradient theory, is developed. By applying the principle of virtual displacement, we derive a weak form of the equilibrium equations based on the nonlocal strain gradient theory, resulting in a local continuum model. This model inherently accounts for deviations in stiffness due to material inhomogeneity and interatomic forces, particularly at small scales. The refined zigzag theory is used to model the displacement field of the plates, which is numerically approximated using the NURBS-based isogeometric analysis technique. This approach allows us to account for the size-dependent characteristics of the laminate at micro/nanoscale through higher-order nonlocal parameters and nonlocal gradient length coefficients. We validate the proposed model for static and free vibration analyses by comparing our numerical results with those reported in the literature. As an original contribution, this research extensively explores the size-dependent effects on the layerwise local responses of laminated composite plates under static loading conditions with various stacking sequences and thickness ratios being investigated.

This study proposes a finite element approach combining higher-order shear plate theory to analyze the dynamic and static stability of tri-directional functionally graded porous (TFGP) plates in high-temperature environments on an elastic medium. The plate structure takes into account initial geometrical imperfections (hereafter referred to as the TFGP-imperfection plate). The material properties vary along three directions and are composed of four different materials, providing the structure with significant mechanical characteristics. Transcendental functions are used to formulate two types of geometric imperfections: the cosine-type global/localized imperfections, which are simulated using cosine and hyperbolic functions, and the sine-type imperfection, which is globally eccentric/symmetrical and represented by sine and exponential functions. The finite element approach, along with Bolotin's approach, is employed to determine the dynamic instability regions of the TFGP-imperfection plate. The accuracy of the method is validated through numerical comparisons with analytical or other reliable numerical methods available in the literature. The findings provide valuable insights into the structural behavior of TFGP-imperfection plates under extreme conditions, offering significant implications for military applications such as armored vehicles and aerospace components.

In the present study, mechanical behavior of Ceresit 20 mortar is investigated through non-destructive experimental modal testing on a mortar beam. The objective was to determine the Young's modulus (E) in three principal directions longitudinal, transverse, and vertical in terms of bending frequencies. Torsion mode data was utilized in order to calculate the shear modulus (G) and to calculate Poisson's ratio (ν). A finite element model was used for validation, with very good correlation with experiment. Besides, a new Machine Learning (ML) model, Self-Attention Interpretable Neural Transformer optimized by Honey Badger Algorithm (SAINT-HBA), was introduced to predict crack depth of mortar beams. Utilizing bending and torsion frequencies of failed specimens, the model predicted single and multiple crack scenarios very well. SAINT-HBA outperformed hybrid SAINT models that used Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Ecosystem-based Optimization (AEO) and was found to be useful for structural health monitoring applications.

Using an analytical approach based on the Galerkin method, a study on the static buckling behavior of functionally graded (FG) laminated shells reinforced with carbon nanotube (CNT) and carbon fibers is presented in this paper. The shapes considered are plate, cylindrical, spherical, elliptical-paraboloidal, and hyperbolic-paraboloidal shells. The variation of the volume fraction through the thickness of the shell is linear, while the percentage of CNT is distributed linearly over all layers of the shell in a constant manner according to four types of configuration: UD, FG-X, FG-O, and FG-V. The influences of various geometrical and material factors on the static buckling of shell structures were then examined using a parametric study.

The cervical spine is an essential structure in human physiology but is vulnerable to diseases, such as lesions, disc herniation, and vertebral fractures. Finite element (FE) modeling represents a powerful approach for predicting the biomechanics of the cervical spine under various loading conditions. Conventional methods usually do not take into account the consistency of the material properties, which potentially restricts their capability to realistically represent biomechanical behavior. This study presents a novel calibration methodology aimed at enhancing the predictive biofidelity of FE models for cervical spine biomechanics. The proposed approach systematically identifies optimal material properties to better replicate in vitro biomechanical responses. To achieve this, the cervical spine geometry was reconstructed from computed tomography (CT) scans and validated against cadaveric morphometric data to ensure anatomical accuracy. Mechanical properties of both hard and soft tissues were collected based on a comprehensive review of the literature. A dataset of biomechanical responses was generated through FE simulations using a range of material properties. The calibration process integrated an adaptive neuro-fuzzy inference system (ANFIS) framework with genetic algorithms, followed by a rigorous validation step against experimental benchmarks to ensure precise replication of in vitro test outcomes. The results show that this calibrated FE model significantly improves cervical spine biomechanics predictions, accurately matching intervertebral disc mechanics and ligament behavior. This research provides a robust framework, integrating numerical modeling with experimental studies, guiding future biomechanical research to potentially improve clinical and surgical outcomes. Some of the prominent applications are injury analysis, degenerative disease modeling, and the prediction of spinal deformity.

The main goal of the article is to analyze the vibration of graphene platelet-reinforced functionally graded triply periodic minimal surface nanoplates (GPLR-FG-TPMS nanoplates) embedded in elastic foundation. The displacement field is determined using HSDT. Nonlocal elasticity theory is used to account for the small-size effects inherent in nanostructures due to its simplicity and efficiency. The motion equation is derived from Hamiltonian principle and solved by the IGA procedure. Parametric studies are performed to verify the accuracy of proposed method and to show the influence of geometrical parameters, material properties, foundation stiffness, and boundary conditions on the vibration of GPLR-FG-TPMS nanoplates.

This study introduces a new optimization method called K-means Optimizer (KO). The special feature of this algorithm lies in the combination of K-means clustering to determine the centroid vectors—representing the regions with high potential in the search space for solutions. Based on those centroids, the algorithm applies two flexible moving strategies, allowing it to both explore new regions and effectively exploit known regions, in order to find the best solution. To evaluate the effectiveness, the KO algorithm is applied to the optimization problem of a 26-storey truss tower structure with a total of 942 bars and 244 nodes, using 59 design variables. Then, the results from KO are compared with two other popular optimization algorithms, ETO and PSO. The results show that the KO algorithm achieves the smallest optimal value and converges faster than the other two methods. Specifically, KO ranks first in performance among the three algorithms, demonstrating its ability to solve optimization problems efficiently. This shows that KO not only performs well on complex models, but is also a reliable choice for engineering problems that require high accuracy and computational efficiency.