Showing 95 results for Structure
A. Kaveh, S. Rezazadeh Ardebili,
Volume 13, Issue 3 (7-2023)
Abstract
This paper deals with the optimum design of the mixed structures that consists of two parts, a lower part made of concrete and an upper part made of steel. Current codes and available commercial software packages do not provide analytical solutions for such structural systems, especially if a decoupled analysis is performed where the lower part is excited by ground motion and its response of total accelerations is used for the upper part. Due to irregular damping ratios, mass and stiffness, dynamic response of each part of a mixed structure differs significantly. The present paper aims at comparing of the optimum design of these structures under the coupled and decoupled models. Toward that goal, the coupled and decoupled time history analyses are performed and the optimum design of the two methods are compared. The results of the two approach show that the cost of the decoupled analysis is higher than the cost of the coupled analysis and the design of the decoupled method may be uneconomical, because the interaction between the two upper and lower parts is neglected.
M. Mohamadinasab, G. Ghodarti Amiri, M. Mohamadi Dehcheshmeh,
Volume 13, Issue 4 (10-2023)
Abstract
Most structures are asymmetric due to functionality requirements and limitations. This study investigates the effect of asymmetry on damage detection. For this purpose, the asymmetry has been applied to models by considering different spans’ length and also different geometry properties for the section of members. Two types of structures comprising symmetric and asymmetric truss and frame have been modeled considering multiple damage scenarios and noise-contaminated data. Three objective functions based on flexibility matrix, natural frequency and modal frequency are proposed. These objective functions are optimized utilizing multiverse optimizer (MVO). For the symmetric models using limited modal data, flexibility-based objective function has the most accurate results, while by increasing the number of mode shapes, its accuracy reduced. Among asymmetric models of truss, damage detection results of the model is more accurate than those of its symmetric pair. Between asymmetric models of frame, the results obtained from frames which have only different spans’ length are more precise than those of the symmetric model. This is while frequency-based objective functions have their least accurate results for the frame model having asymmetry only in the section properties of its elements.
M. Shahrouzi, S.-Sh. Emamzadeh, Y. Naserifar,
Volume 13, Issue 4 (10-2023)
Abstract
Shape optimization of a double-curved dam is formulated using control points for interpolation functions. Every design vector is decoded into the integrated water-dam-foundation rock model. An enhanced algorithm is proposed by hybridizing particle swarm algorithm with ant colony optimization and simulated annealing. The best experiences of the search agents are indirectly shared via pheromone trail deposited on a bi-partite characteristic graph. Such a stochastic search is further tuned by Boltzmann functions in simulated annealing. The proposed method earned the first rank in comparison with six well-known meta‑heuristic algorithms in solving benchmark test functions. It captured the optimal shape design of Morrow Point dam, as a widely addressed case-study, by 21% reduced concrete volume with respect to the common USBR design practice and 16% better than the particle swarm optimizer. Such an optimal design was also superior to the others in stress redistribution for better performance of the dam system.
A. Kaveh, A. Zaerreza,
Volume 13, Issue 4 (10-2023)
Abstract
This paper presents the chaotic variants of the particle swarm optimization-statistical regeneration mechanism (PSO-SRM). The nine chaotic maps named Chebyshev, Circle, Iterative, Logistic, Piecewise, Sine, Singer, Sinusoidal, and Tent are used to increase the performance of the PSO-SRM. These maps are utilized instead of the random number, which defines the solution generation method. The robustness and performance of these methods are tested in the three steel frame design problems, including the 1-bay 10-story steel frame, 3-bay 15-story steel frame, and 3-bay 24-story steel frame. The optimization results reveal that the applied chaotic maps improve the performance of the PSO-SRM.
A. Yadbayza-Moghaddam, S. Gholizadeh,
Volume 14, Issue 1 (1-2024)
Abstract
The primary objective of this paper is to propose a novel technique for hybridizing various metaheuristic algorithms to optimize the size of discrete structures. To accomplish this goal, two well-known metaheuristic algorithms, particle swarm optimization (PSO) and enhanced colliding bodies optimization (ECBO) are hybridized to propose a new algorithm called hybrid PSO-ECBO (HPE) algorithm. The performance of the new HPE algorithm is investigated in solving the challenging structural optimization problems of discrete steel trusses and an improvement in results has been achieved. The numerical results demonstrate the superiority of the proposed HPE algorithm over the original versions of PSO, ECBO, and some other algorithms in the literature.
Dr V.r. Mahdavi, Prof. A. Kaveh,
Volume 14, Issue 3 (6-2024)
Abstract
In order to evaluate the damage state, value, and position of structural members more accurately, a multi-objective optimization (MO) method is utilized that is based on changes in natural frequency. The multi-objective optimization dynamic-based damage detection method is first introduced. Two objective functions for optimization are then introduced in terms of changing the natural frequencies and mode shapes. The multi-objective optimization problem (MOP) is formulated by using the two objective functions. Three considered MO algorithms consist of Colliding Bodies Optimization (MOCBO), Particle Swarm Optimization (MOPSO), and non-dominated sorting genetic algorithm (NSGA-II) to achieve the best structural damage detection. The proposed methods are then applied to three planar steel frame structures. Compared to the traditional optimization methods utilizing the single-objective optimization (SO) algorithms, the presented methods provide superior results.
A.r. Hajizadeh, M. Khatibinia, D. Hamidian,
Volume 14, Issue 3 (6-2024)
Abstract
The contourlet transform as an extension of the wavelet transform in two dimensions uses the multiscale and directional filter banks, and has a more adequate performance in comparison with the classical multi-scale representations. In this study, the efficiency of the contourlet transform is assessed for identifying the damage of plate structures in various conditions. The conditions include single damage and multi–damages with different shapes and severities, the different supports (i.e., boundary conditions), and the higher mode shapes,. For achieving this purpose, the process of the damage detection of plate structures using contourlet transform is implemented in the three steps. In the first step, the first mode shapes of a damaged plate and a reference state as the intact plate are obtained using the finite element method. In the second step, the damage indices are achieved by applying the contourlet transform to the responses of the first mode shapes for the damaged and intact plates. Finally, the location and the approximate shape of the damage are identified by plotting the damage indices. The obtained results indicate that the various conditions influence the performance of the contourlet transform for identifying the location and approximate shape of damages in plate structures.
M.h. Talebpour , S.m.a Razavizade Mashizi, Y. Goudarzi ,
Volume 15, Issue 1 (1-2025)
Abstract
The optimization process of space structures considering the nonlinear material behavior requires significant computational efforts due to the large number of design variables and the complexities of nonlinear structural analysis. Accordingly, the Force Analogy Method (FAM) serves as an efficient tool to reduce computational workload and enhance optimization speed. In this study, the weight optimization of space structures in the inelastic region under seismic loading is carried out using the Shuffled Shepherd Optimization Algorithm (SSOA), with the nonlinear structural analysis based on the FAM. To do this, the FAM formulation for axially loaded members of space structures under seismic forces is presented. Subsequently, weight optimization is performed on two double-layer space structures: a flat double-layer structure with 200 members and a barrel vault structure with 729 members under the Kobe earthquake record. Based on the results, the optimized design using the inelastic behavior showed that the FAM provided accurate results when compared to the precise nonlinear structural analysis. The optimized design based on the FAM is considered acceptable, and the computational time for the optimization process has been significantly reduced.
A. Kaveh, Sh. Rezazadeh Ardebili,
Volume 15, Issue 1 (1-2025)
Abstract
Identification of damping properties for a mixed structure and its interaction with underlying soil is a challenge for structural designers. Current codes and available commercial software packages do not provide analytical solutions for such structural systems. Due to irregular damping ratios, dynamic response of each part of a mixed structure differs significantly. In addition, when the structure is subjected to seismic loads, the soil-structure interaction effects cannot be neglected. To manage these issues, this paper proposes an equivalent damping ratio for mixed structures by means of a semi-empirical error minimization method which considers soil-structure interaction. The results of numerical simulations indicate that the use of the equivalent damping ratios makes the results of dynamics analyses closer to the ones obtained by the actual damping ratios. Consequently, proposed method provides a much better approximation than the case in which the conservative overall ratio of 2% or 5% is used.
M. Paknahd, P. Hosseini, A. Kaveh, S.j.s. Hakim,
Volume 15, Issue 1 (1-2025)
Abstract
Structural optimization plays a crucial role in engineering design, aiming to minimize weight and cost while satisfying performance constraints. This research presents a novel Self-Adaptive Enhanced Vibrating Particle System (SA-EVPS) algorithm that automatically adjusts algorithm parameters to improve optimization performance. The algorithm is applied to two challenging examples from the International Student Competition in Structural Optimization (ISCSO) benchmark suite: the 314-member truss structure (ISCSO_2018) and the 345-member truss structure (ISCSO_2021). Results demonstrate that SA-EVPS achieves significantly better solutions compared to previous studies using the Exponential Big Bang-Big Crunch (EBB-BC) algorithm. For ISCSO_2018, SA-EVPS achieved a minimum weight of 16543.57 kg compared to 17934.3 kg for the best EBB-BC variant—a 7.75% improvement. Similarly, for ISCSO_2021, SA-EVPS achieved 4292.71 kg versus 4399.0 kg for the best EBB-BC variant—a 2.42% improvement. The proposed algorithm also demonstrates superior convergence behavior and solution consistency, with coefficients of variation of 3.13% and 1.21% for the two benchmark problems, compared to 12.5% and 2.4% for the best EBB-BC variant. These results highlight the effectiveness of the SA-EVPS algorithm for solving complex structural optimization problems and demonstrate its potential for engineering applications.
H. Sheikhpour, S. H. Mahdavi, S. Hamzehei-Javaran, S. Shojaee,
Volume 15, Issue 2 (4-2025)
Abstract
Accurate detection and localization of impacts in structural systems are crucial for safety and enabling effective structural health monitoring (SHM). This paper aims to identify multiple consecutive impacts in framed structures with unknown dynamic properties, using time-domain acceleration data. Traditional methods often struggle under complex conditions such as noisy environments and multiple impacts. To overcome these limitations, we propose a deep learning-based framework utilizing Convolutional Neural Networks (CNNs) to extract intricate patterns from acceleration signals. Input data are generated through high-fidelity numerical simulations based on the Finite Element Method (FEM), allowing precise control over impact characteristics and their spatial distribution. A fixed-length sliding window is employed to segment the acceleration time series, enabling the model to perform localized and near-real-time impact detection. To further improve model performance, Bayesian optimization is utilized for hyperparameter tuning, enhancing accuracy and efficiency over traditional grid search. The proposed model is numerically evaluated on two-dimensional structures: a steel pin-jointed camel-back truss and a shear frame. The results reveal that the proposed strategy achieves high accuracy in estimating the location, timing, and magnitude of impacts, even under noisy conditions. The key novelty of this research lies in combining deep learning with advanced optimization techniques to solve the impact detection problem in structures with unknown parameters. These findings establish a robust framework for advancing intelligent, data-driven SHM systems, with direct applications in real-world infrastructure. The proposed methodology demonstrates significant potential to mitigate economic costs and safety risks associated with structural failures under impact loading.
M. Shahrouzi, M. Fahimi Farzam, J. Gholizadeh,
Volume 15, Issue 2 (4-2025)
Abstract
The tuned mass damper inerter systems have recently received considerable attention in the field of structural control. The present work offers a practical configuration of such a device, called double tuned mass damper inerter (DTMDI) that connects the inerter into the damper masses rather than be attached to the main structure. Soil-structure interaction is also taken into account for the soft and dense soils as well as for the fixed based condition. The H∞ norm of the transfer functions for the roof response is minimized as the objective function. The parameters of DTMDI are optimized using opposition-switching search as an efficient parameter-less algorithm in comparison with lightning attachment procedure optimization, sine cosine algorithm and particle swarm optimization. The system performance is evaluated in the frequency domain, as well as in the time domain under various earthquakes including far-field records, near-field records with forward directivity and with fling-step. The results show superiority of opposition-switching search for optimal design of the proposed DTMDI so that it can significantly reduce both the roof displacement and acceleration response for all the SSI conditions.
Kh. Soleymanian, S. M. Tavakkoli,
Volume 15, Issue 2 (4-2025)
Abstract
This study aims to deal with multi-material topology optimization problems by using the Methods of Moving Asymptotes (MMA) method. The optimization problem is to minimize the strain energy while a certain amount of material is used. Several types of structures, including plane, plate and shell structures, are considered and optimal materials distribution is investigated. To parametrize the topology optimization problem, the Solid Isotropic Material with Penalization (SIMP) method is utilized. Analytical sensitivity analysis is performed to obtain the derivatives of the objective function and volume constraints with respect to the design variables. Two types of material with different modulus of elasticities are considered and, therefore, each element has two design variables. The first design variable represents the presence or absence of material in an element, while the second design variable determines the type of material assigned to the element. In order to analyze the structures required during the optimization process, the ABAQUS software is employed. To integrate the topology optimization procedure with ABAQUS model, a Python script is developed. The obtained results demonstrate the performance of the proposed method in generating reasonable and effective topologies.
A. Kaveh,
Volume 15, Issue 3 (8-2025)
Abstract
In this paper, a review is provided for the optimal analysis of structures using the graph theoretic force method. An analysis is defined as “optimal” if the corresponding structural matrices (flexibility or stiffness) are sparse, well-structured, and well-conditioned. An expansion process together with the union-intersection theorem is utilized for generating subgraphs, forming a special cycle basis, corresponding to highly localized self equilibration systems. Admissibility checks are used in place of the more common independence checks to speed up the formation of the basis. An efficient solution requires organizing the non-zero entries into various well-defined patterns. Algorithms are provided to form matrices having banded matrices and small profiles. Though the paper considers mainly skeletal structures, the presented concepts are easily extensible to other finite element models. References for such generalizations have been provided. A brief review of swift analysis methods that skirt the harder problem of matrix conditioning is also provided. The iterative nature of optimal structural design via metaheuristic algorithms rewards any speedup in the analysis process. This review recommends utilizing the force method instead of the alternative displacement method to achieve said speedup. The work concludes with a discussion of future challenges in the field of optimal analysis.
A. Kaveh, S.m. Hosseini, K. Biabani Hamedani,
Volume 15, Issue 3 (8-2025)
Abstract
This paper presents the application of the Plasma Generation Optimization (PGO) algorithm to the optimal design of large-scale dome trusses subjected to multiple frequency constraints. Such problems are notoriously challenging due to their highly non-linear and non-convex nature, characterized by numerous local optima. PGO is a physics-inspired metaheuristic that simulates the processes of excitation, de-excitation, and ionization in plasma generation, balancing global exploration and local refinement through its unique search mechanisms. The performance of PGO is evaluated on three well-established dome truss benchmarks: a 52-bar, a 120-bar, and a 600-bar structure, encompassing both sizing and sizing-shape optimization. A comprehensive statistical analysis based on multiple independent runs demonstrates the algorithm's effectiveness and robustness. The results show that PGO achieves the best-reported minimum weight for the 120-bar and 600-bar domes, while obtaining a highly competitive, near-optimal design for the 52-bar dome. Furthermore, PGO consistently produced low average weights across all problems, confirming its reliability. The convergence histories further validate the algorithm's efficiency in locating feasible, high-quality designs. The findings conclusively establish PGO as a powerful and reliable optimizer for handling complex structural optimization problems with dynamic constraints.
T. Payamifar, R. Sojoudizadeh, H. Azizian, L. Rahimi,
Volume 15, Issue 4 (11-2025)
Abstract
This paper presents an Enhanced Prairie Dog Optimization (IPDO) algorithm for solving complex engineering optimization problems. The proposed improvement integrates Lévy flight dynamics into the original PDO framework to enhance exploration-exploitation balance and accelerate convergence. The performance of IPDO is evaluated against seven established metaheuristics across four challenging civil engineering applications: (1) discrete sizing optimization of a 120-bar truss, (2) structural reliability analysis of a cantilever tube, (3) cost optimization of reinforced concrete beams, and (4) hyperparameter tuning of a Support Vector Machine (SVM) for shear strength prediction of steel fiber-reinforced concrete. Experimental results demonstrate that IPDO consistently achieves superior solution quality, robustness, and convergence speed. Notably, in SVM hyperparameter optimization, IPDO attained the lowest mean squared error (1.4881) with zero variance across runs, outperforming all competitors. The algorithm also proved highly effective in structural design and reliability problems, offering a reliable and efficient tool for real-world engineering optimization.
P. Rajabi , S. M. Tavakkoli,
Volume 16, Issue 1 (1-2026)
Abstract
This paper presents a method for detecting the location and severity of damage in shell structures. The method relies on extracting time-domain damage-sensitive features from vibrational responses and applying topology optimization. To achieve this, singular values are extracted from the Hankel matrix using singular value (SVD) decomposition and selected as damage-sensitive features. The damage detection problem is formulated as a topology optimization problem in which damage is modeled using the solid isotropic material with penalization (SIMP) method. Sensitivity analysis is carried out using the finite difference method to compute the derivatives of the objective function with respect to the design variables, thereby enabling efficient gradient-based optimization. The objective function is defined to minimize the differences between the singular values of the reference structure and those of the model. Abaqus software is used to perform dynamic finite element analysis of the shell model and to derive acceleration responses at selected nodes, which serve as sensor locations. The results from several numerical examples demonstrate the high capability of the proposed method in accurately identifying both the location and severity of damage.
A. Zaerreza, P. Hassanvand, S. R. Nabavian,
Volume 16, Issue 1 (1-2026)
Abstract
The VPS-SRM algorithm is an enhanced metaheuristic approach developed for structural optimization. While it demonstrates robust performance in structural design, its efficiency remains subject to improvement, especially when dealing with large-scale structural optimization problems. To address this, the present study introduces improved versions of the VPS-SRM by incorporating chaotic maps. The performance of these chaotic-based variants was evaluated through the optimization of large-scale structural problems, including a 3-bay 15-story frame, 520-bar double-layer grid, and 800-bar double-layer grid. The results indicate that the chaotic versions significantly outperform the original algorithm, providing superior structural designs with higher precision and enhanced statistical results. Statistical analysis via the Kruskal-Wallis test further confirms that the chaotic variants offer a substantial improvement over the standard VPS-SRM.
A. Paudel, S. Chhetri,
Volume 16, Issue 3 (7-2026)
Abstract
Optimization strategies have turned out to be a requirement in the design and construction of dams because of the increasing demand for water resources, hydropower generation, and flood control and due to the inbuilt high cost and complexities of such massive structures (1) (2) (3) (4) (5) (6) (7). This review paper summarizes recent advances on dam optimization, covering a broad scope of dam types, primary objectives, approaches, and performance evaluation indices. It highlights the vast transition from traditional, at times time-consuming, trial-and-error based design techniques and classical optimization methods to efficient meta-heuristic (MH) and hybrid algorithms that outperform in terms of efficiency, accuracy, and global exploration (1) (3) (8). The paper also touches upon the growing application of Reliability-Based Design Optimization (RBDO) and robust optimization towards dealing with material property uncertainty, loading, and environmental uncertainties to achieve safer and cost-efficient designs (9) (10) (11). Ideal optimization objectives, such as volume reduction of concrete and dam safety maximization, are explored along with several geometric, stress, stability, and frequency constraints. Finally, it discusses the ongoing problems, including the computational expense and selection of appropriate algorithms, and instilling future research directions essential for further advancement of dam engineering practice.
V. R. Mahdavi, A. Kaveh,
Volume 16, Issue 3 (7-2026)
Abstract
This paper uses multi-objective methods to improve the exploration for single-objective problems. This method involved splitting the objective function into two segments and these enhanced using specialized algorithms designed for handling multiple objective functions. Three MO algorithms include Colliding Bodies Optimization (MOCBO), Particle Swarm Optimization (MOPSO), and non-dominated sorting genetic algorithm (NSGA-II), which are used to get the best prediction of structural modal strain energy. The indicated method is then implemented to two spatial truss structures. The recently developed method has superior performance over the previous approach that relied on single-objective optimization (SO) algorithms.
