Showing 515 results for Type of Study: Research
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.
R. Sheikholeslami, A. Kaveh,
Volume 15, Issue 1 (1-2025)
Abstract
The stability of large complex systems is a fundamental question in various scientific disciplines, from natural ecosystems to engineered environmental networks. This paper examines the interplay between network complexity and stability through the lens of graph theory and spectral analysis, based on Robert May’s seminal work on stability in randomly connected networks. Environmental systems are modeled as graphs in which components, such as reservoirs in a water distribution system or physical processes in hydrological cycle, interact through defined connections of varying strengths. Stability in these networks depends on the level of connectivity, the number of interacting components, and the strength of interactions between them. Previous studies have shown that as a system becomes more interconnected, it reaches a threshold beyond which it transitions sharply from stability to instability. Using concepts from spectral graph theory, we show how structural properties of an environmental network—such as degree distribution, modularity, and spectral characteristics—shape stability. Two numerical examples are presented to illustrate how increasing connectivity affects stability in water resource networks modeled as random graphs. The results suggest that systems with many weak interactions are generally more stable, whereas systems with fewer but stronger interactions are more prone to instability unless their structure is carefully managed. These insights provide valuable insights for designing resilient environmental networks and optimizing the management of interconnected natural and engineered systems.
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. Ilchi Ghazaan, M. Sharifi,
Volume 15, Issue 2 (4-2025)
Abstract
This paper introduces a novel two-phase metamodel-driven methodology for the simultaneous topology and size optimization of truss structures. The approach addresses critical limitations in computational efficiency and solution quality. The framework integrates the Flexible Stochastic Gradient Optimizer (FSGO) with adaptive sampling and machine learning to minimize the number of structural analyses (NSAs), while achieving lighter, high-performance designs. In Phase One, FSGO employs a dual global-local search strategy governed by Extensive Constraints (EC), a dynamic constraint relaxation mechanism to balance exploration of unconventional topologies and exploitation of optimal member sizes. By creating adaptive margins around design constraints, EC enables broader exploration of the design space while ensuring feasibility. Phase Two focuses on precision size optimization, leveraging pruned metamodels trained on critical regions of the design space to refine cross-sectional areas for the finalized topology. Comparative evaluations on benchmark planar and spatial trusses demonstrate the method’s superiority: it reduces NSAs by 22–79% compared to state-of-the-art approaches and achieves 0.04–0.7% lighter designs while eliminating up to 31% of redundant members. Results validate the framework as a paradigm shift in truss optimization, merging computational efficiency with structural innovation.
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.
R. Kamgar, A. Ahmadi, A. Ghale Sefidi,
Volume 15, Issue 2 (4-2025)
Abstract
This paper utilized the multi-objective cuckoo search (mocs) optimization algorithm to compute the optimum parameters of three-dimensional frame structures controlled by the triple friction pendulum bearing (TFPB) systems. For this purpose, firstly, the maximum capacity of the unisolated structure (uncontrolled structures) is evaluated for six main earthquakes using an incremental dynamic analysis (IDA). Then, the structure is controlled using the TFPB systems and excited using the maximum acceleration calculated from the previous step to calculate the optimal parameters of the TFPB system (i.e., the coefficients of friction and effective radius of curvature) subjected to some constraints in such a way that the maximum local drift ratio and also the Park-Ang damage index ratio minimized. Finally, to evaluate the behavior of the controlled structure, it is excited by main shock-aftershock earthquakes under sequence IDA. The results showed an average seismic improvement of 30% and 40% for the controlled structures according to the Park-Ang damage and drift indices, respectively.
T. Bakhshpoori, M. Heydari,
Volume 15, Issue 2 (4-2025)
Abstract
In this research, different types of weirs have been numerically investigated to determine the optimal design based on two hydraulic and structural criteria. FLOW 3D and ABAQUS software were utilized for the hydraulic and structural analysis, respectively. The accuracy of the numerical models was verified with the available experimental and numerical results. In the hydraulic investigation, 18 models of different types of weirs including rectangular (6 models), square, triangular (3 models), circular, ogee (3 models), and labyrinth (4 models) weirs were examined. In the structural study of weirs, there are 13 models, including rectangular, square, triangular (3 models), circular, ogee (3 models), and labyrinth (4 models) weirs were analyzed. The results of hydraulic analyzes showed that the dimensions of the rectangular weir significantly affect the output velocity. In triangular weirs, the highest energy dissipation will occur with an apex angle of 45°, and with the increase of the apex angle in the ogee weir, more turbulence is observed in the downstream flow. In labyrinth weirs, by changing the shape of the weir from triangular to rectangular, the output velocity and also turbulence of the flow will be much less. According to the findings of the structural analyses, the increase of the apex angle in triangular weirs, the weir will be more critical, but the situation will be more suitable in ogee weirs. Additionally, the rectangular labyrinth weir performs the best structurally among the labyrinth weirs.
A. Kaveh, A. Eskandari,
Volume 15, Issue 2 (4-2025)
Abstract
Metaheuristic algorithms mostly consist of some parameters influencing their performance when faced with various optimization problems. Therefore, this paper applies Multi-Stage Parameter Adjustment (MSPA), which employs Extreme Latin Hypercube Sampling (XLHS), Primary Optimizer, and Artificial Neural Networks (ANNs) to a recently developed algorithm called the African Vulture Optimization Algorithm (AVOA) and a well-known one named Particle Swarm Optimization (PSO) for tuning their parameters. The performance of PSO is tested against two engineering and AVOA for two structural optimization problems, and their corresponding results are compared to those of their default versions. The results showed that the employment of MSPA improved the performance of both metaheuristic algorithms in all the considered optimization problems.
M. Paknahad, P. Hosseini, A. R. Mazaheri, A. Kaveh,
Volume 15, Issue 2 (4-2025)
Abstract
This study presents a novel approach for optimizing critical failure surfaces (CFS) in homogeneous soil slopes by incorporating seepage and seismic effects through the Self-Adaptive Enhanced Vibrating Particle System (SA_EVPS) algorithm. The Finite Element Method (FEM) is employed to model fluid flow through porous media, while Bishop's simplified method calculates the Factor of Safety (FOS). Two benchmark problems validate the proposed approach, with results compared against traditional and meta-heuristic methods. The SA_EVPS algorithm demonstrates superior convergence and accuracy due to its self-adaptive parameter optimization mechanism. Visualizations from Abaqus simulations and comprehensive statistical analyses highlight the algorithm's effectiveness in geotechnical engineering applications. The results show that SA_EVPS consistently achieves lower FOS values with smaller standard deviations compared to existing methods, indicating more accurate identification of critical failure surfaces.
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.
S. Talatahari, B. Nouhi,
Volume 15, Issue 3 (8-2025)
Abstract
The emergence of Generative Artificial Intelligence (GenAI) presents new possibilities for transforming structural optimization processes in civil and structural engineering. Unlike traditional AI models focused on prediction or classification, GenAI models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, and Large Language Models (LLMs), enable the generation of novel structural designs by learning complex patterns within design-performance data. This paper provides a comprehensive review of how GenAI can support tasks such as design generation, inverse design, data augmentation for surrogate modeling, and multi-objective trade-off exploration. It also examines key challenges, including constraint integration, model interpretability, and data scarcity. By evaluating recent applications and proposing hybrid frameworks that blend generative modeling with domain knowledge and optimization strategies, this study outlines a research roadmap for the responsible and effective use of GenAI in structural optimization. The findings emphasize the need for interdisciplinary collaboration to translate GenAI’s creative potential into physically valid, structurally sound, and engineering-relevant solutions.
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.
M. Shahrouzi, A.m. Taghavi,
Volume 15, Issue 3 (8-2025)
Abstract
Sound Energy Optimizer (SEO) is a recent metaheuristic algorithm inspired by the propagation and reception of sound waves in physical environments. While conventional metaheuristics that rely on random number generators with certain distributions, SEO can utilize various real-world or simulated sound signals as the source of stochasticity to guide its search process. Concerning structural design by SEO, the effect of natural sound signals is compared with the artificial signals generated from uniform or normal distributions. In this regard, a 244-bar power transmission tower and a 1016-bar double-layer grid are simultaneously optimized with continuous geometry as well as discrete sizing variables to evaluate the impact of input signals on convergence behavior, solution quality and robustness of the algorithm. A sensitivity analysis is conducted to calibrate key control parameters of SEO. The results declare that the nature of the input sound signal can significantly affect the algorithm’s exploration-exploitation balance. In this study, the "Knocking sound" signal yields the best performance, while the synthetic random signals revealed less stable optimization trajectories.
K. Farzad, S. Ghaffari,
Volume 15, Issue 3 (8-2025)
Abstract
The use of steel shear wall systems has increased significantly in recent years as an effective solution for resisting lateral loads in buildings. This study focuses on the seismic collapse safety assessment of steel frames with optimal positions of steel shear walls obtained through various metaheuristic optimization algorithms and concepts of performance-based design methodology. Due to potential irregularities and discontinuities in the lateral load-resisting system and the limitations of code-based linear analysis, nonlinear pushover analyses with multiple lateral load patterns are employed to estimate key structural responses during the optimization process. The seismic collapse performance of the optimized frames is further evaluated using the FEMA P-695 methodology, which involves nonlinear dynamic analysis to assess collapse capacity. The primary objective is to examine the influence of steel plate shear wall placement on the structural weight optimization of steel frames. To this end, two case studies, a 10-story and a 15-story steel frame equipped with steel shear walls, are presented. The results demonstrate the critical role of shear wall location in achieving optimal structural designs.
M. Paknahad, P. Hosseini, A. Kaveh,
Volume 15, Issue 3 (8-2025)
Abstract
This study presents the application of the Self-Adaptive Enhanced Vibrating Particle System (SA-EVPS) algorithm for large-scale dome truss optimization under frequency constraints. SA-EVPS incorporates self-adaptive parameter control, memory-based learning mechanisms, and statistical regeneration strategies to overcome limitations of traditional metaheuristic algorithms in structural optimization. The algorithm's performance is evaluated on three benchmark dome structures: (1) a 600-bar single-layer dome with 25 design variable groups, (2) an 1180-bar single-layer dome with 59 design variable groups, and (3) a 1410-bar double-layer dome with 47 design variable groups, all subject to natural frequency constraints. Comparative analysis against five state-of-the-art algorithms—Dynamic Particle Swarm Optimization (DPSO), Colliding Bodies Optimization (CBO), Enhanced Colliding Bodies Optimization (ECBO), Vibrating Particles System (VPS), and Enhanced Vibrating Particles System (EVPS)—demonstrates SA-EVPS's superior convergence characteristics and solution quality. Results show that SA-EVPS consistently achieves the lowest structural weights with remarkable stability across all test cases. The algorithm's self-adaptive mechanisms eliminate manual parameter tuning while the statistical regeneration mechanism prevents premature convergence in large-scale optimization problems. This research establishes SA-EVPS as a robust and efficient metaheuristic for frequency-constrained structural optimization of complex dome structures.
M. Khatibinia, A.h. Saeedi, E. Mohtashami,
Volume 15, Issue 3 (8-2025)
Abstract
Bracing-friction damper system (BFDS), one of passive control devices, consists of a Pall friction damper added in-line with a diagonal, which is utilized for the seismic retrofit of building structures. The BFDS can dissipate the input energy of earthquakes and mitigate a considerable amount of the hysteretic energy of structures. This study presents the optimal seismic retrofit of inelastic steel moment-resisting frames (SMRFs) through the optimum design of the BFDSs installed in each story of SMRFs. For this purpose, minimizing the maximum damage index of stories averaged over seven scaled earthquake excitations is selected as the objective function so that the story damage is uniformly distributed along the height of SMRFs. The damage index is calculated based on the Park-Ang damage model which is expressed based on a linear combination of deformation, moment, and absorbed hysteretic energy of structural elements imposed by an earthquake excitation. The results indicate that the optimized BFDSs-equipped SMRFs exhibits the better distribution of story damage than that of uncontrolled SMRFs. Finally, the seismic assessment of SMRFs is done by the fragility analysis. The results of the seismic fragility assessment demonstrate that the optimized BFDSs improve the seismic performance of retrofitted SMRFs compared to that of uncontrolled SMRFs at different damage states.
A. Asaad Samani, S. R. Hoseini Vaez, P. Hosseini,
Volume 15, Issue 3 (8-2025)
Abstract
This study addresses the critical necessity for optimized structural design under fire conditions, where conventional methods often prove inadequate. The research focuses on the optimal design of two three and nine story steel moment-resisting frames, without fireproofing protection. The optimization objectives were to minimize the structural weight while satisfying constraints under critical fire scenarios. The key design constraints included inter-story drift and the demand-to-capacity ratio of structural members. The study employed the Enhanced Vibrating Particles System (EVPS) and the Accelerated Water Evaporation Optimization (AWEO) algorithms. A significant aspect of the investigation involved analyzing various severe fire scenarios to identify which parts of the structures are most vulnerable during a fire event. The results demonstrate the effectiveness of the proposed optimization framework in achieving a lightweight yet resilient structural design that meets regulations under extreme thermal loading.
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.
Pooya Zakian, Pegah Zakian,
Volume 15, Issue 4 (11-2025)
Abstract
This study employs Monte Carlo simulation together with a deep feedforward neural network to predict the natural frequencies of truss domes under uncertainty. Material and/or geometric properties of these structures are modeled as random variables, and their influence on the natural frequencies is examined. Monte Carlo simulation is applied to perform stochastic eigenvalue analyses of the finite element models. To reduce computational cost, a deep neural network is trained to predict natural frequencies in place of repeated eigenvalue solves, accelerating the overall simulation. Bayesian optimization is used to tune the network hyperparameters. Numerical examples show that the proposed approach substantially improves computational efficiency and predictive accuracy compared with direct Monte Carlo simulation for domes with random inputs.
M. Fahimi Farzam, M. Salehi,
Volume 15, Issue 4 (11-2025)
Abstract
Reducing the degrees of freedom of building models significantly reduces computational costs in time-consuming structural engineering problems, such as dynamic analysis, nonlinear analysis, or the optimal design of structural systems. In this study, the Finite Element (FE) model of a 20-story benchmark steel building with numerous degrees of freedom (DoF) is simplified to a 20-degree-of-freedom linear shear-type building. First, a preliminary linear shear-type model was derived by estimating the story stiffness so that the fundamental frequency matches that of the FE model. Then, an optimization problem is formulated and solved using a Genetic Algorithm (GA) combined with a weighted-sum method to achieve greater accuracy at higher frequencies in the preliminary model. Two objective functions were established and assessed for the optimization problem: one is the difference in frequencies between the FE model and the preliminary model with equal weighting, and the other is the first objective function improved with the modal participation percent weighting. The stiffness of each story in the preliminary model is selected as the design variable in both optimization problems. Finally, these optimized models are evaluated against the FE model using frequencies and dynamic time-history responses. The model derived from the weighted objective function demonstrates acceptable accuracy compared to its FE model in frequency and time-history analysis. It can be used for dynamic analysis and other structural and earthquake engineering purposes.
