Showing 96 results for Kaveh
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.
P. Hosseini, A. Kaveh, A. Naghian, A. Abedi,
Volume 14, Issue 2 (2-2024)
Abstract
The global population growth and the subsequent surge in housing demand have inevitably led to an increase in the demand for concrete, and consequently, cement. This has posed environmental challenges, as cement factories are significant contributors to carbon dioxide emissions. One promising solution is to incorporate pozzolanic materials into concrete production. This study investigates the effects of using travertine sludge as a partial substitute for cement. Seven different mix designs, along with a control mix, were created and compared. The primary variable was the ratio of travertine sludge to cement weight, considered in intervals of 10%, 15%, 20%, 25%, 30%, 35%, and 40% of the cement's weight. Various tests were conducted, including compressive strength and flexural strength at ages of 7, 28, and 90 days, as well as a permeability test at 28 days. The findings revealed interesting patterns. At the 7-day mark, as the percentage of travertine sludge increased, there was a decrease in compressive strength. However, by the 28-day mark, the concrete displayed a varied behavior: using up to 30% travertine sludge by weight reduced the strength, but exceeding 30% resulted in increased strength. At the 90-day mark, an overall increase in strength was observed with the rise in travertine sludge percentage. Such pozzolanic effects on compressive strength were somewhat predictable. Additionally, based on the flexural strength tests, travertine sludge can be deemed a viable substitute for a certain percentage of cement by weight. This research underscores the potential of sustainable alternatives in the construction industry, promoting both professional development and personal branding for those engaged in eco-friendly practices.
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.
P. Hosseini, A. Kaveh, A. Naghian, A. Abedi,
Volume 14, Issue 3 (6-2024)
Abstract
This study aimed to develop and optimize artificial stone mix designs incorporating microsilica using artificial neural networks (ANNs) and metaheuristic optimization algorithms. Initially, 10 base mix designs were prepared and tested based on previous experience and literature. The test results were used to train an ANN model. The trained ANN was then optimized using SA-EVPS and EVPS algorithms to maximize 28-day compressive strength, with aggregate gradation as the optimization variable. The optimized mixes were produced and tested experimentally, revealing some discrepancies with the ANN predictions. The ANN was retrained using the original and new experimental data, and the optimization process was repeated iteratively until an acceptable agreement was achieved between predicted and measured strengths. This approach demonstrates the potential of combining ANNs and metaheuristic algorithms to efficiently optimize artificial stone mix designs, reducing the need for extensive physical testing.
A. Kaveh, N. Khavaninzadeh,
Volume 14, Issue 4 (10-2024)
Abstract
In this paper, a neural network is trained for optimal nodal ordering of graphs to obtain a small wavefront using soft computing. A preference function consists of six inputs that can be seen as a generalization of Sloan's function. These six inputs represent the different connection characteristics of graph models. This research is done with the aim of comparing Sloan's theoretical numbering method with Sloan's developed method with neural networks and WSA meta-heuristic algorithm. Unlike the Sloan algorithm, which uses two fixed coefficients, six coefficients are used here, based on the evaluation of artificial neural networks. The weight of networks is obtained using Water Strider algorithm. Examples are included to demonstrate the performance of the present hybrid method.
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.
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.
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.
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. 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.
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.
A. Kaveh, P. Salimi, H.a. Rahimi Bondarabadi,
Volume 15, Issue 4 (11-2025)
Abstract
This work investigates the optimization of concrete structures using metaheuristic algorithms-based reliability. One of the major challenges in the optimization of concrete structures is the extensive search domain, which may lead to convergence to local optima and incorrect results. In this study, instead of solely relying on optimization algorithms that are prone to local optima, a novel approach is proposed. Based on the Cascade Algorithm, this method discretized the search domain for section of beam and column dimensions and increased step by step. After each cross-section is created, it is assigned to the corresponding element. Subsequently, structural analysis is performed, and using reliability-based constraints and analysis, the least-cost section for each element is selected. Based on the obtained low-cost sections, the upper and lower bounds for each design variable are then narrowed. Finally, metaheuristic algorithms are applied to determine the optimal cross-sections with high precision. The results demonstrate that this approach significantly reduces the likelihood of falling into local optima and improves both the speed and accuracy of metaheuristic algorithms.
A. Kaveh, A. Beitollahi, N. Khavaninzadeh,
Volume 16, Issue 1 (1-2026)
Abstract
This study develops a synthetic earthquake catalog for Iran (1900–1963) using a deep neural network (DNN) optimized by the Enhanced Colliding Bodies Optimization (ECBO) algorithm. The model, trained on post-1964 instrumental data from the Iranian Seismological Center, incorporates spatial, temporal, and tectonic features to estimate earthquake magnitudes. Statistical indices (MAE = 0.0064; RMSE = 0.3748) and bootstrap uncertainty analysis (±0.18 M) confirm the model’s reliability. The generated catalog provides a data-driven basis for improving seismic hazard assessment and historical seismicity reconstruction across the Iranian plateau.
P. Hosseini, M. Paknahad, A. Kaveh,
Volume 16, Issue 1 (1-2026)
Abstract
Concrete mixture design optimization has evolved from traditional trial-and-error approaches to sophisticated computational methods. This paper presents a comprehensive review of optimization techniques applied to concrete mixture proportioning, covering statistical methods (Response Surface Methodology, Taguchi method), particle packing models, machine learning algorithms (Artificial Neural Networks, Random Forest, XGBoost, Support Vector Regression), and metaheuristic optimization techniques (Particle Swarm Optimization, Genetic Algorithms, EVPS, SA-EVPS). The review synthesizes findings from over 180 published studies, with detailed analysis of recent advances in artificial intelligence applications for multi-objective optimization of mechanical properties, cost, workability, durability, environmental sustainability, and structural performance. Key findings indicate that ensemble machine learning methods achieve superior prediction accuracy (R² > 0.95) for compressive strength, while metaheuristic algorithms effectively handle multi-objective trade-offs generating Pareto frontiers. The review also identifies critical research gaps including the need for standardized datasets, interpretable AI models, integration of life cycle assessment, and field validation of optimization results. Recent developments in self-adaptive algorithms (SA-EVPS) demonstrate improved convergence and solution quality for both material and structural optimization problems.
