Showing 316 results for Optimization
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.
M. Arjmand, H. Naderpour, A. Kheyroddin,
Volume 15, Issue 4 (11-2025)
Abstract
The seismic resilience of existing reinforced concrete (RC) buildings can be improved by optimizing both energy dissipation and post-earthquake recovery. This study proposes a practical framework for upgrading RC moment-resisting frames using nonlinear fluid viscous dampers (NFVDs). Two typical frames, a four-story and an eight-story structure, were modeled and analyzed in OpenSees. Nonlinear time-history analyses with seven earthquake records were carried out to estimate the Park–Ang damage index, while incremental dynamic analyses (IDA) with 22 far-field records from FEMA P695 were used to evaluate fragility and collapse performance. The NFVDs were represented through a velocity-dependent Maxwell model, and the optimal damper parameters and locations were determined through a cost-based single-objective optimization scheme under predefined damage limits. The results show that the optimized damper configurations effectively reduced structural damage and improved post-event functionality recovery under seismic hazard levels corresponding to 10% and 2% probabilities of exceedance in 50 years. Overall, the proposed approach provides an efficient and economical solution for improving the seismic performance and resilience of existing RC frame buildings.
M. Ravan, H. Rahami, M. R. Shokoohfar,
Volume 16, Issue 1 (1-2026)
Abstract
Optimization is a key tool for solving complex engineering problems. This research introduces a novel particle swarm optimization algorithm in which all particles have a probability of being selected as guide particles, while the likelihood of each particle influencing others is determined proportionally to its performance. In other words, unlike the classical PSO algorithm where only the best particle is chosen as the fixed guide in each iteration, every particle can independently select its own guide based on the performance of other particles. This approach appears to prevent premature convergence of particles and enhance the exploration capability of the algorithm. Additionally, a parameter has been defined and investigated in this algorithm to adjust the ratio of exploration to exploitation power, which can be initialized according to the complexity type of the problem. The performance of the proposed algorithm was first evaluated using a set of benchmark mathematical functions, which confirmed the high accuracy of the algorithm in finding optimal solutions. Then, several truss design problems were examined as real structural case studies, and the obtained results indicate that the proposed algorithm exhibits suitable and acceptable performance compared with other well-known algorithms.
M. Shahrouzi, Y. Naserifar, A. M. Taghavi, S.-Sh Emamzadeh,
Volume 16, Issue 1 (1-2026)
Abstract
Although metaheuristic algorithms are popular tools for global optimization, none of them is reported as the best for all problems. Hybridization is an advanced solution to overcome the shortcomings of individual methods by using the power points of the others. Here, a popular swarm intelligent algorithm with high explorative capability is combined with an exploitative operator of differential evolution and some dynamic parameter variation, as well as a greedy operator to enhance the search refinement. The proposed method is evaluated on a variety of engineering and constrained engineering problems, including the optimal design of Belleville Spring, pressure vessel, car side impact problem, and Morrow point dam. According to the results, considerable improvement is observed with respect to the standard particle swarm optimizer as well as competitive performance with a number of metaheuristic algorithms.
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.
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.
Mr V. Jabbari, Dr H. Azizian, Dr R. Sojoudizadeh, Dr L. Rahimi,
Volume 16, Issue 2 (4-2026)
Abstract
Structural design seeks to achieve optimal performance with minimum cost while meeting code requirements. Evaluating optimized designs usually depends on finite element analysis, which is computationally expensive. Recently, surrogate models have been developed to predict structural behavior more efficiently. Among these, Support Vector Machine (SVM) has become a reliable tool in civil engineering. However, the predictive power of SVM is highly dependent on proper parameter tuning. This study introduces the Improved Electric Eel Foraging Optimization Algorithm (I-EEFO) for training SVM to estimate the response of steel frames. Two benchmark structures, a 2‑story and a 7‑story steel frame, were analyzed, and the results were compared with other metaheuristic algorithms. The proposed method achieved very high accuracy: mean squared errors of 1.11E‑13 for the 2‑story frame and 2.99E‑07 meters for the 7‑story frame over 10 runs. The root mean square errors for displacement prediction on test data were 2.67E‑07 and 7.23E‑04 meters, respectively, confirming reliable estimates. Convergence curves demonstrated that I‑EEFO converges faster and more effectively than competing methods. These findings highlight the potential of the proposed approach as a robust and computationally efficient alternative to traditional simulations, offering engineers a practical tool to reduce costs in structural design without compromising accuracy.
R. Kamgar, S. Rostami,
Volume 16, Issue 2 (4-2026)
Abstract
A new type of seismic control device is introduced—the elastoplastic inerter-tuned mass damper—which omits the conventional viscous damping element and instead incorporates stiffness with elastoplastic behavior. The key objective is to benchmark its earthquake performance against alternative vibration mitigation strategies, including the standard tuned mass damper, the elastoplastic tuned mass damper, and the inerter-tuned mass damper. For each configuration, the design variables are optimized by minimizing the Park–Ang damage index, thereby maximizing the structure’s seismic resilience. A nine-story moment-resisting frame is employed and is modeled with the OpenSees software nonlinearly. Simulation results reveal that the proposed elastoplastic inerter-tuned mass damper surpasses all other examined systems. Specifically, it delivers up to a 38% drop in the global damage index and a 50% damage reduction in the lower stories.
M. Goodarzi, S. A. Latifi Rostami,
Volume 16, Issue 2 (4-2026)
Abstract
In thermomechanical topology optimization, variations in material distribution influence not only the structural stiffness but also the temperature field and thermal loads induced by constrained thermal expansion. In this study, a moving morphable component (MMC)-based framework is proposed for thermomechanical topology optimization, in which the density-based representation of the SIMP method is replaced with an explicit geometry description using an MMC. In the proposed approach, steady-state heat conduction analysis, equivalent thermomechanical load formulation, and structural mechanical analysis are retained, whereas compliance minimization subject to a volume constraint is adopted as the optimization objective. The elemental density field is derived from the topology description and Heaviside functions, and the sensitivity of the objective function with respect to the geometric parameters of the MMC is evaluated using the chain rule. Numerical examples demonstrate that the proposed method achieves satisfactory thermomechanical performance while producing smoother boundaries and a more explicit geometric representation than the conventional SIMP method.
Mr R. Sepehri, Dr H. Azizian, Dr R. Sojoudizadeh, Dr S. Salehian,
Volume 16, Issue 3 (7-2026)
Abstract
The dual steel system comprising moment-resisting frames integrated with steel shear walls represents an advanced seismic-resistant solution in structural engineering. This system synergistically combines the high ductility and energy dissipation capacity of steel frames with the substantial lateral stiffness and strength provided by steel shear walls. Proper design of such systems requires precise determination of the optimal location, thickness, and mechanical properties of the shear walls parameters that critically govern seismic performance, structural safety, material efficiency, and construction cost. To achieve an optimal balance between performance and economy, the problem is formulated as a constrained optimization task. This study employs the recently developed Puma Optimizer (PO) and introduces a novel enhanced variant, termed the Upgraded Puma Optimizer (U-PO). The key novelty of this work lies in the integration of Lévy flight distribution into the PO framework, replacing conventional Brownian motion to significantly strengthen the exploration–exploitation balance, global search capability, and convergence speed. This modification enables more effective handling of complex, high-dimensional structural optimization problems. The performance of the proposed U-PO is rigorously evaluated through the optimal design of three benchmark steel frames (1-, 10-, and 20-story) equipped with shear walls. The primary objective is to minimize the total structural weight while satisfying strength, serviceability, and seismic design requirements according to relevant building codes. Decision variables include frame member cross-sections as well as the location and thickness of shear walls. Comparative results against several established metaheuristic algorithms (HHO, AOA, and GWO) demonstrate the superiority of the U-PO, confirming that the incorporation of Lévy flights leads to markedly improved convergence behavior and solution quality. The U-PO consistently yields superior designs featuring notable reductions in structural weight through more efficient sizing and strategic placement of steel shear walls.
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.
A. Bagherkhani,
Volume 16, Issue 3 (7-2026)
Abstract
Detecting the location and severity of damage, particularly in the early stages, is a significant topic in structural engineering. This study proposes a new identification method that can achieve third-level damage detection of structures. The approach involves conducting a static load test to measure the structure's static responses, along with a finite element model updating method that utilizes an optimization strategy. Based on the structure's static responses, a new damage index is introduced. This index comprises two components: a location index, which represents the residual values between structural responses in intact and damaged states, and a severity index, which is defined based on the correlation coefficient between structural responses in actual damage scenarios and the outcomes from the model updating process. Jaya optimization algorithm is used in this regard. The effectiveness of the proposed method is assessed through four numerical examples involving beam and frame structures under various damage scenarios. In addition, the method's performance is evaluated for noisy data. The results demonstrate the proposed method's effectiveness and high precision in identifying structural damage. However, the results also indicate that the proposed approach is highly sensitive to noisy data.
Mr. A. Alipour, Dr. D. Sedaghat Shayegan, Dr. A. A. Amirkardoust,
Volume 16, Issue 3 (7-2026)
Abstract
Deciding where to place temporary facilities on a construction site is one of the more consequential choices a project team makes, since that arrangement shapes how materials and people move around the site, how accessible different zones are, and how smoothly logistics run overall. This paper examines whether the Mouth Brooding Fish (MBF) algorithm can handle a standard construction site layout planning problem. The goal is to minimize the total distance that must be traveled between facilities that interact with each other. A well-known benchmark involving eleven facilities and eleven predetermined locations is solved with MBF and compared against Genetic Algorithm (GA), Ant Colony Optimization (ACO), Colliding Bodies Optimization (CBO), and Enhanced Colliding Bodies Optimization (ECBO). Fifty independent runs are performed, each with 200 iterations. The best solution obtained by MBF yields a total travel distance of 12,538, while the mean, worst, and standard deviation values are 12,608, 12,888, and 96, respectively. These figures place MBF in a competitive position relative to the other methods on this particular benchmark. The study does not claim direct field validation for oil and gas projects; rather, it demonstrates that the optimization framework works on a benchmark whose structure is relevant to industrial construction logistics. Further work that incorporates project-specific constraints and actual field data is recommended.
H. Fazli,
Volume 16, Issue 3 (7-2026)
Abstract
Large-scale structural sizing optimization involves discrete design variables, nonlinear constraints, and repeated computationally expensive structural analyses. This study proposes a Chaotic Quantum-Inspired Particle Swarm Optimization (CQPSO) algorithm that integrates chaotic search mechanisms into QPSO to improve exploration, population diversity, and convergence. The proposed method is evaluated on a range of planar and spatial truss and frame benchmarks and compared with PSO, QPSO, chaotic PSO, CLPSO, differential evolution, and Grey Wolf Optimizer. Five chaotic maps are systematically examined within the CQPSO framework. The results show that CQPSO generally provides lower structural weight, greater robustness, and higher feasibility than the reference methods. The tent map provides the most favorable overall performance, with convergence and diversity analyses indicating an improved balance between exploration and exploitation. Statistical tests confirm that the observed improvements are significant for most comparisons, while computational results show that CQPSO reaches competitive designs with fewer structural evaluations and lower CPU time. The proposed approach therefore provides an effective and computationally efficient framework for discrete structural sizing optimization.
