Showing 541 results for Type of Study: Research
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.
A. R. Taghizadeh, S. Gholizadeh,
Volume 16, Issue 1 (1-2026)
Abstract
This paper employs a hybrid approach that integrates a metaheuristic algorithm with a properly trained neural network (NN) to perform seismic life‑cycle cost optimization of reinforced concrete (RC) frames within the framework of performance‑based design. In the proposed hybrid methodology, the center of mass optimization (CMO) metaheuristic algorithm is used to explore the design space. Additionally, a properly trained NN model is employed to estimate the nonlinear seismic response of the RC frames in order to evaluate the design constraints and compute the life‑cycle cost during the optimization process within a reasonable computational time. The efficiency of the proposed hybrid methodology is assessed through two performance‑based design optimization case studies involving 5‑ and 10‑story RC frames. The numerical results demonstrate that the proposed approach is an effective tool for optimizing the life‑cycle cost of RC frames by substantially reducing the computational burden of the optimization process.
K. Farzad, M. Javanmard Barbin,
Volume 16, Issue 1 (1-2026)
Abstract
This study investigates the optimal design of steel frames with chevron bracing systems and semi-rigid connections using a performance-based design framework and metaheuristic optimization algorithms. Optimization effectively balances design performance and cost in structural engineering. The three algorithms employed were selected based on their proven application to similar optimization problems, enabling identification of the most suitable approach for the present case. Chevron bracing offers architectural benefits and enhances lateral stiffness and strength. However, unbalanced vertical forces from tension and compression braces under seismic loading require nonlinear analysis for reliable capacity estimation. To address this, pushover analyses with multiple lateral load patterns are performed to capture responses consistent with performance-based design principles. Connection behavior plays a decisive role in the global performance of steel frames. Conventional assumptions of rigid or pinned connections oversimplify reality and produce inaccurate predictions. In this study, semi-rigid connections are modeled with greater fidelity by incorporating column panel zones (CPZs) and gusset plate stiffness at bracing joints. CPZs significantly influence energy dissipation and deformation, while gusset plates may contribute up to 40% of connection rotational stiffness. Neglecting these effects can underestimate interstory drift and misrepresent hinge mechanisms. Results show that accounting for initial connection stiffness improves both accuracy and cost efficiency. For 10- and 15-story frames, structural cost were reduced by about 7%, underscoring the value of realistic connection modeling in optimal design.
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.
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.
A. S. Hadi Ajli, S. Gholizadeh,
Volume 16, Issue 2 (4-2026)
Abstract
This paper aims to predict the maximum inter-story drift ratios of steel moment-resisting frame (MRF) structures under seismic loading, corresponding to different performance levels, using cascade-forward back-propagation (CFBP) neural network models. To this end, CFBP networks with varying numbers of hidden layer neurons are trained on nonlinear time-history analysis results of 6- and 12-story planar steel MRFs subjected to a suite of earthquake ground motions. The predictive performance of the trained models is systematically compared. Numerical results demonstrate that CFBP networks with 15 neurons in the hidden layer consistently outperform other network architectures, yielding more accurate predictions of the maximum inter-story drift ratios at each seismic performance level for both frame heights. These findings highlight the potential of moderately sized CFBP networks as efficient surrogates for nonlinear dynamic analysis in performance-based seismic assessment.
R. Javanmardi, H. Rahami,
Volume 16, Issue 2 (4-2026)
Abstract
This paper presents a novel framework for structural reliability assessment of buildings incorporating Concrete-Filled Steel Tubular columns, utilizing a deep surrogate model formulated in the complex number domain. High-fidelity numerical models are developed using SAP2000 software, with analysis outputs pre-processed in MATLAB. A hybrid deep learning architecture is implemented within the PyTorch framework, featuring complex-valued parameters and activation functions that enable superior representation of phase-dependent and oscillatory behaviors inherent in nonlinear limit state functions. Each complex parameter simultaneously encodes both real and imaginary influences, enhancing representational efficiency while requiring fewer parameters than conventional real-valued networks. Bidirectional communication between MATLAB and PyTorch is established through system-level execution protocols, enabling seamless integration with the SM Toolbox for parametric structural modeling. The surrogate model is trained on strategically sampled datasets, with architecture complexity and dataset size adaptively determined based on parameter counts. Reliability indices are computed using the Weighted Average Simulation Method applied separately to real and imaginary components, with final reliability estimated through weighted averaging. The proposed method is validated through three mathematical benchmark functions and three engineering case studies, including a three-span continuous beam, a roof truss, and a ten-story building with CFST columns. Results demonstrate minimum improvements of 79% in mathematical examples and up to 95% in engineering applications regarding required function evaluations, while maintaining essentially zero estimation error. For the ten-story building, computation time reduced from approximately 3.9 days using conventional simulation to 2.3 hours—a 98% improvement—demonstrating the framework's potential for efficient and accurate reliability assessment of complex structural systems.
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. Rastegar Moghaddam,
Volume 16, Issue 2 (4-2026)
Abstract
Decision-making in the selection of sustainable building components remains one of the most persistent challenges in the construction industry. Projects involve numerous conflicting objectives and highly interdependent variables, yet the rich semantic and relational data embedded in IFC-based BIM models is rarely fully exploited for advanced analytical support. Existing approaches typically suffer from fragmented workflows, inefficient data extraction, and poor integration between modelling, optimisation, and decision-making processes. This study proposes a comprehensive, integrated data-driven decision-support framework that directly addresses these limitations. The framework transforms IFC-based BIM data into a scalable graph database using Neo4j and connects it seamlessly with multi-objective optimisation, Data Envelopment Analysis (DEA), and multi-criteria decision-making (MCDM) within a single coherent pipeline. The framework was implemented and validated on a residential building case study, considering four key sustainability objectives. Results demonstrate that the graph-based representation improves data accessibility and efficient retrieval, while the integrated pipeline effectively reduces the solution space and delivers transparent, high-quality recommendations that balance technical performance with stakeholder preferences. Compared with conventional fragmented methods, the proposed framework offers a more coherent, practical, and potentially scalable solution for complex multi-criteria decision-making problems across the Architecture, Engineering, and Construction (AEC) industry.
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.
M. Talebi , G. Ghodrati Amiri,
Volume 16, Issue 2 (4-2026)
Abstract
Bridge Health Monitoring (BHM) plays a vital role in ensuring the safety, reliability, and long-term performance of bridge infrastructure. This study proposes an ARMA–Wavelet–Artificial Neural Network (AWAN) framework for predicting unmeasured bridge deck acceleration responses from limited sensor measurements. The proposed methodology integrates Auto-Regressive Moving Average (ARMA) modeling for temporal feature extraction, Continuous Wavelet Transform (CWT) for signal denoising, and a feed-forward Artificial Neural Network (ANN) for nonlinear response prediction. The combined framework exploits both spatial and short-term temporal correlations to achieve accurate response reconstruction while maintaining computational efficiency. The proposed framework was validated using three bridge models, including a simply supported beam, a two-span steel grid benchmark, and a scaled single-plane cable-stayed bridge. Prediction performance was evaluated using different statistical metrics under multiple loading scenarios. The results demonstrated excellent agreement between the predicted and measured acceleration responses, with higher prediction accuracy generally achieved at mid-span locations than near the supports, reflecting differences in local structural dynamics. In addition, the framework maintained stable performance under moderate temperature variation, demonstrating its robustness for practical bridge health monitoring applications. The proposed AWAN framework provides an efficient and reliable approach for reconstructing unmeasured structural responses while reducing sensor requirements. Its combination of prediction accuracy, computational efficiency, and robustness makes it a promising tool for data-driven bridge health monitoring and response reconstruction.
S. Maleki, M. Ilchi Ghazaan, A. Ghafouri,
Volume 16, Issue 2 (4-2026)
Abstract
Reduced order models (ROMs) are widely used to approximate the dynamic response of large-scale structural systems while substantially reducing computational cost. Inherent uncertainties necessitate the assessment of ROMs within an uncertainty quantification (UQ) framework. Although the deterministic accuracy of reduction techniques has been extensively investigated, their capability for UQ remains insufficiently understood. This study presents a systematic UQ-based assessment of four condensation techniques: Guyan reduction, dynamic condensation, Improved Reduced System (IRS), and the System Equivalent Reduction Expansion Process (SEREP). A shear frame and a plane truss are used to evaluate the combined influence of the reduction technique, master degree of freedom (DOF) selection, and structural dynamic complexity on ROM predictive capability. Polynomial Chaos Expansion (PCE) is employed for UQ, and variance-based Sobol' indices are adopted for global sensitivity analysis (GSA). SEREP consistently provides the closest approximation to the full order model, whereas IRS also maintains high accuracy over most vibration modes. The accuracy of the Guyan and dynamic condensation methods decreases as higher-order dynamics become increasingly important.
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.
