Showing 505 results for Type of Study: Research
B. Ahmadi-Nedushan, A. M. Almaleeh,
Volume 14, Issue 4 (10-2024)
Abstract
This study uses an elitist Genetic Algorithm (GA) to optimize material costs in one-way reinforced concrete slabs, adhering to ACI 318-19. A sensitivity analysis demonstrated the critical role of elitism in GA performance. Without elitism, the GA consistently failed to reach the target objective, with success rates often nearing zero across various crossover fractions. Incorporating elitism dramatically increased success rates, highlighting the importance of preserving high-performing individuals. With an optimal configuration of 0.3 crossover fraction and 0.45 elite percentage, a 92% success rate was achieved, finding a cost of 24.91 in 46 of 50 runs for a simply supported slab. This optimized design, compared to designs based on ACI 318-99 and ACI 318-08, yielded material cost savings of between 5.8% to 8.6% for simply supported, one-end continuous, both-ends continuous, and cantilevered slabs. The influence of slab dimensions on cost was evaluated across 64 scenarios, varying slab lengths from 5 to 20 feet for each support condition. Resulting cost versus slab length diagrams illustrate the economic benefits of GA optimization.
P. Salmanpour, Dr. A. Deylami, Professor M. Z. Kabir,
Volume 14, Issue 4 (10-2024)
Abstract
The multi-material size optimization of transmission tower trusses is carried out in the present study. Three real-size examples are designed, and statically analyzed, and the Black Hole Mechanics Optimization (BHMO) algorithm, a recently developed metaheuristic optimizer methodology, is employed. The BHMO algorithm's innovative search strategy, which draws inspiration from black hole quantum physics, along with a robust mathematical kernel based on the covariance matrix between variables and their associated costs, efficiently converges to global optimum solutions. Besides, three alloys of steel are taken into account in these examples for discrete size variables, each of which is defined in the problem by a weighted coefficient in terms of the elemental weight. The results also indicate that using multiple materials or alloys in addition to diverse cross-sectional sizes leads to the lowest possible cost and the most efficient solution.
A. Hassan Radhi Alhilali, S. Gholizadeh, S. Tariverdilo,
Volume 14, Issue 4 (10-2024)
Abstract
This paper employs neural network models to assess the seismic confidence levels at various performance levels, as well as the seismic collapse capacity of steel moment-resisting frame structures. Two types of shallow neural network models including back-propagation (BP) and radial basis (RB) models are utilized to evaluate the seismic responses. Both neural network models consist of a single hidden layer with a different number of neurons. The prediction accuracy of the trained neural network models is compared using two illustrative examples of 6- and 12-story steel moment-resisting frames. The obtained numerical results indicate that the BP model outperforms the RB model in predicting seismic responses.
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.
Dr. V. Goodarzimehr, Dr. N. Fanaie, Dr. S. Talatahari,
Volume 15, Issue 1 (1-2025)
Abstract
In this study, the Improved Material Generation Algorithm (IMGA) is proposed to optimize the shape and size of structures. The original Material Generation Algorithm (MGA) introduced an optimization model inspired by the high-level and fundamental characteristics of material chemistry, particularly the configuration of compounds and chemical reactions for generating new materials. MGA uses a Gaussian normal distribution to produce new combinations. To enhance MGA for adapting truss structures, a new technique called Random Chaotic (RC) is proposed. RC increases the speed of convergence and helps escape local optima. To validate the proposed method, several truss structures, including a 37-bar truss bridge, a 52-bar dome, a 72-bar truss, a 120-bar dome, and a 200-bar planar structure, are optimized under natural frequency constraints. Optimizing the shape and size of structures under natural frequency constraints is a significant challenge due to its complexity. Choosing the frequency as a constraint prevents resonance in the structure, which can lead to large deformations and structural failure. Reducing the vibration amplitude of the structure decreases tension and deflection. Consequently, the weight of the structure can be minimized while keeping the frequencies within the permissible range. To demonstrate the superiority of IMGA, its results are compared with those of other state-of-the-art metaheuristic methods. The results show that IMGA significantly improves both exploitation and exploration.
R. Kamgar, Z. Falaki Nafchi,
Volume 15, Issue 1 (1-2025)
Abstract
Earthquakes are random phenomena and there has been no report of similar earthquakes occurring worldwide. Therefore, traditional methods of designing buildings based on past earthquakes with inappropriate discontinuity joints are sometimes ineffective for vital structures. This may lead to collision and destruction of adjacent structures during a severe earthquake. As in the Iranian Standard No. 2800-4, this distance should be at least five-thousandths of the building height from the base level to the adjacent ground boundary for buildings up to eight stories to prevent or reduce this damage. Also, for important or/with more than eight-story buildings, this value is determined using the maximum nonlinear lateral displacement of the structures by considering the effects of the P-delta. Also, if the properties of the adjacent building are not known, this distance should be considered at least equal to 70% of the maximum nonlinear lateral displacement of the structures. The main objective of this study is to investigate the adequacy of the discontinuity joint introduced in the Iranian Standard No. 2800-4 based on the critical excitation method. This method calculates critical earthquakes for three buildings (e.g., three-, seven- and eleven-story moment frames) by considering some constraints on the energy, peak ground acceleration, Fourier amplitude, and strong ground motion duration. The results indicate that the minimum gap between two adjacent buildings derived from the existing codes is lower than those calculated using the critical excitation method. Therefore, oscillation might occur if a structure is designed according to the seismic codes and subjected to a critical earthquake.
M. Shahrouzi, M. Rashidi-Moghaddam,
Volume 15, Issue 1 (1-2025)
Abstract
Clustering is a well-known solution to deal with complex database features as an unsupervised machine learning technique. One of its practical applications is the selection of non-similar earthquakes for consequent analysis of structural models. In the present work, appropriate clustering of seismic data is searched via optimization. Silhouette value is penalized and used to define the performance objective. A stochastic search algorithm is combined with a greedy search to solve the problem for distinct sets of near–field and far-field ground motion records. The concept of coherency is borrowed from optics to propose a coherency metric for earthquake signals before and after being filtered by structural models. It is then evaluated for various cases of structural response-to-record and response-to-response comparisons. According to the results the proposed coherency detection procedure performs well; confirmed by distinguished structural response spectra between different clusters.
M. Rezaiee-Pajand, H. Estiri,
Volume 15, Issue 1 (1-2025)
Abstract
One of the goals of the nonlinear structural analysis is to reduce the required time for obtaining the numerical solution. More important than this issue, the nonlinear scheme could converge to the answers for all types of problems. A perfect nonlinear solver must have both of these specifications. This article aims to reduce the duration of structural analysis as well as to boost convergent requirements. To reach these two objectives, the authors simultaneously minimize the kinetic and residual structural energies. The ability of the new formulation is shown by solving several structures, with nonlinear geometrical behavior. Based on the compressive studies, numerical solutions show the high efficiency of the new method.
M.h. Talebpour , S.m.a Razavizade Mashizi, Y. Goudarzi ,
Volume 15, Issue 1 (1-2025)
Abstract
The optimization process of space structures considering the nonlinear material behavior requires significant computational efforts due to the large number of design variables and the complexities of nonlinear structural analysis. Accordingly, the Force Analogy Method (FAM) serves as an efficient tool to reduce computational workload and enhance optimization speed. In this study, the weight optimization of space structures in the inelastic region under seismic loading is carried out using the Shuffled Shepherd Optimization Algorithm (SSOA), with the nonlinear structural analysis based on the FAM. To do this, the FAM formulation for axially loaded members of space structures under seismic forces is presented. Subsequently, weight optimization is performed on two double-layer space structures: a flat double-layer structure with 200 members and a barrel vault structure with 729 members under the Kobe earthquake record. Based on the results, the optimized design using the inelastic behavior showed that the FAM provided accurate results when compared to the precise nonlinear structural analysis. The optimized design based on the FAM is considered acceptable, and the computational time for the optimization process has been significantly reduced.
A. Kaveh, Sh. Rezazadeh Ardebili,
Volume 15, Issue 1 (1-2025)
Abstract
Identification of damping properties for a mixed structure and its interaction with underlying soil is a challenge for structural designers. Current codes and available commercial software packages do not provide analytical solutions for such structural systems. Due to irregular damping ratios, dynamic response of each part of a mixed structure differs significantly. In addition, when the structure is subjected to seismic loads, the soil-structure interaction effects cannot be neglected. To manage these issues, this paper proposes an equivalent damping ratio for mixed structures by means of a semi-empirical error minimization method which considers soil-structure interaction. The results of numerical simulations indicate that the use of the equivalent damping ratios makes the results of dynamics analyses closer to the ones obtained by the actual damping ratios. Consequently, proposed method provides a much better approximation than the case in which the conservative overall ratio of 2% or 5% is used.
M. Paknahd, P. Hosseini, A. Kaveh, S.j.s. Hakim,
Volume 15, Issue 1 (1-2025)
Abstract
Structural optimization plays a crucial role in engineering design, aiming to minimize weight and cost while satisfying performance constraints. This research presents a novel Self-Adaptive Enhanced Vibrating Particle System (SA-EVPS) algorithm that automatically adjusts algorithm parameters to improve optimization performance. The algorithm is applied to two challenging examples from the International Student Competition in Structural Optimization (ISCSO) benchmark suite: the 314-member truss structure (ISCSO_2018) and the 345-member truss structure (ISCSO_2021). Results demonstrate that SA-EVPS achieves significantly better solutions compared to previous studies using the Exponential Big Bang-Big Crunch (EBB-BC) algorithm. For ISCSO_2018, SA-EVPS achieved a minimum weight of 16543.57 kg compared to 17934.3 kg for the best EBB-BC variant—a 7.75% improvement. Similarly, for ISCSO_2021, SA-EVPS achieved 4292.71 kg versus 4399.0 kg for the best EBB-BC variant—a 2.42% improvement. The proposed algorithm also demonstrates superior convergence behavior and solution consistency, with coefficients of variation of 3.13% and 1.21% for the two benchmark problems, compared to 12.5% and 2.4% for the best EBB-BC variant. These results highlight the effectiveness of the SA-EVPS algorithm for solving complex structural optimization problems and demonstrate its potential for engineering applications.
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.
