Showing 51 results for Concrete
S. Gholizadeh, C. Gheyratmand , N. Razavi,
Volume 13, Issue 3 (7-2023)
Abstract
The main objective of this study is to optimize reinforced concrete (RC) frames in the framework of performance-based design using metaheuristics. Three improved and efficient metaheuristics are employed in this work, namely, improved multi-verse (IMV), improved black hole (IBH) and modified newton metaheuristic algorithm (MNMA). These metaheuristic algorithms are applied for performance-based design optimization of 6- and 12-story planar RC frames. The seismic response of the structures is evaluated using pushover analysis during the optimization process. The obtained results show that the IBH outperforms the other algorithms.
M. Shahrouzi, S.-Sh. Emamzadeh, Y. Naserifar,
Volume 13, Issue 4 (10-2023)
Abstract
Shape optimization of a double-curved dam is formulated using control points for interpolation functions. Every design vector is decoded into the integrated water-dam-foundation rock model. An enhanced algorithm is proposed by hybridizing particle swarm algorithm with ant colony optimization and simulated annealing. The best experiences of the search agents are indirectly shared via pheromone trail deposited on a bi-partite characteristic graph. Such a stochastic search is further tuned by Boltzmann functions in simulated annealing. The proposed method earned the first rank in comparison with six well-known meta‑heuristic algorithms in solving benchmark test functions. It captured the optimal shape design of Morrow Point dam, as a widely addressed case-study, by 21% reduced concrete volume with respect to the common USBR design practice and 16% better than the particle swarm optimizer. Such an optimal design was also superior to the others in stress redistribution for better performance of the dam system.
G. Sedghi, S. Gholizadeh, S. Tariverdilo ,
Volume 13, Issue 4 (10-2023)
Abstract
In this paper an enhanced ant colony optimization algorithm with a direct constraints handling strategy is proposed for the optimization of reinforced concrete frames. The construction cost of reinforced concrete frames is considered as the objective function, which should be minimized subject to geometrical and behavioral strength constraints. For this purpose, a new probabilistic function is added to the ant colony optimization algorithm to directly satisfy the geometrical constraints. Furthermore, the position of an ant in each iteration is updated if a better solution is found in terms of objective value and behavioral strength constraints satisfaction. Five benchmark design examples of planar reinforced concrete frames are presented to illustrate the efficiency of the proposed algorithm.
P. Hosseini, A. Kaveh, A. Naghian,
Volume 13, Issue 4 (10-2023)
Abstract
In this study, experimental and computational approaches are used in order to develop and optimize self-compacting concrete mixes (Artificial neural network, EVPS metaheuristic algorithm, Taguchi method). Initially, ten basic mix designs were tested, and an artificial neural network was trained to predict the properties of these mixes. The network was then used to generate ten optimized mixes using the EVPS algorithm. Three mixes with the highest compressive strength were selected, and additional tests were conducted using the Taguchi approach. Inputting these results, along with the initial mix designs, into a second trained neural network, 10 new mix designs were tested using the network. Two of these mixes did not meet the requirements for self-compacting concrete, specifically in the U-box test. However, the predicted compressive strength results showed excellent agreement with low error percentages compared to the laboratory results, which indicates the effectiveness of the artificial neural network in predicting concrete properties, thus indicating that self-compacting concrete properties can be predicted with reasonable accuracy. The paper emphasizes the reliability and cost-effectiveness of artificial neural networks in predicting concrete properties. The study highlights the importance of providing diverse and abundant training data to improve the accuracy of predictions. The results demonstrate that neural networks can serve as valuable tools for predicting concrete characteristics, saving time and resources in the process. Overall, the research provides insights into the development of self-compacting concrete mixes and highlights the effectiveness of computational approaches in optimizing concrete performance.
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.
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.
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. 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.
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.
