• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.9 no.1
• /
• pp.33-40
• /
• 1989

In the design of reinforced concrete framed structures, which consist of various design variables, the objective and the constraint functions are formulated in complicated forms. Usually iterative methods have been used to optimize the design variables. In this paper, multilevel formulation is adopted, and design variables are selected in reduced numbers at each level, to reduce the iterative cycle and to accelerate the convergence rate. At level 1, elastic analysis is performed to get the upper and lower bounds of the redistributed design moments due to inelastic behavior of the frame. Then the design moments are taken as design variables and optimized at level 2, and the sizing variables are optimized at level 3. The optimization of redistributed moments is performed using the design sensitivity obtained at the level 2, and force approximation technique is used to reflect the variation of design variables in the lower level to the upper level. The design variables are selected in reduced numbers at each level, and the optimization formulation is simplified effectively. A cost function is taken as the objective function, and the constraints of the stress of the structures are derived from BSI CP 110 following limit state theory. Numerical examples are included to prove the effectiveness of the developed algorithm.

• Steel and Composite Structures
• /
• v.52 no.2
• /
• pp.145-163
• /
• 2024

This paper presents six novel hybrid machine learning (ML) models that combine support vector machines (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with the Harris Hawks Optimization (HHO) algorithm. These models, namely HHO-SVM, HHO-DT, HHO-RF, HHO-GB, HHO-XGB, and HHO-CGB, are designed to predict the ultimate strength of both rectangular and circular reinforced concrete (RC) columns. The prediction models are established using a comprehensive database consisting of 325 experimental data for rectangular columns and 172 experimental data for circular columns. The ML model hyperparameters are optimized through a combination of cross-validation technique and the HHO. The performance of the hybrid ML models is evaluated and compared using various metrics, ultimately identifying the HHO-CGB model as the top-performing model for predicting the ultimate shear strength of both rectangular and circular RC columns. The mean R-value and mean a20-index are relatively high, reaching 0.991 and 0.959, respectively, while the mean absolute error and root mean square error are low (10.302 kN and 27.954 kN, respectively). Another comparison is conducted with four existing formulas to further validate the efficiency of the proposed HHO-CGB model. The Shapely Additive Explanations method is applied to analyze the contribution of each variable to the output within the HHO-CGB model, providing insights into the local and global influence of variables. The analysis reveals that the depth of the column, length of the column, and axial loading exert the most significant influence on the ultimate shear strength of RC columns. A user-friendly graphical interface tool is then developed based on the HHO-CGB to facilitate practical and cost-effective usage.

• Structural Engineering and Mechanics
• /
• v.45 no.1
• /
• pp.111-127
• /
• 2013

In this paper, a new version of gravitational search algorithm based on opposition-based learning (OBGSA) is introduced and applied for optimum design of reinforced concrete retaining walls. The new algorithm employs the opposition-based learning concept to generate initial population and updating agents' position during the optimization process. This algorithm is applied to minimize three objective functions include weight, cost and $CO_2$ emissions of retaining structure subjected to geotechnical and structural requirements. The optimization problem involves five geometric variables and three variables for reinforcement setups. The performance comparison of the new OBGSA and classical GSA algorithms on a suite of five well-known benchmark functions illustrate a faster convergence speed and better search ability of OBGSA for numerical optimization. In addition, the reliability and efficiency of the proposed algorithm for optimization of retaining structures are investigated by considering two design examples of retaining walls. The numerical experiments demonstrate that the new algorithm has high viability, accuracy and stability and significantly outperforms the original algorithm and some other methods in the literature.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1988.10a
• /
• pp.55-60
• /
• 1988

This paper concentrates on the development of simplified and effective algorithm for optimum reinforced concrete(RC) member design. After constructing the data base of predetermined RC sections which are arranged in the order of increasing resistant capacity. Then, the relationship between the section identification numbers and resistant capacities of sections is estabilished by regression and it can be used to obtain the initial solution(section) which satisfies the design constraints imposed. Assuming that there exists the optimum section near the initially selected one, the direct search is conducted to find the discrete optimum solution. The optimization of the entire structure is accomplished through the individual member optimization.

• Computational Structural Engineering
• /
• v.2 no.1
• /
• pp.79-86
• /
• 1989

This paper concentrates on the development of simplified and effective algorithm for optimum reinforced concrete(RC) member design. After constructing the data base of predetermined RC sections which are arranged in the order of increasing resistant capacity, the relationship between the section identification numbers and resistant capacities of sections is estabilished by regression and it can be used to obtain the initial solution(section) which satisfies the design constraints imposed. Assuming that there exists the optimum solution. The optimization of the entire structure is accomplished through the individual mumber optimization.

• Structural Engineering and Mechanics
• /
• v.20 no.2
• /
• pp.161-172
• /
• 2005

This paper presents the section model for analysis of RC circular tower structures based on nonlinear material laws. The governing equations for normal strains due to the bending moment and the normal force are derived in the case when openings are located symmetrically in respect to the bending direction. In this approach the additional reinforcement at openings is also taken into account. The mathematical model is expressed in the form of a set of nonlinear equations which are solved by means of the minimization of the sums of the second powers of the residuals. For minimization the BFGS quasi-Newton and/or Hooke-Jeeves local minimizers suitably modified are applied to take into account the box constraints on variables. The model is verified on the set of data encountered in engineering practice. The numerical examples illustrate the effects of the loading eccentricity and size of the opening on the strains and stresses in concrete and steel in the cross-sections under consideration. Calculated results indicate that the additional reinforcement at the openings increases the resistance capacity of the section by several percent.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2000.10a
• /
• pp.671-676
• /
• 2000

This study was to determine the technical feasibility of using wastepaper fibers, obtained through dry processing of wastepaper, as reinforcement in thin cement produces. Dry-processed waste papers have high levels of noncellulosic impurities, and the recycling process also breads and damages the fibers. To produce wastepaper fiber-cement composites, first the influential variables in the slurry-dewatering method of processing the composites were identified in an experimental study based on factorial design. Among the proportioning and processing variables investigated, fiber mass fraction and level of substitution of virgin fibers with recycled ones were found to have statistically significant effects on mechanical and physical properties of composites. Subsequently, response surface analysis techniques were used to devise an experimental program that helped determine the optimum combinations of the selected influential variables based on mechanical and physical properties, and cost.

• Structural Engineering and Mechanics
• /
• v.45 no.5
• /
• pp.643-654
• /
• 2013

Optimum cost design of columns subjected to axial force and uniaxial bending moment is presented in this paper. In the formulation of the optimum design problem, the height and width of the column, diameter and number of reinforcement bars are treated as design variables. The design constraints are implemented according to ACI 318-08 and studies in the literature. The objective function is taken as the cost of unit length of the column consisting the cost of concrete, steel, and shuttering. The solution of the design problem is obtained using the artificial bee colony algorithm which is one of the recent additions to metaheuristic techniques. The Artificial Bee Colony Algorithm is imitated the foraging behaviors of bee swarms. In application of this algorithm to the constraint problem, Deb's constraint handling method is used. Obtained results showed that the optimum value of numerical example is nearly same with the existing values in the literature.

• Advances in concrete construction
• /
• v.15 no.4
• /
• pp.269-286
• /
• 2023

In the present study, we aim to utilize the numerical solution frequency results of functionally graded beam under thermal and dynamic loadings to train and test an artificial neural network. In this regard, shear deformable functionally-graded beam structure is considered for obtaining the natural frequency in different conditions of boundary and material grading indices. In this regard, both analytical and numerical solutions based on Navier's approach and differential quadrature method are presented to obtain effects of different parameters on the natural frequency of the structure. Further, the numerical results are utilized to train an artificial neural network (ANN) using AdaGrad optimization algorithm. Finally, the results of the ANN and other solution procedure are presented and comprehensive parametric study is presented to observe effects of geometrical, material and boundary conditions of the free oscillation frequency of the functionally graded beam structure.

• Journal of the Korean Recycled Construction Resources Institute
• /
• v.8 no.3
• /
• pp.269-275
• /
• 2020

In this study, flexural tests of precast concrete panels according to the thickness of cross-sectional and the with or not of reinforcement were carried out in order to develop and assess of a lightweight precast concrete panel using ultra high performance concrete. For the test, four panels were fabricated, and consisted of one normal concrete panel and three ultra high performance concrete panels. As a test result, it was found that the plain precast panel using ultra high performance concrete had a lower flexural performance than the reinforced normal concrete panel, regardless of the cross-sectional size. The flexural performance of the hollow-sectional precast panel applying ultra high performance concrete, is improved by 150% compared to that of the reinforced normal concrete panel. That is, through additional performance verification and optimization of the cross-sectional design of the panel, the ultra high performance concrete precast panel can be made lighter. Also, the practical use of lightweight precast panels with ultra high performance concrete can be available through evaluation on shear, joint connection and anchoring, etc.