• Computers and Concrete
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• v.24 no.3
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• pp.193-206
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• 2019

Although concrete is the most widely used construction material, its deficiency in shrinkage and low tensile resistance is undeniable. However, the aforementioned defects can be partially modified by addition of fibers. On the other hand, possibility of adding waste materials in concrete has provided a new ground for use of recycled concrete aggregates in the construction industry. In this study, a constant combination of recyclable coarse and fine concrete aggregates was used to replace the corresponding aggregates at 50% substitution percentage. Moreover, in order to investigate the effects of fibers on mechanical and durability properties of recycled aggregate concrete, the amounts of 0.5%, 1%, and 1.5% steel fibers (ST) and 0.05%, 0.1% and 0.15% polypropylene (PP) fibers by volumes were used individually and in hybrid forms. Compressive strength, tensile strength, flexural strength, ultrasonic pulse velocity (UPV), water absorption, toughness, elastic modulus and shrinkage of samples were investigated. The results of mechanical properties showed that PP fibers reduced the compressive strength while positive impact of steel fibers was evident both in single and hybrid forms. Tensile and flexural strength of samples were improved and the energy absorption of samples containing fibers increased substantially before and after crack presence. Growth in toughness especially in hybrid fiber-reinforced specimens retarded the propagation of cracks. Modulus of elasticity was decreased by the addition of PP fibers while the contrary trend was observed with the addition of steel fibers. PP fibers decreased the ultrasonic pulse velocity slightly and had undesirable effect on water absorption. However, steel fiber caused negligible decline in UPV and a small impact on water absorption. Steel fibers reduce the drying shrinkage by up to 35% when was applied solely. Using fibers also resulted in increasing the ductility of samples in failure. In addition, mechanical properties changes were also evaluated by statistical analysis of MATLAB software and smoothing spline interpolation on compressive, flexural, and indirect tensile strength. Using shell interpolation, the optimization process in areas without laboratory results led to determining optimal theoretical points in a two-parameter system including steel fibers and polypropylene.

• Advances in nano research
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• v.12 no.5
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• pp.515-527
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• 2022

The application of fibers in concrete obviously enhances the properties of concrete, also the application of natural fibers in concrete is raising due to the availability, low cost and environmentally friendly. Besides, predicting the mechanical properties of concrete in general and shear strength in particular is highly significant in concrete mixture with fiber nanocomposite reinforced concrete (FRC) in construction projects. Despite numerous studies in shear strength, determining this strength still needs more investigations. In this research, Adaptive Neuro-Fuzzy Inference System (ANFIS) have been employed to determine the strength of reinforced concrete with fiber. 180 empirical data were gathered from reliable literature to develop the methods. Models were developed, validated and their statistical results were compared through the root mean squared error (RMSE), determination coefficient (R²), mean absolute error (MAE) and Pearson correlation coefficient (r). Comparing the RMSE of PSO (0.8859) and ANFIS (0.6047) have emphasized the significant role of structural parameters on the shear strength of concrete, also effective depth, web width, and a clear depth rate are essential parameters in modeling the shear capacity of FRC. Considering the accuracy of our models in determining the shear strength of FRC, the outcomes have shown that the R² values of PSO (0.7487) was better than ANFIS (2.4048). Thus, in this research, PSO has demonstrated better performance than ANFIS in predicting the shear strength of FRC in case of accuracy and the least error ratio. Thus, PSO could be applied as a proper tool to maximum accuracy predict the shear strength of FRC.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.2
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• pp.165-179
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• 2007

For the purpose of optimum design of reinforced concrete structures, pre-determined section database of column and beam are constructed and arranged in order of the resisting capacity. Then, regression equations representing the relation between section number and section resisting capacity are derived. In advance, effective optimization algorithms which search optimized solution quickly using direct search method from these database are proposed. In practice, from the fact that engineers conduct member design close to capacity optimization rather than cost optimization, both capacity and cost optimization using proposed algorithms are performed, and the review for the obtained results are followed. Moreover, the investigation for the applicability and effectiveness of the Introduced design procedure is conducted through correlation study for example structures. Because of no restriction in constructing objective functions with very simple optimization processes and fast convergence, the introduced method can effectively be used in the preliminary design stage. Especially, selected solutions from database are directly applicable in practice because these sections already satisfy all the requirements in design codes and practical restrictions.

• Structural Engineering and Mechanics
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• v.33 no.6
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• pp.765-779
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• 2009

Quantitative condition assessment of structures has been traditionally using proof load test leading to an indication of the load-carrying capacity. Alternative approaches using ultrasonic, dynamics etc. are based on the unloaded state of the structure and anomalies may not be fully mobilized in the load resisting path and thus their effects are not fully included in the measured responses. This paper studies the effect of the load carried by a reinforced concrete beam on the assessment result of the crack damage. This assessment can only be performed with an approach based on static measurement. The crack damage is modelled as a crack zone over an area of high tensile stress of the member, and it is represented by a damage function for the simulation study. An existing nonlinear optimization algorithm is adopted. The identified damage extent from a selected high level load and a low load level are compared, and it is concluded that accurate assessment can only be obtained at a load level close to the one that creates the damage.

• Computers and Concrete
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• v.22 no.4
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• pp.355-363
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• 2018

In the following paper, a socio-political heuristic search approach, named the imperialist competitive algorithm (ICA) has been used to improve the efficiency of the multi-layer perceptron artificial neural network (ANN) for predicting the compressive strength of concrete. 173 concrete samples have been investigated. For this purpose the values of slump flow, the weight of aggregate and cement, the maximum size of aggregate and the water-cement ratio have been used as the inputs. The compressive strength of concrete has been used as the output in the hybrid ICA-ANN model. Results have been compared with the multiple-linear regression model (MLR), the genetic algorithm (GA) and particle swarm optimization (PSO). The results indicate the superiority and high accuracy of the hybrid ICA-ANN model in predicting the compressive strength of concrete when compared to the other methods.

• Steel and Composite Structures
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• v.6 no.5
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• pp.435-457
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• 2006

The paper presents the cost optimization of composite floor trusses composed from a reinforced concrete slab of constant depth and steel trusses consisting of hot rolled channel sections. The optimization was performed by the nonlinear programming approach, NLP. Accordingly, a NLP optimization model for composite floor trusses was developed. An accurate objective function of the manufacturing material, power and labour costs was proposed to be defined for the optimization. Alongside the costs, the objective function also considers the fabrication times, and the electrical power and material consumption. Composite trusses were optimized according to Eurocode 4 for the conditions of both the ultimate and the serviceability limit states. A numerical example of the optimization of the composite truss system presented at the end of the paper demonstrates the applicability of the proposed approach.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.9 no.3
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• pp.57-64
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• 1989

This study presents a reliability-based optimum design of reinforced concrete frames, in which the AFOSM and SOSM methods are applied for the evaluation of the failure probabilities, and the sequential linear programming method is used as a practical approach to the system optimization. One-story two-bay reinforced concrete frame is chosen for the numerical illustration of the proposed reliability-based optimum design. As a result, it is found that the proposed procedure for the reliability-based optimization of RC frames could provide the accurate estimation of the optimal level of safety, and appears applicable to real structures with reasonable complexity. It is shown in the paper that the probability distributions of the basic random variables and the uncertainties of the applied loadings and material strengths may have great effect on the optimum design, but the AFOSM and SOSM methods do not show significant discrepancy in the optimum design results, but the former appears more realistic and time saving than the latter for this specific study.

• Steel and Composite Structures
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• v.34 no.4
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• pp.467-485
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• 2020

Genetic Algorithm (GA) is a meta-heuristic algorithm which is capable of providing robust solutions for optimal design of structural components, particularly those one needs considering many design requirements. Hence, it has been successfully used by engineers in the typology optimization of structural members. As a novel approach, this study employs GA in order for conducting a case study with high constraints on the optimum mechanical properties of reinforced concrete (RC) beams under different load combinations. Accordingly, unified optimum sections through a computer program are adopted to solve the continuous beams problem. Genetic Algorithms proved in finding the optimum resolution smoothly and flawlessly particularly in case of handling many complicated constraints like a continuous beam subjected to different loads as moments shear - torsion regarding the curbs of design codes.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1993.10a
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• pp.106-112
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• 1993

This paper presents the optimum design of reinforced concrete conical shell which is fixed at the base and free at the top. The calculation of stresses is done using the SMAP(Segmented Matrix Analysis Package)program which uses the simple finite element method of analysis. The objective function contains the ratios of the unit cost of reinforcements and formwork to that of concrete. To Simplify the optimization procedure, the final optimum design of conical shell is obtained by combining the result of each element. The results are presented and discussed.

• Advances in Computational Design
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• v.9 no.1
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• pp.1-23
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• 2024

Present paper deals with the cost effective design of reinforced concrete building frame employing unified particle swarm optimization (UPSO). A building frame with G+8 stories have been adopted to demonstrate the effectiveness of the present algorithm. Effect of seismic loads and wind load have been considered as per Indian Standard (IS) 1893 (Part-I) and IS 875 (Part-III) respectively. Analysis of the frame has been carried out in STAAD Pro software.The design loads for all the beams and columns obtained from STAAD Pro have been given as input of the optimization algorithm. Next, cost optimization of all beams and columns have been carried out in MATLAB environment using UPSO, considering the safety and serviceability criteria mentioned in IS 456. Cost of formwork, concrete and reinforcement have been considered to calculate the total cost. Reinforcement of beams and columns has been calculated with consideration for curtailment and feasibility of laying the reinforcement bars during actual construction. The numerical analysis ensures the accuracy of the developed algorithm in providing the cost optimized design of RC building frame considering safety, serviceability and constructional feasibilities. Further, Monte Carlo simulations performed on the numerical results, proved the consistency and robustness of the developed algorithm. Thus, the present algorithm is capable of giving a cost effective design of RC building frame, which can be adopted directly in construction site without making any changes.