genetic algorithm for predicting shear strength of steel

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genetic algorithm for predicting shear strength of steel

genetic algorithm for predicting shear strength of steel

A Hybrid Neural Network - Genetic Algorithm for Prediction of ... Genetic Algorithm (GA) is a search algorithm based on the mechanics of natural selection and genetics and they combine survival of the fittest among string structures to form a search algorithm[8]. GA is particularly suitable for multi-parameter optimization problems with an objective function subject to numerous hard and soft constraints.

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The Use of Genetic Algorithms to Calibrate JohnsonCook genetic algorithm for predicting shear strength of steel

In this paper, a genetic-algorithm-based optimization strategy is proposed to calibrate the JC strength and failure model parameters of AISI/SAE 1018 steel. Experimental data were obtained from tensile tests performed for different specimen geometries at varying strain rates and temperatures. Related searches genetic algorithm for predicting shear strength of steel

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Proposed Shear Design Equations for FRP-Reinforced Concrete genetic algorithm for predicting shear strength of steel This paper presents simple yet improved equations to calculate the shear capacity of FRP-reinforced concrete beams based on the genetic algorithms approach. The performance of the proposed equations is compared to that of four commonly used shear design methods for FRP-reinforced concrete beams, namely the ACI 440, CSA S806, JSCE, and ISIS Canada.

Optimization of Flexural capacity of Reinforced fibrous genetic algorithm for predicting shear strength of steel

Beams Using Genetic Algorithm SUJI.D,NATESAN S.C, MURUGESAN.R Biography: Suji .D is a Research Scholar at Sathyabama Institute Of Science and Technology, Deemed University, Chennai, India. She is a Life member of Indian society for Technical Education and Member of Institution of Engineers, India. She is having 18 years of teaching experience. Numerical study on the structural performance of corrugated genetic algorithm for predicting shear strength of steel Corrugated steel plate shear wall (CSPSW) as an innovative lateral load resisting system provides various advantages in comparison with the flat steel plate shear wall, including remarkable in-plane and out-of-plane stiffnesses and stability, greater elastic shear buckling stress, increasing the amount of cumulative dissipated energy and maintaining efficiency even in large story drifts. Modeling and analysis of the shear capacity of adhesive genetic algorithm for predicting shear strength of steel The shear resistance of anchor bolt is calculated on the basis of the tensile strength of the concrete acting over the projected area of the semicone surface. According to ACI 349-97 , the design shear strength is given by the formula below (Eq. in U.S. customary units). Ueda et al. presented the same relation in SI units (Eq. ).

Hybrid ANN-based techniques in predicting cohesion of sandy genetic algorithm for predicting shear strength of steel

Soil shear strength parameters play a remarkable role in designing geotechnical structures such as retaining wall and dam. This study puts an effort to propose two accurate and practical predictive models of soil shear strength parameters via hybrid artificial neural network (ANN)-based models namely genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. Herding Behaviors of grasshopper and Harris hawk for genetic algorithm for predicting shear strength of steel Also, evaluating the prediction results revealed the effectiveness of the applied algorithms as the ANN achieved a more reliable understanding of the relationships between the SCC and soil key factors (the MAE was decreased by 15.09% and 11.32%), and at the same time, higher capability of prediction for unseen soil conditions (the MAE was genetic algorithm for predicting shear strength of steel DESIGN OPTIMIZATION OF REINFORCED CONCRETE SLABS USING genetic algorithm for predicting shear strength of steel Characteristic cube strength of the concrete f ck = 20.68 MPa Cost of concrete, C c 3= 610 (Rs. /m ) Characteristic strength for the steel f y = 275.8 MPa Cost of steel bars, C s =95.2809 (Rs. /kg). Solution The above problem is solved using Genetic algorithm coding and the results obtained are as follows: Cost = 1650.34 Rs. /m

Corrosion current density prediction in reinforced concrete genetic algorithm for predicting shear strength of steel

The most popular seems to be imperialist competitive algorithms (ICA) and genetic algorithms (GA). The total number of papers related to corrosion of reinforced concrete published in Science Direct database increased from 42 in 2010 to 78 in 2013 (Fig. 3). The number of papers related to corrosion by using neural networks is on the same level genetic algorithm for predicting shear strength of steel An evolutionary hybrid optimization of MARS model in genetic algorithm for predicting shear strength of steel KeywordsBeamsReinforced concreteFibre-reinforced polymerShear strengthGenetic algorithms. genetic algorithm for predicting shear strength of steel strength of steel (fy), and the load eccentricity (e). genetic algorithm for predicting shear strength of steel GP models predict the bond genetic algorithm for predicting shear strength of steel An Intelligent Model for the Prediction of Bond Strength of genetic algorithm for predicting shear strength of steel Accurate prediction of bond behavior of fiber reinforcement polymer (FRP) concrete has a pivotal role in the construction industry. This paper presents a soft computing method called multi-gene genetic programming (MGGP) to develop an intelligent prediction model for the bond strength of FRP bars in concrete. The main advantage of the MGGP method over other similar methods is that it can genetic algorithm for predicting shear strength of steel

A multilayer perceptron for predicting the ultimate shear genetic algorithm for predicting shear strength of steel

algorithm to predict the shear capacity of reinforced concrete deep and slender beams. Dopico et al. (2008) applied the neural network technique to predict the shear strength of It should be poinhigh and normal strength reinforced concrete beams with or without shear reinforcement. Kumar and A novel hybrid extreme learning machinegrey wolf optimizer genetic algorithm for predicting shear strength of steel Shariati M et al (2020) Prediction of concrete strength in presence of furnace slag and fly ash using Hybrid ANN-GA (Artificial Neural Network-Genetic Algorithm). Smart Struct Syst 25(2):183. Google Scholar A Hybrid Neural Network - Genetic Algorithm for Prediction of genetic algorithm for predicting shear strength of steel Genetic Algorithm (GA) is a search algorithm based on the mechanics of natural selection and genetics and they combine survival of the fittest among string structures to form a search algorithm[8]. GA is particularly suitable for multi-parameter optimization problems with an objective function subject to numerous hard and soft constraints.

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Gandomi A.H., Alavi A.H., Discussion on Predicting the Shear Strength of Reinforced Concrete Beams Using Artificial Neural Networks, Engineering Structures, 31(11): 2801, 2009. Chapters in Book Using genetic algorithms method for the paramount design of genetic algorithm for predicting shear strength of steel This study puts an effort to propose two accurate and practical predictive models of soil shear strength parameters via hybrid artificial neural network (ANN)-based models namely genetic algorithm genetic algorithm for predicting shear strength of steel Using genetic algorithms method for the paramount design of genetic algorithm for predicting shear strength of steel Genetic Algorithms (GAs) have found the best design for reinforced concrete frames. The design of the optimum beam sections by GAs has been unified. The process of the optimum-design sections has satisfied axial, flexural, shear and torsion necessities based on the designing code.

Shear strength of SFRCB without stirrups simulation genetic algorithm for predicting shear strength of steel

Recent developments on shear strength (Vf) of steel fiber-reinforced concrete beam (SFRCB) simulation have been shifted to the implementation of the computer aid advancements. The current study is attempted to explore new hybrid artificial intelligence (AI) model called integrative support vector regression with firefly optimization algorithm (SVR-FFA) for shear strength prediction of SFRCB genetic algorithm for predicting shear strength of steel Shear strength prediction of reinforced concrete beams by genetic algorithm for predicting shear strength of steel The shear strength of reinforced concrete (RC) beams is critical in the design of structural members. Developing an effective mathematical method for accurately estimating shear strength of RC beams is beneficial for civil engineers. This work presents a hybrid artificial intelligent (AI) model for effectively predicting the shear strength of various types of RC beam. The hybrid AI model was genetic algorithm for predicting shear strength of steel Prediction of shear strength of FRP-reinforced concrete beams genetic algorithm for predicting shear strength of steel The shear design method provided by ACI 440-03 assumes that the shear strength of FRP-reinforced concrete beams decreases as f c increases, whereas all other methods, including the GEP model assume that the shear strength of FRP-reinforced concrete beams increases with an increase of concrete compressive strength .

Prediction of Shear Strength and Ductility of Cyclically genetic algorithm for predicting shear strength of steel

The prediction of shear strength and ductility for these types of structural members has historically been performed using empirically or semi-empirically derived formulae based on experimental results. Predicting Shear Capacity of FRP-Reinforced Concrete Beams genetic algorithm for predicting shear strength of steel The shear strength prediction of fiber-reinforced polymer- (FRP-) reinforced concrete beams is one of the most complicated issues in structural engineering applications. Developing accurate and reliable prediction models is necessary and cost saving. This paper proposes three new prediction models, utilizing artificial neural networks (ANNs) and gene expression programming (GEP), as a recently genetic algorithm for predicting shear strength of steel Optimization of Fiber-Reinforced Polymer Patches for genetic algorithm for predicting shear strength of steel Brighenti developed a tool in which a genetic algorithm (GA) was embedded in the FE code to obtain the patch topology that minimized the SIF of cracked steel plates. Errouane et al. ( 2014 ) combined the FE method, response surface methodology (RSM), and gradient-based optimization to minimize the composite patch volume for cracked aluminum sheets.

Modified imperialist competitive algorithm-based neural genetic algorithm for predicting shear strength of steel

Fiber reinforced polymers (FRPs), unlike steel, are corrosion-resistant and therefore are of interest; however, their use is hindered because their brittle shear is formulated in most specifications using limited data available at the time. We aimed to predict the shear strength of concrete beams reinforced with FRP bars and without stirrups by compiling a relatively large database of 198 genetic algorithm for predicting shear strength of steel Genetic Programming Based Formulation to Predict Compressive genetic algorithm for predicting shear strength of steel "Linear genetic programming for shear strength prediction of reinforced concrete beams without stirrups", Applied Soft Computing, 19, 112-120. Ganguly, S., Datta, S. and Chakraborti, N. (2009). "Genetic algorithm-based search on the role of variables in the work hardening process of multiphase steels", Computational Materials Science , 45(1 genetic algorithm for predicting shear strength of steel Extracting Knowledge of Concrete Shear Strength From genetic algorithm for predicting shear strength of steel the ACI code in this paper) (ACI 318, 2005) use a model for predicting the shear strength of reinforced concrete beams that is based approximately on a physical understanding of the mechanisms of shear resistance in concrete beams.

Development of prediction models for shear strength of SFRCB genetic algorithm for predicting shear strength of steel

prediction models for the shear capacity of SFRCB without stirrups. The derived models relate the shear strength to a couple of inuencing parameters. The models were estab-lished based on several published shear tests on SFRCB. 2 Multi-expression programming GP is a branch of evolutionary algorithms (EAs). It uses the Computational Hybrid Machine Learning Based Prediction of genetic algorithm for predicting shear strength of steel Understanding shear behavior is crucial for the design of reinforced concrete beams and sustainability in construction and civil engineering. Although numerous studies have been proposed, predicting such behavior still needs further improvement. This study proposes a soft-computing tool to predict the ultimate shear capacities (USCs) of concrete beams reinforced with steel fiber, one of the genetic algorithm for predicting shear strength of steel Artificial Intelligence techniques for prediction of the genetic algorithm for predicting shear strength of steel Perera et al. [12] modified the existing ACI code shear equation [13] by Genetic Algorithm, using the strut and tie model as a basis of shear prediction. Machial et al. [14] performed a genetic algorithm for predicting shear strength of steel

(PDF) Development of prediction models for shear strength of genetic algorithm for predicting shear strength of steel

To evaluate the effectiveness of steel fibers on shear strength and shear deformation, tests were conducted on T-beams with 2 to 4 percent main reinforcing steel, and on rectangular beams.

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