Assessment of RC exterior beam-column Joints based on artificial neural networks and other methods

Kotsovou, Gregoria; Cotsovos, Demetrios M.; Lagaros, Nikos D.

doi:10.1016/j.engstruct.2017.04.048

Cited by 34 publications

(9 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kotsovou et al [19] suggested a method that is dissimilar from any method suggested to date, does unneeded calibration by using experimental behavior of the joint data, and is found to be adequate for predicting the failure mode of exterior beam-column joint sub-assemblages for more than 90% of the 153 cases examined, as well as safe load carrying capacity predictions of joint. In comparison to this proposed method, the current code methods fail to determine joint strength and fail to estimate the beam-column joint failure.…”

Section: Previous Analytical Studiesmentioning

confidence: 99%

“…Study behavior of steelfiber strengthen under of axial and cyclic load Tensile post-cracking was increased uniaxial compression; no change was found. The tensile cracking determines the material behavior of concrete [19] two parameters groups are tested, group1 contains fifteen parameters, and group 2 of twelve produced a method proper for the joint's structural assessment in the form of an analytical algorithm obtained by used the artificial neural networks…”

Section: Ref Used Parameters Objectives Of Studymentioning

confidence: 99%

See 1 more Smart Citation

A Review-Behavior of Reinforced Concrete Exterior Beam-Column Connections under Cyclic Loading

Sabah

Harba

2021

E3S Web Conf.

View full text Add to dashboard Cite

In many seismically active regions worldwide, massive reinforced concrete (RC) structures built before the 1970s existed. These older RC buildings, in countries having seismic history, were designed for gravity loads only. Anyway, the beam-column connections influence the structures where the functions of connection shortage by transport the forces like shear, moment, and torsion through the beam to the column. Also, it could behave in a ductile manner to help the structure resist the seismic, as simulate the seismic loading by high and low cyclic loading. Due to the failure of external joints more than the internal beam-column joints, this review focuses on the behavior of exterior beam-column joints under cyclic loading, consequently simulated the behavior under an earthquake and the reinforcement detailed.

show abstract

Section: Previous Analytical Studiesmentioning

confidence: 99%

Section: Ref Used Parameters Objectives Of Studymentioning

confidence: 99%

A Review-Behavior of Reinforced Concrete Exterior Beam-Column Connections under Cyclic Loading

Sabah

Harba

2021

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…It has the advantages of global search, high parallelism and strong generalization ability. Applying GA to BP neural network can enhance the accuracy of network training convergence, which gives full play to the global optimization characteristics of GA (Yan et al, 2016;Kotsovou et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Prediction and analysis of compressive strength of recycled aggregate thermal insulation concrete based on GA-BP optimization network

Liu

Zhou

et al. 2020

JEDT

View full text Add to dashboard Cite

Purpose This paper aims to predict the 28-day compressive strength of recycled thermal insulation concrete more accurately. Design/methodology/approach The initial weights and thresholds of BP neural network are improved by genetic algorithm on MATLAB 2014 a platform. Findings Genetic algorithm–back propagation (GA-BP) neural network is more stable. The generalization performance of the complex is better. Originality/value The GA-BP neural network based on the training sample data can better realize the strength prediction of recycled aggregate thermal insulation concrete and reduce the complex orthogonal experimental process. GA-BP neural network is more stable. The generalization performance of the complex is better.

show abstract

“…Heidari et al [26] used a BP neural network to predict the compressive strength of waste concrete. Recently, Kotsovou et al [27] reported a comprehensive study on the failure assessment of reinforced concrete exterior beam-column joints using ANNs that the results were compared to the analytical methods indicating the accuracy of prediction model. More recently, Gharehbaghi et al [6] used ANN and SVM methods to estimate the inelastic seismic responses of reinforced concrete structures.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Performance Point of Semi-Rigid Steel Frames Using Artificial Neural Networks

Bahmani¹,

Ghasemi²,

Mousaviamjad³

et al. 2019

IJISA

View full text Add to dashboard Cite

One of the main steps in the performance based seismic analysis and design of structures is determination of performance point where the nonlinear static analysis approach is used. The aim of this paper is to predict the performance point of semi-rigid steel frames using Artificial Neural Networks. As such, to generate data required for the prediction, several semirigid steel frames were modeled and their performance point was determined then. Ten input variables including number of bays, number of stories, bays width, moment of inertia of beams, cross sectional area of columns, cross sectional area of braces, rigidity degree of connections and soft story (existence or nonexistence) were considered in the prediction. In addition, the actual results were obtained at the presence of different earthquake intensity levels and soil types. Back Propagation with eleven different algorithms and Radial Basis Function Artificial Neural Networks were used in the prediction. The prediction process was carried out in two steps. In the first step, all samples were used for the prediction and the performance metrics were computed. In the second step, three of the best networks were selected, and the optimum number of samples was found considering a very slight reduction in the accuracy of the networks used. Finally, it was shown that, despite using rather limited number of samples, the generated Artificial Neural Networks accurately predict the performance point of semi-rigid steel frames.

show abstract

Assessment of RC exterior beam-column Joints based on artificial neural networks and other methods

Cited by 34 publications

References 17 publications

A Review-Behavior of Reinforced Concrete Exterior Beam-Column Connections under Cyclic Loading

A Review-Behavior of Reinforced Concrete Exterior Beam-Column Connections under Cyclic Loading

Prediction and analysis of compressive strength of recycled aggregate thermal insulation concrete based on GA-BP optimization network

Prediction of Performance Point of Semi-Rigid Steel Frames Using Artificial Neural Networks

Contact Info

Product

Resources

About