Crack width prediction of RC structures by Artificial Neural Networks

The fundamental period is one of the most critical parameters for the seismic design of structures. There are several literature approaches for its estimation which often conflict with each other, making their use questionable. Furthermore, the majority of these approaches do not take into account the presence of infill walls into the structure despite the fact that infill walls increase the stiffness and mass of structure leading to significant changes in the fundamental period. In the present paper, artificial neural networks (ANNs) are used to predict the fundamental period of infilled reinforced concrete (RC) structures. For the training and the validation of the ANN, a large data set is used based on a detailed investigation of the parameters that affect the fundamental period of RC structures. The comparison of the predicted values with analytical ones indicates the potential of using ANNs for the prediction of the fundamental period of infilled RC frame structures taking into account the crucial parameters that influence its value.

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Crack width in concrete using artificial neural networks

Engineering Structures ◽

10.1016/j.engstruct.2013.03.020 ◽

2013 ◽

Vol 52 ◽

pp. 676-686 ◽

Cited By ~ 10

Author(s):

Ahmed A. Elshafey ◽

Nabil Dawood ◽

H. Marzouk ◽

M. Haddara

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Crack Width ◽

Artificial Neural

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Crack Width Estimation Using Feed and Cascade Forward Back Propagation Artificial Neural Networks

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.786.293 ◽

2018 ◽

Vol 786 ◽

pp. 293-301 ◽

Cited By ~ 1

Author(s):

Hesham M. Shehata ◽

Yasser S. Mohamed ◽

Mohamed Abdellatif ◽

Taher H. Awad

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Crack Width ◽

Laser Scanning ◽

Back Propagation ◽

Maximum Width ◽

The Neural Networks ◽

Maximum Crack ◽

Artificial Neural ◽

The Cost

Automatic crack inspection techniques that limit the necessity of human have the potential to lower the cost and time of the process. In this study, a maximum crack width estimation approach is presented. Seventy nine segments of cracks are used for training the neural networks and twenty six segments are used for examination. The maximum width for each segment is measured using laser scanning microscope and segment image is captured and magnified using the microscope camera in order to obtain the extracted crack profile number of pixels. Feed and cascade forward back propagation artificial neural networks are designed and constructed. The input and output for the networks are the crack width in terms of number of pixels and the maximum estimated crack width respectively. It is shown that, the artificial neural networks technique can effectively be used to estimate the crack width. The feedforward back propagation structure which is designed with two layers and training function TRAINLM gives the best results in examination.

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