Nondestructive Concrete Strength Estimation based on Electro-Mechanical Impedance with Artificial Neural Network

In the current study 28 day strength of Recycled Aggregate Concrete (RAC) and Fly ash (class F) based concrete is predicted using Artificial Neural Network (ANN), Multigene Genetic Programming (MGGP) and Model Tree (MT). Four sets of models were designed for per cubic proportions of materials, Properties of materials and non-dimensional parameters as input parameters. The study shows that the predicted 28 day strength is in good agreement with the observed data and also generalize well to untrained data. ANN outperforms MGGP and MT in terms of model performance. Output of the developed models can be presented in terms of trained weights and biases in ANN, equations in MGGP and in the form of series of equations in MT. ANN, MGGP and MT can grasp the influence of input parameters which can be seen through Hinton diagrams in ANN, input frequency distribution in MGGP and coefficients of input parameters in MT. The study shows that these data driven techniques can be used for developing model/s to predict strength of concrete with an acceptable performance.

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Concrete strength prediction using artificial neural network and genetic programming

Challenge Journal of Concrete Research Letters ◽

10.20528/cjcrl.2018.03.002 ◽

2018 ◽

Vol 9 (3) ◽

pp. 75

Author(s):

Preeti Kulkarni ◽

Shreenivas N. Londhe

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Genetic Programming ◽

Construction Material ◽

Concrete Strength ◽

Maximum Size ◽

Design Parameters ◽

Ann Model ◽

Influencing Parameters ◽

Artificial Neural

Concrete is a highly complex composite construction material and modeling using computing tools to predict concrete strength is a difficult task. In this work an effort is made to predict compressive strength of concrete after 28 days of curing, using Artificial Neural Network (ANN) and Genetic programming (GP). The data for analysis mainly consists of mix design parameters of concrete, coefficient of soft sand and maximum size of aggregates as input parameters. ANN yields trained weights and biases as the final model which sometime may impediment in its application at operational level. GP on other hand yields an equation as its output making its plausible tool for operational use. Comparison of the prediction results displays the result the model accuracy of both ANN and GP as satisfactory, giving GP a working advantage owing to its output in an equation form. A knowledge extraction technique used with the weights and biases of ANN model to understand the most influencing parameters to predict the 28 day strength of concrete, promises to prove ANN as grey box rather than a black box. GP models, in form of explicit equations, show the influencing parameters with reference to the presence of the relevant parameters in the equations.

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Estimation of the Concrete Strength Setting off from the Physical Properties of the Aggregates by Artificial Neural Network and Linear Regression Method and Comparison of the Results

International Conference on Future Computer and Communication, 3rd (ICFCC 2011) ◽

10.1115/1.859711.paper50 ◽

2011 ◽

pp. 335-344

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Linear Regression ◽

Physical Properties ◽

Concrete Strength ◽

Regression Method ◽

Linear Regression Method ◽

Artificial Neural ◽

Comparison Of The Results

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Shear Resistance Prediction of Post-fire Reinforced Concrete Beams Using Artificial Neural Network

International Journal of Concrete Structures and Materials ◽

10.1186/s40069-019-0358-8 ◽

2019 ◽

Vol 13 (1) ◽

Cited By ~ 1

Author(s):

Bin Cai ◽

Long-Fei Xu ◽

Feng Fu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Reinforced Concrete ◽

Concrete Strength ◽

Shear Resistance ◽

Reinforced Concrete Beams ◽

Rc Beams ◽

Residual Shear Strength ◽

Element Analysis ◽

Artificial Neural

Abstract In this paper, a prediction method based on artificial neural network was developed to rapidly determine the residual shear resistance of reinforced concrete (RC) beams after fire. Firstly, the temperature distribution along the beam section was determined through finite element analysis using software ABAQUS. A residual shear strength calculation model was developed and validated using the test data. Using this model, 384 data entries were derived for training and testing. The input layer of neural network involved parameters of beam height, beam width, fire exposure time, cross-sectional area of stirrup, stirrup spacing, concrete strength, and concrete cover thickness. The output was the shear resistance of RC beams. It was found that use of BP neural network could precisely predict the post-fire shear resistance of RC beams. The predicted data were highly consistent with the target data. Thus, this is a novel method for computing post-fire shear resistance of RC beams. Using this new method, further investigation was also made on the effects of different parameters on the shear resistance of the beams.

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Axial Capacity Prediction of Concrete-Filled Steel Tubular Short Members Using Multiple Linear Regression and Artificial Neural Network

Materials Science Forum ◽

10.4028/www.scientific.net/msf.1047.220 ◽

2021 ◽

Vol 1047 ◽

pp. 220-226

Author(s):

Md Nasir Uddin ◽

Ling Zhi Li ◽

Raja Khurram Mahmood Khan ◽

Farhan Shahriar ◽

Landry Wilfried Tim Sob

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Concrete Strength ◽

Ultimate Capacity ◽

Axial Strength ◽

Axial Capacity ◽

Wide Range ◽

Artificial Neural ◽

Cft Column ◽

Capacity Prediction

The estimation of the ultimate capacity of rectangular or circular shaped steel tubular members filled with concrete, such as columns, beams, and beam-column connections, requires a detailed structural study to be carried out. Therefore, identify the concrete strength the member subjected to axial-load only. Using the Levenberg-Marquardt artificial neural network, this paper investigates the concrete-filled steel tubular (CFT) members axial strength. 201 experimental specimens were collected from the literature to obtain the best results, and a wide range of geometric and material properties of CFT members were included. The proposed design and specimens illustrate the practicality and effectiveness of the chosen CFT column approach to classify real structural results.

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