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Madandoust, R., Ranjbar, M., momeni, K. (2018). Generalized GMDH-Type Neural Network for Prediction of Concrete Compressive Strength via Core Testing. Electronic and Cyber Defense, 9(4), 405-414.
Rahmat Madandoust; Malek Mohammad Ranjbar; komeil momeni. "Generalized GMDH-Type Neural Network for Prediction of Concrete Compressive Strength via Core Testing". Electronic and Cyber Defense, 9, 4, 2018, 405-414.
Madandoust, R., Ranjbar, M., momeni, K. (2018). 'Generalized GMDH-Type Neural Network for Prediction of Concrete Compressive Strength via Core Testing', Electronic and Cyber Defense, 9(4), pp. 405-414.
Madandoust, R., Ranjbar, M., momeni, K. Generalized GMDH-Type Neural Network for Prediction of Concrete Compressive Strength via Core Testing. Electronic and Cyber Defense, 2018; 9(4): 405-414.

Generalized GMDH-Type Neural Network for Prediction of Concrete Compressive Strength via Core Testing

علوم و فناوریهای پدافند نوین
Article 3, Volume 9, Issue 4 - Serial Number 34, August 2018, Pages 405-414    PDF (1.16 M)
Authors
Rahmat Madandoust* ; Malek Mohammad Ranjbar; komeil momeni
Receive Date: 30 January 2019,  Revise Date: 16 July 2025,  Accept Date: 30 January 2019 
Abstract
In this paper, generalized Group Method of Data Handling (GMDH)-type neural network has been successfully used for modeling concrete core testing including reinforcing bars based on various data obtained experimentally. Genetic Algorithm (GA) and Singular Value Decomposition (SVD) techniques are deployed for optimal design of such model. A set of input-output data for the training and testing the evolved models are employed in which core diameter, length-to-diameter ratio, number of reinforcing bars, distance of bar axis from nearer end of core as well as strength of cores, with or without reinforcing bars, are considered as inputs and standard cube strength of concrete is regarded as the output variables. The comparison of results obtained experimentally in this work with the proposed GMDH model depicted that this model has a great ability for prediction of the concrete compressive strength on the basis of core testing. Finally, sensitivity analysis was performed on the models obtained by GMDH-type neural network to study the influence of input parameters on model output. The sensitivity analysis reveals that the output variable (standard cube strength) is significantly changed by core strength and number of rebars in comparison with other input variables.
Keywords
compressive strength; core test; GMDH; genetic algorithm; reinforcing bars
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