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Computers and Concrete
  Volume 19, Number 1, January 2017, pages 059-68

Artificial neural network model using ultrasonic test results to predict compressive stress in concrete
Jason Ongpeng, Marcus Soberano, Andres Oreta and Sohichi Hirose

    This study focused on modeling the behavior of the compressive stress using the average strain and ultrasonic test results in concrete. Feed-forward backpropagation artificial neural network (ANN) models were used to compare four types of concrete mixtures with varying water cement ratio (WC), ordinary concrete (ORC) and concrete with short steel fiber-reinforcement (FRC). Sixteen (16) 150 mmx150 mmx150 mm concrete cubes were used; each contained eighteen (18) data sets. Ultrasonic test with pitch-catch configuration was conducted at each loading state to record linear and nonlinear test response with multiple step loads. Statistical Spearman\'s rank correlation was used to reduce the input parameters. Different types of concrete produced similar top five input parameters that had high correlation to compressive stress: average strain, fundamental harmonic amplitude (A1), 2nd harmonic amplitude (A2), 3rd harmonic amplitude (A3), and peak to peak amplitude (PPA). Twenty-eight ANN models were trained, validated and tested. A model was chosen for each WC with the highest Pearson correlation coefficient (R) in testing, and the soundness of the behavior for the input parameters in relation to the compressive stress. The ANN model showed increasing WC produced delayed response to stress at initial stages, abruptly responding after 40%. This was due to the presence of more voids for high water cement ratio that activated Contact Acoustic Nonlinearity (CAN) at the latter stage of the loading path. FRC showed slow response to stress than ORC, indicating the resistance of short steel fiber that delayed stress increase against the loading path.
Key Words
    artificial neural network; linear ultrasonic test; nonlinear ultrasonic test; concrete; fiber-reinforced concrete
Jason Ongpeng, Marcus Soberano, Andres Oreta: Department of Civil Engineering, De La Salle University, Manila, Philippines
Sohichi Hirose: Department of Civil Engineering, Tokyo Institute of Technology, Tokyo, Japan

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