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Appl. Sci. 2018, 8(3), 405;

Investigation on the Sensitivity of Ultrasonic Test Applied to Reinforced Concrete Beams Using Neural Network

Department of Civil Engineering, De La Salle University, 2401 Taft Avenue, Manila 1004, Philippines
Department of Civil Engineering, Tokyo Institute of Technology, Meguro Ookayama 2-12-1, Tokyo 152-8552, Japan
Author to whom correspondence should be addressed.
Received: 1 February 2018 / Revised: 28 February 2018 / Accepted: 5 March 2018 / Published: 9 March 2018
(This article belongs to the Special Issue Soft Computing Techniques in Structural Engineering and Materials)
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An experiment on reinforced concrete beams using four-point bending test during an ultrasonic test was conducted. Three beam specimens were considered for each water/cement ratio (WC) of 40% and 60%, with three reinforcement schedules named design A (comprising two top bars and two bottom bars), design B (with two bottom bars), and design C (with one bottom bar). The concrete beam had a size of 100 mm × 100 mm × 400 mm in length with a plain reinforcement bar of 9 mm in diameter. An ultrasonic test with pitch–catch configuration was conducted at each loading with the transducers oriented in direct transmission across the beams' length with recordings of 68 datasets per beam specimen. Recordings of ultrasonic test results and strains at the top and bottom surfaces subjected to multiple step loads in the experiment were done. After the collection of the data, feed-forward backpropagation artificial neural network (ANN) was used to investigate the sensitivity of the ultrasonic parameters to the mechanical load applied. Five input parameters were examined, as follows: neutral axis (NA), fundamental harmonic amplitude (A1), second harmonic amplitude (A2), third harmonic amplitude (A3), and peak-to-peak amplitude (PPA), while the output parameter was the percentage of ultimate load. Optimum models were chosen after training, validating, and testing 60 ANN models. The optimum model was chosen on the basis of the highest Pearson’s Correlation Coefficient (R) and soundness, confirming that it exhibited good behavior in agreement with theories. A classification of sensitivity was performed using simulations based on the developed optimum models. It was found that A2 and NA were sensitive to all WC and reinforcements used in the ANN simulation. In addition, the range of sensitivity of A2 and NA was inversely and directly proportional to the reinforcing bars, respectively. This study can be used as a guide in the selection of ultrasonic parameters to assess damage in concrete with low or high WC and varying reinforcement content. View Full-Text
Keywords: non-destructive test; concrete; ultrasonic; bending; neural network non-destructive test; concrete; ultrasonic; bending; neural network

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Ongpeng, J.M.C.; Oreta, A.W.C.; Hirose, S. Investigation on the Sensitivity of Ultrasonic Test Applied to Reinforced Concrete Beams Using Neural Network. Appl. Sci. 2018, 8, 405.

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