Saowanee Saechai, Phatra Kusalanggoorawat, Waree Kongprawechnon, and Raktipong Sahamitmongkol

pp. 217 - 225

Abstract

This paper introduces a newly developed test system for defect detection, classification of number of defects and
identification of defect materials in cement-based products. With the system, the pattern of ultrasonic waves for each case of
specimen can be obtained from direct and indirect measurements. The machine learning algorithm called artificial neural
network classifier with back-propagation model is employed for classification and verification of the wave patterns obtained
from different specimens. By applying the system, the presence or absence of a defect in mortar can be identified. Moreover,
the system is applied to identify the number and materials of defects inside the mortar. The methodology is explained and the
classification results are discussed. The effectiveness of the developed test system is evaluated. Comparison of the classification results between different input features with different number of training sets is demonstrated. The results show that this
technique based on pattern recognition has a potential for practical inspection of concrete structures.