Raktipong Sahamitmongkol

Test system for defect detection in construction materials with ultrasonic waves by support vector machine and neural network

This paper introduces a newly developed test system for structure inspection and evaluation of cement-based products by applying ultrasonic test with support vector machine (SVM) classifier. In other words, this paper represents a novel method based on SVM for defect detection, classification of number of defects, and identification of defect materials. With the system, pattern of ultrasonic waves for each case of specimen can be obtained from direct and indirect measurements. Machine learning algorithm called support vector machine and artificial neural network (ANN) are employed for classification and verification of the wave patterns obtained from different samples. By applying the system, the presence or absence of a defect in mortar can be identified. Moreover, the system can also classify the number of defects and identify the defect materials being inside the mortar. For classification, input features are extracted in different ways and the numbers of training sets are varied. Base on the results from SVM, the signals extracted in frequency domain gives better performance than time domain. Using a larger training set can give more satisfactory results. In this article, the methodology is explained and the classification results are discussed. The effectiveness of the developed test system is evaluated. Comparison of the classification results that obtained by between SVM and ANN classifiers is also demonstrated. This study shows that this technique based on pattern recognition has a high potential for practical inspection of concrete structure.

Damage analysis of an RC column subjected to long-term transient elevated temperature

Damage in a reinforced concrete column subjected to long-term elevated temperature (approximately 150°C) was studied by conducting detail inspection as well as finite element analysis. The damage information was collected through visual inspection, non-destructive tests, coring, and chemical analysis. Inspection results illustrated more damage level in the portion exposed to high temperature. Chemical analysis as well as cracking patterns indicated that the damage was not the same as the damage caused by fire attack and was not caused by chloride penetration, carbonation or sulphate attack. The mechanism of the damage was clarified by using finite element analysis with a condition of sudden drop of surface temperature. It was found from the analysis that cracking can be induced on the surface of the column due to temperature gradient if there is a substantial surface temperature drop.

New developed testing system of defect in cementitious material with Neural Network

A developed testing system of defect detection in mortar is introduced. Base on this development, more information of the defection could be obtained. Machine Learning algorithm, named Artificial Neural Network (ANN) with pattern recognition, is used to verify the results. As a result, the medium with and without defects could be identified. Moreover, the number of defects could be indicated. This study shows the methodology for implementing the system, factors to be considered, and verifying the results. Classification examples are given to demonstrate the performance of the network by using the data set from different mediums. The result shows the effectiveness of this classifier and possibility of further development.