• Journal of KIISE
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• v.43 no.8
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• pp.883-892
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• 2016

Spectrum-based fault localization (SFL) method assigns a suspicious ratio. The statement is strongly affected by a failed test case compared to a passed test case. A failed test case assigns a suspicious ratio while a passed test case reduces some parts of assigned suspicious ratio. In the absence of a failed test case, it is impossible to localize the fault. Thus, a failed test case is very important for fault localization. However, spectrum-based fault localization has difficulty in reflecting the unique characteristics of a failed test because a failed test case and a passed test case are input at the same time to calculate a suspicious ratio. This paper supplements for this limitation and suggests a test case grouping method for more accurate fault localization. In addition, this paper suggested a filtering method considering test efficiency and verified the effectiveness by applying 65 algorithms. In 90 % of whole methods, the accuracy was improved by 13% and the effectiveness was improved by 72% based on EXAM score.

• Computers and Concrete
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• v.34 no.2
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• pp.247-265
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• 2024

Measuring the fracture toughness of concrete in laboratory settings is challenging due to various factors, such as complex sample preparation procedures, the requirement for precise instruments, potential sample failure, and the brittleness of the samples. Therefore, there is an urgent need to develop innovative and more effective tools to overcome these limitations. Supervised learning methods offer promising solutions. This study introduces seven machine learning algorithms for predicting concrete's effective fracture toughness (K-eff). The models were trained using 560 datasets obtained from the central straight notched Brazilian disc (CSNBD) test. The concrete samples used in the experiments contained micro silica and powdered stone, which are commonly used additives in the construction industry. The study considered six input parameters that affect concrete's K-eff, including concrete type, sample diameter, sample thickness, crack length, force, and angle of initial crack. All the algorithms demonstrated high accuracy on both the training and testing datasets, with R² values ranging from 0.9456 to 0.9999 and root mean squared error (RMSE) values ranging from 0.000004 to 0.009287. After evaluating their performance, the gated recurrent unit (GRU) algorithm showed the highest predictive accuracy. The ranking of the applied models, from highest to lowest performance in predicting the K-eff of concrete, was as follows: GRU, LSTM, RNN, SFL, ELM, LSSVM, and GEP. In conclusion, it is recommended to use supervised learning models, specifically GRU, for precise estimation of concrete's K-eff. This approach allows engineers to save significant time and costs associated with the CSNBD test. This research contributes to the field by introducing a reliable tool for accurately predicting the K-eff of concrete, enabling efficient decision-making in various engineering applications.