Applying Adaptive Neural Fuzzy Inference System to Improve Concrete Strength Estimation in Ultrasonic Pulse Velocity Tests
When inspecting the property of material, nondestructive testing methods are more preferable than destructive testing since they do not damage the test sample. Nondestructive testing methods, however, might not yield the same accurate results in examining the property of material when compared with...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2018/2451915 |
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| author | Loan T. Q. Ngo Yu-Ren Wang Yi-Ming Chen |
| author_facet | Loan T. Q. Ngo Yu-Ren Wang Yi-Ming Chen |
| author_sort | Loan T. Q. Ngo |
| collection | DOAJ |
| description | When inspecting the property of material, nondestructive testing methods are more preferable than destructive testing since they do not damage the test sample. Nondestructive testing methods, however, might not yield the same accurate results in examining the property of material when compared with destructive testing. To improve the result of nondestructive testing methods, this research applies artificial neural networks and adaptive neural fuzzy inference system in predicting the concrete strength estimation using nondestructive testing method, the ultrasonic pulse velocity test. In this research, data from a total of 312 cylinder concrete samples were collected. Ultrasonic pulse velocity test was applied to those 312 samples in the lab, following the ASTM procedure. Then, the testing results of 312 samples were used to develop and validate two artificial intelligence prediction models. The research results show that artificial intelligence prediction models are more accurate than statistical regression models in terms of the mean absolute percentage error. |
| format | Article |
| id | doaj-art-a13cd28282974c9db09037c6c279fcec |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-a13cd28282974c9db09037c6c279fcec2025-02-03T01:24:33ZengWileyAdvances in Civil Engineering1687-80861687-80942018-01-01201810.1155/2018/24519152451915Applying Adaptive Neural Fuzzy Inference System to Improve Concrete Strength Estimation in Ultrasonic Pulse Velocity TestsLoan T. Q. Ngo0Yu-Ren Wang1Yi-Ming Chen2Department of Civil Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, TaiwanDepartment of Civil Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, TaiwanDepartment of Civil Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, TaiwanWhen inspecting the property of material, nondestructive testing methods are more preferable than destructive testing since they do not damage the test sample. Nondestructive testing methods, however, might not yield the same accurate results in examining the property of material when compared with destructive testing. To improve the result of nondestructive testing methods, this research applies artificial neural networks and adaptive neural fuzzy inference system in predicting the concrete strength estimation using nondestructive testing method, the ultrasonic pulse velocity test. In this research, data from a total of 312 cylinder concrete samples were collected. Ultrasonic pulse velocity test was applied to those 312 samples in the lab, following the ASTM procedure. Then, the testing results of 312 samples were used to develop and validate two artificial intelligence prediction models. The research results show that artificial intelligence prediction models are more accurate than statistical regression models in terms of the mean absolute percentage error.http://dx.doi.org/10.1155/2018/2451915 |
| spellingShingle | Loan T. Q. Ngo Yu-Ren Wang Yi-Ming Chen Applying Adaptive Neural Fuzzy Inference System to Improve Concrete Strength Estimation in Ultrasonic Pulse Velocity Tests Advances in Civil Engineering |
| title | Applying Adaptive Neural Fuzzy Inference System to Improve Concrete Strength Estimation in Ultrasonic Pulse Velocity Tests |
| title_full | Applying Adaptive Neural Fuzzy Inference System to Improve Concrete Strength Estimation in Ultrasonic Pulse Velocity Tests |
| title_fullStr | Applying Adaptive Neural Fuzzy Inference System to Improve Concrete Strength Estimation in Ultrasonic Pulse Velocity Tests |
| title_full_unstemmed | Applying Adaptive Neural Fuzzy Inference System to Improve Concrete Strength Estimation in Ultrasonic Pulse Velocity Tests |
| title_short | Applying Adaptive Neural Fuzzy Inference System to Improve Concrete Strength Estimation in Ultrasonic Pulse Velocity Tests |
| title_sort | applying adaptive neural fuzzy inference system to improve concrete strength estimation in ultrasonic pulse velocity tests |
| url | http://dx.doi.org/10.1155/2018/2451915 |
| work_keys_str_mv | AT loantqngo applyingadaptiveneuralfuzzyinferencesystemtoimproveconcretestrengthestimationinultrasonicpulsevelocitytests AT yurenwang applyingadaptiveneuralfuzzyinferencesystemtoimproveconcretestrengthestimationinultrasonicpulsevelocitytests AT yimingchen applyingadaptiveneuralfuzzyinferencesystemtoimproveconcretestrengthestimationinultrasonicpulsevelocitytests |