A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Multifractal Analysis and Compressive Strength Prediction for Concrete through Acoustic Emission Parameters
Acoustic emission (AE) can be applied to identify crack propagation and damage of materials and structures. However, few studies investigate the multifractal regularity and compressive strength prediction for concrete using AE parameters. Therefore, the major objective of this research is to perform multifractal analysis of damage and develop support vector machine (SVM) for strength prediction based on AE parameters. Meanwhile, fuzzy c-means (FCM) was implemented to identify damage mechanisms. The results showed that the level of damage can be revealed qualitatively and quantitatively by analyzing morphology and parameters of multifractal. In particular, the multifractal parameter α0 has the ability to identify critical damage and primary failure surface. Moreover, damage mechanisms were further distinguished by FCM. Finally, the results showed that the parameters of AE can further expand the application of AE for predicting compressive of concrete. SVM prediction results using AE parameters perform higher precision than the artificial neural network (ANN). Furthermore, a significant reduction in sample size uses AE parameters to predict concrete strength.
Multifractal Analysis and Compressive Strength Prediction for Concrete through Acoustic Emission Parameters
Acoustic emission (AE) can be applied to identify crack propagation and damage of materials and structures. However, few studies investigate the multifractal regularity and compressive strength prediction for concrete using AE parameters. Therefore, the major objective of this research is to perform multifractal analysis of damage and develop support vector machine (SVM) for strength prediction based on AE parameters. Meanwhile, fuzzy c-means (FCM) was implemented to identify damage mechanisms. The results showed that the level of damage can be revealed qualitatively and quantitatively by analyzing morphology and parameters of multifractal. In particular, the multifractal parameter α0 has the ability to identify critical damage and primary failure surface. Moreover, damage mechanisms were further distinguished by FCM. Finally, the results showed that the parameters of AE can further expand the application of AE for predicting compressive of concrete. SVM prediction results using AE parameters perform higher precision than the artificial neural network (ANN). Furthermore, a significant reduction in sample size uses AE parameters to predict concrete strength.
Multifractal Analysis and Compressive Strength Prediction for Concrete through Acoustic Emission Parameters
Zhiqiang Lv (author) / Annan Jiang (author) / Jiaxu Jin (author) / Xiangfeng Lv (author)
2021
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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