Dynamic Evaluation for Compaction Quality of Roller Compacted Concrete based on Reliability Metrics
Publication: Journal of Construction Engineering and Management
Volume 146, Issue 10
Abstract
The compaction quality of the compacted layer is a major concern of roller compacted concrete (RCC). Current compaction quality evaluation methods, such as testing random sampling spots or predicting with common models, have strong deviation due to the limited amount of data points and the ignorance of the effect of parameter variability on the reliability of evaluation results. This study presents a dynamic quality evaluation method by incorporating reliability to account for the variability of material parameters. This method consists of three parts. First, the compactness of the compacted layer is predicted using genetic algorithm-based support vector machine (GA-SVM). Second, reliability analysis is proposed to incorporate the influence of material parameter variability on the credibility of compactness prediction. Finally, the index as an evaluation criterion is developed based on compactness and reliability by which the compaction quality of RCC is predicted. By using the kriging interpolation procedure, the compactness and reliability at any point of a work area can be estimated, and the overall passing rate of compaction quality of the work area can be analyzed. The advantages of the proposed method are as follows: (1) through minimizing structural risks, GA-SVM model can solve the problem of high deviation and low accuracy caused by limited sampling data in common models; and (2) to overcome the limitation of low reliability of single compactness evaluation, the reliability index is introduced to quantify the impact of material parameter variability on the credibility of the compactness.
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Data Availability Statement
All data, models, or code generated or used during the study are available from the corresponding author by request.
Acknowledgments
This study is financially supported by the National Natural Science Fund of China (Nos. 51879094 and 51909072) and the Fundamental Research Funds for the Central Universities (No. 2018B00214).
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Published In
Journal of Construction Engineering and Management
Volume 146 • Issue 10 • October 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Dec 14, 2019
Accepted: Jun 1, 2020
Published online: Aug 13, 2020
Published in print: Oct 1, 2020
Discussion open until: Jan 13, 2021
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