Dynamic evaluation for compaction quality of roller compacted concrete based on reliability metrics

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 R 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.