Evaluation of Compaction Quality Based on SVR with CFA: Case Study on Compaction Quality of Earth-Rock Dam

Abstract

The compaction quality of earth-rock dam materials is a major concern in the evaluation of earth-rock dams. Current compaction quality assessment methods, such as graphical reports or simple prediction models, are imprecise and can cause unobserved quality assessment defects. These methods do not comprehensively consider factors that affect the compaction quality because they do not integrate heterogeneous construction data sets collected by different data acquisition systems. In this research, a method of assessing compaction quality on the basis of support vector regression (SVR), the chaos-based firefly algorithm, is presented. The assessment method has three stages. In the first stage, a chaotic firefly algorithm (CFA) is proposed to optimize the SVR hyperparameters. In the second stage, a multisource heterogeneous data integration subsystem based on the compaction monitoring system is designed, in which compaction monitoring data, material source statistical data, and detected data from test pits are integrated. Finally, the optimized SVR is used to evaluate the compaction quality of the storehouse surface. The significance of the proposed method is threefold: first, it integrates both chaos theory and the firefly algorithm to optimize the SVR hyperparameters; second, it integrates heterogeneous construction data, allowing comprehensive consideration of factors that affect the compaction quality; and third, it has high prediction accuracy because it implements structural risk minimization. Compared with current models based on empirical risk minimization, the proposed method performs the best according to several error measures.