A data-driven method for real-time compaction quality evaluation of a cement-stabilized base layer. Issue 11 (August 2022)

• Record Type: Journal Article
• Authors: Wu, Qilun; Cao, Weidong; Lam, Heung-Fai; Adeagbo, Mujib Olamide
• Is Part Of: Advances in structural engineering. Volume 25:Issue 11(2022)
• Page Start: 2319
• Page End: 2331
• Publication Date: 2022-08
• Subjects: Compaction quality -- cement-stabilized base layer -- natural frequency -- support vector machine -- data-driven; Structural engineering -- Periodicals; Construction, Technique de la; Structural engineering; Periodicals; 624.1
• DOI: 10.1177/13694332221094633
• Languages: English
• ISSNs: 1369-4332

Abstract

Adequate and uniform compaction is essential for the safety and durability of a pavement structure. The current compaction quality control, which relies on limited spot tests data, cannot provide timely feedback about compaction quality. The main objective of this paper is to develop a novel data-driven method for compaction quality assessment of cement-stabilized base in a real-time manner. The basic idea is to consider the drum of the vibratory roller together with the underlying cement-stabilized base layer as a spring-mass-dashpot system. The compaction level of the base is connected to the stiffness of the system and to the corresponding natural frequencies. In this study, the vibration of the drum was monitored during operation. The fundamental mode natural frequency of the system at different compaction levels of the base was identified by the fast Bayesian fast Fourier transform method. The results indicate that the natural frequency of the vibratory system has a positive correlation with the degree of compaction (DOC) of the underlying cement-stabilized base layer. Therefore, a novel and practical method for on-site compaction quality assessment utilizing measured natural frequency is proposed in this paper. A data-driven classification method based on support vector machine (SVM) was developed to realize the compaction quality evaluation. The classification results show that the proposed method can recognize signal patterns corresponding to different DOCs. The proposed method combines vibration theory and machine learning to provide a technical reference for the research of continuous compaction control with vibratory rollers.