journal article·2024-12Open access

Compaction quality assessment of highway using roller-integrated positive maximum kinetic energy increment detection technique

Xian Yang, Chuping Wu, Lan Qiao, Qinglong Zhang, Huahai Wu, Ren Liu, Wang Guo, Cunbing Chen

Abstract

During conventional highway construction, quality control and acceptance (QC/QA) are based on limited spot tests of material density at random locations which may not comprehensively represent the entire compacted area and are vulnerable to potential biases. Based on roller-integrated positive maximum kinetic energy increment detection technique with real-time kinematic global positioning systems, this research adopted kinetic energy compaction value (KECV) as a real-time monitoring index for the highway compaction quality, and subsequently proposed an KECV-based assessment method to estimate the compaction quality of subgrade and pavement materials. Additionally, a global interpolation method (Green spline) was adopted to obtain estimates for both KECVs and compaction quality at any location on the work area, enabling the analysis of the passing rate of compaction quality for the entire work area. A case study on the Hengyong highway project in China indicates a highly linear correlation between the KECV and the compaction parameters, the compactness of LLL silt, and the relative density of cement-stabilized macadam; therefore, it can serve as a reliable index for monitoring compaction quality. Assessment of compaction quality on the entire work area can be fast and continuously achieved using the proposed method, which can effectively overcome the above limitation of conventional testing, timely feedback compaction information to avoid quality defects, and availably improve the construction quality of highways.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Asphalt Pavement Performance Evaluation · Rock Mechanics and Modeling · Infrastructure Maintenance and Monitoring

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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