Intelligent compaction methods and quality control

Abstract

Ensuring high-quality fill compaction is crucial for the stability and longevity of infrastructures and affects the sustainability of urban infrastructure networks. The purpose of this paper is to provide a refined analysis and insight understanding of the current practice, limitations, challenges, and future development trends of compaction methods from the perspective of the development stage. This paper offers a comprehensive overview of the evolution of compaction methods and classifies compaction quality control methods into four groups through quantitative analysis of literature: traditional compaction methods, digital compaction methods, automated compaction methods, and intelligent compaction methods. Each method's properties and issues are succinctly stated. Then, the research on three key issues in intelligent compaction including compaction quality evaluation algorithms, dynamic optimal path planning, and implementation of unmanned technology is summarized. Currently, the field of intelligent compaction is far from mature, a few challenges and limitations need further investigation: coupling problems of multiple indicators in intelligent evaluation algorithms, unmanned roller groups collaborative control problems, and intelligent decision-making and optimization problems of multi-vehicle compaction paths. This review serves as a valuable reference for systematically understanding the development of compaction methods.

Recent Development in Intelligent Compaction for Asphalt Pavement Construction: Leveraging Smart Sensors and Machine Learning

“…Continuous feedback control yields more consistent outcomes versus batch-style verification. However, while progress has been substantial, further validation under varied real-world conditions remains essential to unlock IC's full potential [53][54][55]. The following findings can be drawn:…”