Method for Detecting Roadbed Compaction Degree Based on Machine Learning and Vibration Acceleration

Abstract

Roadbed construction occupies a core position in highway construction, but its quality is easily constrained by multiple factors such as changing environmental factors, the performance of construction equipment, and the professional abilities of construction personnel, leading to potential quality risks. Traditional quality inspection methods are mostly carried out after construction is completed, making it difficult to achieve continuous and real-time monitoring of roadbed compaction quality, which to some extent limits the real-time feedback and adjustment of construction quality. Vibration compaction technology has been widely used in the field of highway engineering due to its high efficiency and speed. The compaction degree is directly related to the durability and service life of the highway; therefore, accurate and efficient detection of compaction degree is crucial. This article proposes a method for detecting roadbed compaction degree by integrating machine learning (ML) and vibration acceleration signals. This method aims to achieve accurate evaluation of roadbed compaction by real-time monitoring and analysis of vibration acceleration data, combined with the powerful prediction and classification capabilities of ML algorithms. The experimental results show that this method not only improves the detection efficiency, but also significantly enhances the accuracy of compaction degree detection.