A novel neural network-based asphalt compaction analyzer
Abstract:Achieving the desired density during field compaction of asphalt mixes is critical to meeting the design specifications of an asphalt pavement. Existing techniques measure the density of asphalt mixes at a discrete number of points. As such, the process is cumbersome, time consuming, and is not indicative of the overall compaction achieved unless large amounts of data is collected and analyzed. In this paper, the concept of a novel neural network-based asphalt compaction analyzer capable of predicting the density continuously, in real time, during the construction of the pavement is presented. The concept is verified using laboratory data from an asphalt vibratory compactor (AVC). The compaction analyzer is based on the hypothesis that a vibratory compactor and the hot mix asphalt (HMA) mat form a coupled system having unique vibration properties. The measured vibrations of the compactor along with the process parameters such as lift thickness, mix type, mix temperature, and compaction pressure can be used to predict the density of the asphalt mat. Vibration data obtained during compaction of asphalt mixes in the laboratory is used to design and train the neural network (NN). The trained NN is then used to continuously predict the degree of compaction in real time. The proposed approach is validated through compaction studies in the laboratory. Preliminary field studies demonstrate the capability of the analyzer in predicting the density of an asphalt pavement during construction.
Document Type: Research Article
Publication date: June 1, 2008