Neural Network–Based Intelligent Compaction Analyzer for Estimating Compaction Quality of Hot Asphalt Mixes

Abstract

The development and validation of a tool that can estimate the level of compaction of a Hot Mix Asphalt (HMA) pavement during its construction is addressed in this paper. Densification of asphalt pavements during their construction is usually accomplished through the use of vibratory compactors. During compaction, the compactor and the asphalt mat form a coupled system whose dynamics are influenced by the changing stiffness of the mat. In this paper, it is shown that 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. Contrary to existing techniques in the literature where a model is developed to fit the experimental data and to predict the density of the mat, a novel neural network based approach is adopted that is model-free and uses pattern-recognition techniques to estimate the density. During compaction of a HMA mat, the neural network then classifies the observed vibrations as those corresponding to a known level of compaction. The results also show that the analyzer can estimate the density continuously, and in realtime with accuracy levels adequate for quality control in the field. Using this tool, for the first time, the overall quality of construction of a HMA pavement can be verified thereby creating the potential to improve the quality of the roads.

Comprehensive Evaluation of Compaction of Asphalt Pavements and a Practical Approach for Density Predictions

“…It is believed that the thin asphalt overlays being compacted could hinder the IC roller from using its intelligent features of applying more compactive effort when needed (9). Commuri et al developed a neural network-based intelligent asphalt compaction analyzer (IACA) to estimate the level of compaction in the field by using a steel vibratory compactor (10). Commuri et al reported that density measured with the IACA correlated well with the field cores' densities (10).…”

Development of compaction monitoring system for asphalt pavements

“…Other studies confirmed that the stiffness of the underlying layer has a great impact on the IC stiffness measurements of the asphalt layer being compacted [11]. Commuri et al [12] developed a neural network-based Intelligent Asphalt Compaction Analyzer (IACA) to estimate the compaction quality of asphalt pavements. The results showed that density measured using the IACA method correlated well with the density in the field.…”

“…Recently, Intelligent Compaction (IC) technology was used to monitor the field compaction of asphalt pavements in real time [10][11][12]. There are several IC systems that are used and commercially available [13][14][15][16][17][18].…”

Finite element modelling of field compaction of hot mix asphalt. Part II: Applications

“…The stiffness measured using the IC roller is influenced by the stiffness of the materials underneath (Asphalt Institute 2010). Commuri et al (2011) developed a neural network-based intelligent asphalt compaction analyser (IACA) to quantify the compaction of asphalt mixtures in the field. They reported good correlation between density measured using the IACA and density in the field.…”