Study of retrofitted system for Intelligent Compaction Analyzer, a machine learning approach for Quality Control of Asphalt Pavement during Construction
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Authors
Poudel, Shankar
Issue Date
2023
Type
Thesis
Language
Keywords
compaction quality , density estimation , Intelligent asphalt compaction analyzer , machine learning , nondestructive testing , quality assurance
Alternative Title
Abstract
Asphalt pavements play a vital role in transportation infrastructure, but their performance can suffer due to subpar quality resulting from improper construction practices. To tackle this issue, we introduce the Retrofit Intelligent Compaction Analyzer (RICA), a real-time compaction density estimation system for asphalt pavements during construction. RICA utilizes machine learning principles and machine learning to predict compaction density based on received vibratory patterns at different compaction levels. By leveraging the roller's spatial location and analyzing vibration patterns, RICA delivers density estimates.In this study, we gathered data from actual construction sites, implementing RICA on a Caterpillar CB-10 Rotary dialed dual drum vibratory compactor. The density estimates from RICA were validated against densities measured from roadway cores extracted randomly on the compacted pavement. Our findings affirm the efficacy of RICA in providing reliable density estimates for asphalt pavements.The ability of RICA to provide real-time, nondestructive compaction information to the roller operator establishes its value as a quality control tool during asphalt pavement construction. By ensuring proper compaction, RICA contributes to the construction of durable, high-quality roads while reducing the financial and environmental costs associated with construction and maintenance. The validation of RICA's estimates with percent within limits (PWL) calculations based on roadway cores further attests to its effectiveness as a Quality Assurance tool.