Bao, J.; Adcock, J.; Li, S.; Jiang, Y. Enhancing Quality Control of Chip Seal Construction through Machine Learning-Based Analysis of Surface Macrotexture Metrics. Lubricants2023, 11, 409.
Bao, J.; Adcock, J.; Li, S.; Jiang, Y. Enhancing Quality Control of Chip Seal Construction through Machine Learning-Based Analysis of Surface Macrotexture Metrics. Lubricants 2023, 11, 409.
Bao, J.; Adcock, J.; Li, S.; Jiang, Y. Enhancing Quality Control of Chip Seal Construction through Machine Learning-Based Analysis of Surface Macrotexture Metrics. Lubricants2023, 11, 409.
Bao, J.; Adcock, J.; Li, S.; Jiang, Y. Enhancing Quality Control of Chip Seal Construction through Machine Learning-Based Analysis of Surface Macrotexture Metrics. Lubricants 2023, 11, 409.
Abstract
Efforts to enhance quality control (QC) practices in chip seal construction have predominantly relied on single surface frictional metrics such as mean profile depth (MPD) or friction number. These metrics assess chip seal quality by targeting issues such as aggregate loss or excessive bleeding, which may yield low friction numbers or texture depths. However, aggregate loss particularly due to snowplow operations doesn't always result in slippery conditions and may lead to uneven surfaces. The correlation between higher MPD or friction number and superior chip seal quality isn't straightforward. This research introduces an innovative machine learning-based approach to enhance chip seal QC. Using a hybrid DBSCAN-Isolation Forest model, anomaly detection is conducted on a dataset comprising 183,794 20-meter MPD measurements from actual chip seal projects across six districts in Indiana. This results in typical 20 m-segment MPD ranges of [0.9, 1.9], [0.6, 2.1], [0.3, 1.3], [1.0, 1.7], [0.6, 1.9], and [1.0, 2.3] for the respective six districts in Indiana. A two-step QC procedure tailored for chip seal evaluation is proposed. The first step calculates outlier percentages across 1-mile segments, with an established limit of 25% outlier segments per wheel track. The second step assesses unqualified rates across projects, setting a threshold of 50% for 1-mile unqualified wheel track segments. While the results are data-specific, this framework offers pavement construction practitioners a foundational QC standard for chip seal projects.
Keywords
chip seal; quality control; quality acceptance; macro-texture; mean segment depth; mean profile depth; machine learning; DBSCAN-Isolation Forest; proportion control chart; anomaly detection
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
