Enhancing Quality Control of Chip Seal Construction through Machine Learning-Based Analysis of Surface Macrotexture Metrics

Abstract

Efforts to enhance quality control (QC) practices in chip seal construction have predominantly relied on single surface friction 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, does not always result in slippery conditions and may lead to uneven surfaces. The correlation between higher MPD or friction number and superior chip seal quality is not straightforward. This research introduces an innovative machine learning-based approach to enhance chip seal QC. Using a hybrid DBSCAN-Isolation Forest model, anomaly detection was conducted on a dataset comprising 183,794 20 m MPD measurements from actual chip seal projects across six districts in Indiana. This resulted in typical 20 m segment MPD ranges of [0.9 mm, 1.9 mm], [0.6 mm, 2.1 mm], [0.3 mm, 1.3 mm], [1.0 mm, 1.7 mm], [0.6 mm, 1.9 mm], and [1.0 mm, 2.3 mm] for the respective six districts in Indiana. A two-step QC procedure tailored for chip seal evaluation was proposed. The first step calculated outlier percentages across 1-mile segments, with an established limit of 25% outlier segments per wheel track. The second step assessed unqualified rates across projects, setting a threshold of 50% for unqualified 1-mile wheel track segments. Through validation analysis of four chip seal projects, both field inspection and friction measurements closely aligned with the proposed methodology’s results. The methodology presented establishes a foundational QC standard for chip seal projects, enhancing both acceptance efficiency and safety by using a quantitative method and minimizing the extended presence of practitioners on roadways.