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Asphalt Pavement Performance Prediction Using Ensemble Learning Methods
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DOI：10.1007/978-3-032-04774-8_23.png)
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Abstract
En 中文
Ensuring road quality is crucial for the safety and comfort of all road users. The International Roughness Index (IRI) is a fundamental index for assessing the performance of flexible pavements. However, many conventional methods to gauge this index often fall short in accuracy and reliability. To address this, the present research harnessed ensemble machine learning methods, incorporating data from 55 asphalt pavement sections from the Long-Term Pavement Performance InfoPave database. The models were trained using pavement structure, age, traffic, and climatic factors to predict IRI values. Results indicate that ensemble learning methods effectively predict pavement performance, recording R-2 values ranging from 0.82 to 0.87. Notably, the initial IRI emerged as the most significant predictor. Moreover, models showed heightened precision for pavements displaying IRI values under 2.0 m/km, indicating their aptness for early-stage flexible road pavements. These findings underscore the potential to refine these models further and the necessity for more diversified datasets.
Keywords:
Pavement Quality
Performance Prediction
Ensemble Learning
International Roughness Index
Long-Term Pavement Program
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