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Development of Pavement Condition Index Modelling Using Machine Learning Techniques
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PRE
AI
DOI：10.5269/bspm.80219.png)
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Abstract
En 中文
The Pavement performance modelling is critical for sustainable transportation infrastructure, since deterioration caused by traffic loads, weather conditions, and structural distress has a direct impact on serviceability and maintenance costs. This research compared two prediction approaches for predicting Pavement Condition Index (PCI): Artificial Neural Network (ANN) and Random Forest (RF) using key deterioration variables: patches, potholes, temperature, depressions and cumulative Equivalent Single Axle Loads (ESAL). The model demonstrated strong predictive performance, with RF achieving R2 = 0.916 (RMSE = 3.42), and ANN attaining the highest precision with R2 = 0.961 (RMSE = 2.34). Sensitivity analysis revealed that temperature, traffic loading, and potholes were the most important indicators, with patching and depressions having little significance. The ANN model improved better predictive capability and RF balanced accuracy with shifting importance. Collectively, technologies enhance improve data-driven pavement management by allowing for accurate PCI forecasting and permitting proactive, cost-effective maintenance planning for resilient transportation networks.
Keywords:
Artificial neural network
random forest algorithm
pavement condition index
Journal
B
IF:
0.4
Papers:
604
Citations:
0
