Evaluation and Prediction of Pavement Deflection Parameters Based on Machine Learning Methods

The deflection measurements made using Falling Weight Deflectometers (FWDs) are widely used in the back-calculation of pavement layer moduli. Pavement structural characteristics, changes in temperature, and other related factors exert a significant effect on the deflection measurements. Therefore, three machine learning methods—Classification and Regression Tree (CART), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT)—were used to evaluate the importance of influencing factors including FWD test conditions, pavement structural parameters, climatic factors, traffic level, rehabilitation level, and service age, on the FWD measurements of deflection basin in this study. The results indicated that structural number was an important feature for all FWD measurements but its importance on lg(D0–D20) and lg(D0–D30) was smaller than other FWD measurements. The relative feature importance of the asphalt layer, base, and subbase on lg(D0–D20) and lg(D0–D30) was asphalt layer > subbase > base; their relative importance on lg(D20–D60), lg(D30–D60), and lg(D30–D90) was asphalt layer > base > subbase; and their relative importance on lg(D90−D120) and lg(D60–D120) was base > subbase > asphalt layer. Among the FWD test condition variables, drop load was the most significant factor influencing deflection measurements. The second-layer temperature was also important for lg(D0–D20), lg(D0–D30), and lg(D0–D45). The importance of precipitation was greater than the freeze index. The prediction results shown that the accuracy of GBDT was as high as 99%. Besides, GBDT outperformed RF, and RF outperformed CART. The analyses between FWD deflection parameters and influencing factors, especially the structural characteristics of the pavement, provide theoretical evidence for the evaluation of pavement layer strength on the basis of FWD data.

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