Use of random forests regression for predicting IRI of asphalt pavements
By Hongren Gong, Yiren Sun, X. Shu, Baoshan Huang. Construction and Building Materials, 2018
294 citations recorded by the source corpus.
Abstract Random forest is a powerful machine learning algorithm with demonstrated success. In this study, the authors developed a random forests regression (RFR) model to estimate the international roughness index (IRI) of flexible pavements from distress measurements, traffic, climatic, maintenance and structural data. To validate the model, more than 11,000 samples were collected from the database of long-term pavement performance (LTPP) program, with 80% randomly sampled data for training and 20% of them for testing the RFR model. The performance of the RFR model was then compared with that of the regularized linear regression model. The results showed that the RFR model significantly outperformed the linear regression model, with coefficients of determination ( R 2 ) greater than 0.95 in both the training and test sets. The variable importance score obtained from the RFR revealed that the initial IRI was the most important factor affecting the development of the IRI. In addition, the transverse cracking, fatigue cracking, rutting, annual average precipitation and service age had important influences on the IRI. Other distresses such as longitudinal cracking, edge cracking, aggregate polishing, and potholes exerted little impact on the evolution of the IRI.
Subfields: Civil and Structural Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/3c1a43c2a50dd550c4516ecbd182a455dc95765d
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In Civil and Structural Engineering
In Mechanical Engineering
Citations in the corpus
- Estimation of the dynamic modulus of asphalt concretes using random forests algorithm (2020, methodology): “In this regard, it has been proven that the use of ML is an effective technique to derive prediction models in many civil engineering applications including the performance of asphalt mixtures (Ozsahin and Oruc 2008, Behnood et al. 2015, Gong et al. 2018, Fathi et al. 2019, Majidifard et al. 2019).”