期刊
INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING
卷 22, 期 3, 页码 341-354出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/10298436.2019.1609673
关键词
Machine learning; pavement performance models; pavement management systems (PMS); time series forecasts; international roughness index (IRI); predictive maintenance
类别
资金
- Fundacao para a Ciencia e a Tecnologia [SFRH/BD/129907/2017]
- Fundação para a Ciência e a Tecnologia [SFRH/BD/129907/2017] Funding Source: FCT
This paper proposes a general machine learning approach for the development of pavement performance prediction models in pavement management systems, supporting different machine learning algorithms and emphasizing generalization performance.
In recent years, there has been an increasing interest in the application of machine learning for the prediction of pavement performance. Prediction models are used to predict the future pavement condition, helping to optimally allocate maintenance and rehabilitation funds. However, few studies have proposed a systematic approach to the development of machine learning models for pavement performance prediction. Most of the studies focus on artificial neural networks models that are trained for high accuracy, disregarding other suitable machine learning algorithms and neglecting the importance of models' generalisation capability for Pavement Engineering applications. This paper proposes a general machine learning approach for the development of pavement performance prediction models in pavement management systems (PMS). The proposed approach supports different machine learning algorithms and emphasizes generalisation performance. A case study for prediction of International Roughness Index (IRI) for 5 and 10-years, using the Long-Term Pavement Performance, is presented. The proposed models were based on a random forest algorithm, using datasets comprising previous IRI measurements, structural, climatic, and traffic data.
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