Forecasting pavement performance condition is essential within the pavement management system to optimize decisions with regard to planning maintenance and rehabilitation projects. Accurate forecasts facilitate timely interventions and assist in formulating cost-effective asset management plans. Data-driven machine learning models that utilize historical data to improve forecasting precision have gained attention in the field of asset management. Although numerous studies have employed regression-based models to forecast pavement condition, transportation asset management often operates according to condition index ranges rather than exact values. Therefore, classification models are suitable for predicting pavement condition grades and determining the appropriate maintenance type for pavement assets. This research focuses on developing five machine learning classification models to predict pavement condition: random forest; gradient boost; support vector machine; k-nearest neighbors; and artificial neural network. To enhance prediction performance, these models are integrated using ensemble methods, including voting and stacking. The classification models are developed using a dataset from the Georgia Department of Transportation that documented the condition of asphalt pavements for predefined maintenance sections between 2017 and 2021. A voting ensemble model constructed with the two best-performing individual classification models reached the highest accuracy rate at 83%. Although the performance of individual models fluctuates, ensemble models consistently produce a top-tier performance regardless of the variations in data sampling. Therefore, ensemble methods are recommended for developing pavement condition prediction models to improve accuracy and achieve a more consistent quality of predictions. The findings of this research will provide transportation agencies with information to help them strengthen their forecasting practices in relation to pavement condition, thereby improving their maintenance planning and cost savings.


    Zugriff

    Download

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Ensemble Machine Learning Classification Models for Predicting Pavement Condition


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:
    Chung, Frederick (Autor:in) / Doyle, Andy (Autor:in) / Robinson, Ernay (Autor:in) / Paik, Yejee (Autor:in) / Li, Mingshu (Autor:in) / Baek, Minsoo (Autor:in) / Moore, Brian (Autor:in) / Ashuri, Baabak (Autor:in)


    Erscheinungsdatum :

    05.05.2024




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Assessment of Present Pavement Condition Using Machine Learning Techniques

    Sharma, Madhavendra / Kumar, Pradeep | TIBKAT | 2022



    Hybrid Transfer Learning and Support Vector Machine Models for Asphalt Pavement Distress Classification

    Apeagyei, Alex / Ademolake, Toyosi Elijah / Anochie-Boateng, Joseph | Transportation Research Record | 2024


    Predicting the Porosity of SCM-Blended Concrete Composites Using Ensemble Machine Learning Models

    Ansari, Saad Shamim / Farid, Sayed Ali / Abdullah, Syed Ahmad et al. | Springer Verlag | 2024