Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Municipal Street Pavement Management Systems in Sweden
Street pavements are subject to various types of distress which necessitate a cost-effective management approach. This paper presents the outcomes of a survey focusing on street pavement maintenance and the utilization of machine learning (ML) pavement performance models on a 320 km municipal street network in Skellefteå municipality, Sweden. The findings reveal that the most common types of distress on Swedish streets include potholes, surface unevenness and alligator cracking, while prevalent causes of these distress are pavement ageing, heavy traffic and pavement patches. The windshield method of assessment of street pavement is prevalent, but the use of pavement management systems (PMS) is limited and pavement performance models are rarely employed. The case study reveals that Random Forest (RF) models developed for non-residential streets perform better than residential street models. RF models based on the variables age (A) and traffic (T) emerged as the best models, with 84% prediction accuracy. However, the R-squared value for the RF model applied to residential streets was 0.53, slightly surpassing the values for all models applied to non-residential streets (0.31, 0.50, 0.49). Further evaluation of models is suggested by using additional data.
Municipal Street Pavement Management Systems in Sweden
Street pavements are subject to various types of distress which necessitate a cost-effective management approach. This paper presents the outcomes of a survey focusing on street pavement maintenance and the utilization of machine learning (ML) pavement performance models on a 320 km municipal street network in Skellefteå municipality, Sweden. The findings reveal that the most common types of distress on Swedish streets include potholes, surface unevenness and alligator cracking, while prevalent causes of these distress are pavement ageing, heavy traffic and pavement patches. The windshield method of assessment of street pavement is prevalent, but the use of pavement management systems (PMS) is limited and pavement performance models are rarely employed. The case study reveals that Random Forest (RF) models developed for non-residential streets perform better than residential street models. RF models based on the variables age (A) and traffic (T) emerged as the best models, with 84% prediction accuracy. However, the R-squared value for the RF model applied to residential streets was 0.53, slightly surpassing the values for all models applied to non-residential streets (0.31, 0.50, 0.49). Further evaluation of models is suggested by using additional data.
Municipal Street Pavement Management Systems in Sweden
Lecture Notes in Civil Engineering
Pereira, Paulo (Herausgeber:in) / Pais, Jorge (Herausgeber:in) / Afridi, Muhammad Amjad (Autor:in) / Erlingsson, Sigurdur (Autor:in) / Sjögren, Leif (Autor:in)
International Conference on Maintenance and Rehabilitation of Pavements ; 2024 ; Guimarães, Portugal
Proceedings of the 10th International Conference on Maintenance and Rehabilitation of Pavements ; Kapitel: 42 ; 437-446
21.07.2024
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Municipal street maintenance challenges and management practices in Sweden
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