A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Machine learning helps reveal key factors affecting tire wear particulate matter emissions
Tire wear particles (TWPs) are generated with every rotation of the tire. However, obtaining TWPs under real driving conditions and revealing key factors affecting TWPs are challenging. In this study, we obtained a TWPs dataset by simulating tire wear process under real driving conditions using a tire wear simulator and custom-designed test conditions. This study shows that tire wear PM2.5 accounts for about 65 % of PM10. The response relationship between TWP emissions (both PM2.5 and PM2.5-10) and factors (the radial force, the lateral force, the tangential force, speed, driving torque, tire contact area, total contour length and tire tread temperature) was obtained by machine learning (ML) method. The random forest (RF) model was developed and displayed good prediction performance with an R2 of 0.84 and 0.78 for PM2.5 and PM2.5-10 on the test set, respectively. Model-related (similarity network graph) and model-unrelated (partial dependence plots and centered-individual conditional expectation plots) explainability methods were used to break the black box of ML. Model explainability results show that the feature parameters-emission response relationships for tire wear PM2.5 and PM2.5-10 are different. Avoiding strenuous driving behaviors (TTF < 400 N, TLF < 400 N), reducing tread temperature (T < 45℃), and minimizing the number of small tread patterns are feasible ways to reduce TWPs.
Machine learning helps reveal key factors affecting tire wear particulate matter emissions
Tire wear particles (TWPs) are generated with every rotation of the tire. However, obtaining TWPs under real driving conditions and revealing key factors affecting TWPs are challenging. In this study, we obtained a TWPs dataset by simulating tire wear process under real driving conditions using a tire wear simulator and custom-designed test conditions. This study shows that tire wear PM2.5 accounts for about 65 % of PM10. The response relationship between TWP emissions (both PM2.5 and PM2.5-10) and factors (the radial force, the lateral force, the tangential force, speed, driving torque, tire contact area, total contour length and tire tread temperature) was obtained by machine learning (ML) method. The random forest (RF) model was developed and displayed good prediction performance with an R2 of 0.84 and 0.78 for PM2.5 and PM2.5-10 on the test set, respectively. Model-related (similarity network graph) and model-unrelated (partial dependence plots and centered-individual conditional expectation plots) explainability methods were used to break the black box of ML. Model explainability results show that the feature parameters-emission response relationships for tire wear PM2.5 and PM2.5-10 are different. Avoiding strenuous driving behaviors (TTF < 400 N, TLF < 400 N), reducing tread temperature (T < 45℃), and minimizing the number of small tread patterns are feasible ways to reduce TWPs.
Machine learning helps reveal key factors affecting tire wear particulate matter emissions
Zhenyu Jia (author) / Jiawei Yin (author) / Tiange Fang (author) / Zhiwen Jiang (author) / Chongzhi Zhong (author) / Zeping Cao (author) / Lin Wu (author) / Ning Wei (author) / Zhengyu Men (author) / Lei Yang (author)
2025
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Machine learning helps reveal key factors affecting tire wear particulate matter emissions
| Elsevier | 2025
Particulate Emissions from Combustion of a Coal+Tire Blend
| British Library Conference Proceedings | 2005
Factors affecting wear of ceramic balls
| British Library Online Contents | 1994