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   https://doi.org/10.1111/j.1931-0846.2016.12213.x  

   Buckley, Geoffrey L.; Boone, Christopher G.; Morgan Grove, J. (July 2017, Geographical Review)
2. Primary and Secondary Organic Aerosol Formation from Asphalt Pavements

   https://doi.org/10.1021/acs.est.3c06037  

   Humes, Mackenzie B.; Machesky, Jo E.; Kim, Sunhye; Oladeji, Oladayo J.; Gentner, Drew R.; Donahue, Neil M.; Presto, Albert A. (November 2023, Environmental Science & Technology)
3. Deep transfer learning-based vehicle classification by asphalt pavement vibration

   https://doi.org/10.1016/j.conbuildmat.2022.127997  

   Liu, Fangyu; Ye, Zhoujing; Wang, Linbing (August 2022, Construction and Building Materials)

   NA (Ed.)

   Deep transfer learning (TL) has great potential for a wide range of applications in civil engineering. This work aims to propose a deep transfer learning-based method for vehicle classification by asphalt pavement vibration. This work first used the pavement vibration IoT monitoring system to collect raw vibration signals and performed the wavelet transform to obtain denoised vibration signals. The vibration signals were then represented in two different ways, including the time-domain graph and the time-frequency graph. Finally, two deep transfer learning-based methods, namely Method Ⅰ (Time-domain & TL) and Method Ⅱ (Time-frequency & TL), were applied for vehicle classification according to the two different representations of vibration signals. The results show that the CNN model had a satisfactory performance in both methods with the accuracy of Method Ⅰ exceeding 0.94 and Method Ⅱ exceeding 0.95. The CNN model in Method Ⅱ performed better in the accuracy metrics with considering label imbalance, but worse in the accuracy metrics without considering label imbalance than that in Method Ⅰ. The differences between these two methods have been investigated and discussed in detail in terms of input types, accuracy metrics, and application prospects. The CNN model with deep transfer learning could be an effective, accurate, and reliable technique for vehicle classification based on asphalt pavement vibration. 

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4. Study of wind loads on asphalt shingles using full-scale experimentation

   https://doi.org/10.1016/j.jweia.2022.105005  

   Tolera, Ameyu B.; Mostafa, Karim; Chowdhury, Arindam Gan; Zisis, Ioannis; Irwin, Peter (June 2022, Journal of Wind Engineering and Industrial Aerodynamics)
5. Anthropogenic secondary organic aerosol and ozone production from asphalt-related emissions

   https://doi.org/10.1039/D3EA00066D  

   Seltzer, Karl M.; Rao, Venkatesh; Pye, Havala O.; Murphy, Benjamin N.; Place, Bryan K.; Khare, Peeyush; Gentner, Drew R.; Allen, Christine; Cooley, David; Mason, Rich; et al (July 2023, Environmental Science: Atmospheres)

   Liquid asphalt is a petroleum-derived substance commonly used in construction activities. Recent work has identified lower volatility, reactive organic carbon from asphalt as an overlooked source of secondary organic aerosol (SOA) precursor emissions. Here, we leverage potential emission estimates and usage data to construct a bottom-up inventory of asphalt-related emissions in the United States. In 2018, we estimate that hot-mix, warm-mix, emulsified, cutback, and roofing asphalt generated ∼380 Gg (317 Gg–447 Gg) of organic compound emissions. The impacts of these emissions on anthropogenic SOA and ozone throughout the contiguous United States are estimated using photochemical modeling. In several major cities, asphalt-related emissions can increase modeled summertime SOA, on average, by 0.1–0.2 μg m−3 (2–4% of SOA) and may reach up to 0.5 μg m−3 at noontime on select days. The influence of asphalt-related emissions on modeled ozone are generally small (∼0.1 ppb). We estimate that asphalt paving-related emissions are half of what they were nearly 50 years ago, largely due to the concerted efforts to reduce emissions from cutback asphalts. If on-road mobile emissions continue their multidecadal decline, contributions of urban SOA from evaporative and non-road mobile sources will continue to grow in relative importance. 

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