Nima Kargah-Ostadi, K. Vasylevskyi, A. Ablets, A. Drach
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引用次数: 0
Abstract
Physics-informed neural networks (PINN) are machine learning (ML) algorithms that can bridge the gap between our understanding of physical phenomena and the corresponding empirical observations. Th...
期刊介绍：
The international journal Road Materials and Pavement Design welcomes contributions on mechanical, thermal, chemical and/or physical properties and characteristics of bitumens, additives, bituminous mixes, asphalt concrete, cement concrete, unbound granular materials, soils, geo-composites, new and innovative materials, as well as mix design, soil stabilization, and environmental aspects of handling and re-use of road materials.
The Journal also intends to offer a platform for the publication of research of immediate interest regarding design and modeling of pavement behavior and performance, structural evaluation, stress, strain and thermal characterization and/or calculation, vehicle/road interaction, climatic effects and numerical and analytical modeling. The different layers of the road, including the soil, are considered. Emerging topics, such as new sensing methods, machine learning, smart materials and smart city pavement infrastructure are also encouraged.
Contributions in the areas of airfield pavements and rail track infrastructures as well as new emerging modes of surface transportation are also welcome.
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