Suppr超能文献

自感应沥青路面的发展:综述与展望

Development of Self-Sensing Asphalt Pavements: Review and Perspectives.

作者信息

Gulisano Federico, Jimenez-Bermejo David, Castano-Solís Sandra, Sánchez Diez Luis Alberto, Gallego Juan

机构信息

Departamento de Ingeniería del Transporte, Territorio y Urbanismo, Universidad Politécnica de Madrid, C/Profesor Aranguren 3, 28040 Madrid, Spain.

Information Processing and Telecommunication Center (IPTC-GATV), Universidad Politécnica de Madrid, 28040 Madrid, Spain.

出版信息

Sensors (Basel). 2024 Jan 25;24(3):792. doi: 10.3390/s24030792.

Abstract

The digitalization of the road transport sector necessitates the exploration of new sensing technologies that are cost-effective, high-performing, and durable. Traditional sensing systems suffer from limitations, including incompatibility with asphalt mixtures and low durability. To address these challenges, the development of self-sensing asphalt pavements has emerged as a promising solution. These pavements are composed of stimuli-responsive materials capable of exhibiting changes in their electrical properties in response to external stimuli such as strain, damage, temperature, and humidity. Self-sensing asphalt pavements have numerous applications, including in relation to structural health monitoring (SHM), traffic monitoring, Digital Twins (DT), and Vehicle-to-Infrastructure Communication (V2I) tools. This paper serves as a foundation for the advancement of self-sensing asphalt pavements by providing a comprehensive review of the underlying principles, the composition of asphalt-based self-sensing materials, laboratory assessment techniques, and the full-scale implementation of this innovative technology.

摘要

道路运输部门的数字化需要探索具有成本效益、高性能且耐用的新型传感技术。传统传感系统存在局限性,包括与沥青混合料不兼容以及耐久性低。为应对这些挑战,自感应沥青路面的开发已成为一种有前景的解决方案。这些路面由能够响应诸如应变、损伤、温度和湿度等外部刺激而表现出电学性能变化的刺激响应材料组成。自感应沥青路面有许多应用,包括与结构健康监测(SHM)、交通监测、数字孪生(DT)以及车路通信(V2I)工具相关的应用。本文通过全面综述其基本原理、沥青基自感应材料的组成、实验室评估技术以及这项创新技术的全面实施,为自感应沥青路面的发展奠定了基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb44/10856935/8e23e339898c/sensors-24-00792-g006.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验