I-Corps: Automated pavement condition survey system to improve cost-effectiveness

I-Corps：自动化路面状况调查系统，以提高成本效益

• 批准号: 2051192
• 负责人: Feng Wang
• 金额: $ 5万
• 依托单位: Texas State University - San Marcos
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-15 至 2023-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2051192&HistoricalAwards=false
• 关键词: Corps; Automated; pavement; condition; survey

The broader impact/commercial potential of this I-Corps project is the development of a more cost-effective, automated, pavement condition data collection system. In a pavement management system (PMS), accurate, precise, and reliable pavement condition data will assist pavement engineers to make sound maintenance decisions. While a manual data collection method is labor intensive, time consuming, and frequently impossible or unsafe to perform, the automated data collection methods are receiving more attention in industry and replacing manual surveys in recent years. There are problems with inaccuracy with the existing automated pavement data collection methods. The collection technology proposed in this project is designed to improve the data quality of automated pavement data collection at a sustainable cost. A successful application of the proposed technology may help maintain transportation infrastructure and the technology may be extended to other infrastructure systems such as airport runways, railroads, bridges, and dams.This I-Corps project is based on the development of a more cost-effective, automated, pavement condition data collection system. To overcome the inaccuracy problems associated with existing automated pavement data collection methods, the proposed technology is expected to use more sophisticated and smarter image processing algorithms. In addition, the proposed system seeks improvements in data quality assurance by using data analysis capabilities to screen and validate the data, locating problematic data and reducing errors in the data. The core components of this system are: 1) a cost-effective 3D image acquisition hardware system; 2) artificial intelligence-enhanced pavement image processing algorithms; and 3) systemic design and optimization-based data quality assurance. The design utilizes sensor technologies, statistical theories, 3D reconstruction models, deep learning algorithms, and synthetic ground truth data functionality in the development of this technology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该I-Corps项目的更广泛的影响/商业潜力是开发更具成本效益，自动化，路面条件数据收集系统的开发。在路面管理系统（PMS）中，准确，精确且可靠的路面条件数据将有助于路面工程师做出合理的维护决策。尽管手动数据收集方法是劳动密集型，耗时，并且经常不可能或不安全无法执行，但自动数据收集方法在近年来会在行业中受到更多关注并替换手动调查。现有的自动路面数据收集方法存在不准确的问题。该项目提出的收集技术旨在以可持续的成本提高自动路面数据收集的数据质量。提出的技术的成功应用可能有助于维持运输基础设施，并且该技术可能会扩展到其他基础设施系统，例如机场跑道，铁路，桥梁和大坝。这项I-Corps项目基于开发更具成本效益，自动化，自动化，分行状况数据收集系统的开发。为了克服与现有自动化数据收集方法相关的不准确问题，预计该提议的技术将使用更复杂和更智能的图像处理算法。 此外，提出的系统通过使用数据分析功能来筛选和验证数据，定位有问题的数据并减少数据中的错误，从而寻求数据质量保证的改善。该系统的核心组件是：1）成本效益的3D图像采集硬件系统； 2）人工智能增强的路面图像处理算法； 3）基于系统设计和基于优化的数据质量保证。 该设计利用传感器技术，统计理论，3D重建模型，深度学习算法和合成基础真相数据功能在这项技术的开发中。该奖项反映了NSF的法定任务，并被视为值得通过基金会的知识分子和更广泛的影响审查标准来通过评估来通过评估来获得支持。

项目成果

Improving Data Quality of Automated Pavement Condition Data Collection: Summary of State of the Practices of Transportation Agencies and Views of Professionals

提高自动路面状况数据采集的数据质量：运输机构实践状况和专业人士观点的总结

• DOI: 10.1061/jpeodx.0000392
• 发表时间: 2022
• 期刊: Part B: Pavements
• 作者: Luo, Xiaohua;Gong, Haitao;Tao, Jueqiang;Wang, Feng;Minifie, Jana;Qiu, Xin
• 通讯作者: Qiu, Xin

Deep learning based automated segmentation of air-void system in hardened concrete surface using three dimensional reconstructed images

• DOI: 10.1016/j.conbuildmat.2022.126717
• 发表时间: 2022-02-04
• 期刊: CONSTRUCTION AND BUILDING MATERIALS
• 影响因子: 7.4
• 作者: Tao, Jueqiang;Gong, Haitao;Liu, Jinli
• 通讯作者: Liu, Jinli