Development of automated condition assessment system for concrete bridge decks using airborne and ground infrared thermography
Development of automated condition assessment system for concrete bridge decks using airborne and ground infrared thermography
批准号:
491556-2015
负责人:
Nehdi, Moncef
金额:
$1.78万
依托单位国家:
加拿大
项目类别:
Engage Grants Program
财政年份:
2015
资助国家:
加拿大
项目状态:
已结题
起止时间:
2015-01-01 至 2016-12-31
中文摘要
拟议的研究将调查使用机载和地面红外热成像(IR)检测混凝土桥面的表面下缺陷。将使用具有足够空间分辨率和热灵敏度的先进高光谱和热成像相机对桥面进行空中成像调查。地面红外线将使用车载红外线摄像机在相同的桥面上进行。定期的视觉数据将与红外数据同步收集,以便可以在红外图像中区分和映射染色和修补等表面特征。在实地数据收集之后,大学团队将使用数据挖掘方法将数据标准化,并使用区域生长技术开发自动图像分割算法,以重建高对比度合成图像
精确地描绘表面下的缺陷。这将导致量化和映射桥面退化。在同一桥面上进行的探地雷达(GPR)测量,将在每个桥面的宽度上横向间隔3英尺的一系列线上进行。GPR数据将由大学研究人员分析和绘制,以检查IRT结果。随后,将开发一个基于IRT数据的桥面状况指数,利用人工神经网络技术的无监督数据聚类。此后将开发一个自动化软件,以便利拟议系统的实施。据信,结合桥面的IR和GPR调查将使行业合作伙伴处于该领域的技术前沿,提高其竞争力和盈利能力。
英文摘要
The proposed research will investigate using airborne and ground infrared thermography (IR) to detect subsurface defects in concrete bridge decks. Airborne imaging surveys of bridge decks will be conducted using an advanced hyperspectral and thermal imaging camera with adequate spatial resolution and thermal sensitivity. Ground infrared will be carried out on the same bridge decks using a vehicle mounted infrared camera. Regular visual data will be collected synchronously with the infrared data so that surface features such as staining and patching can be differentiated and mapped in the infrared images. Following field data collection, the University team will normalise the data using data mining approaches and develop an automated image segmentation algorithm using a region growing technique to reconstruct a high contrast composite image
accurately delineating subsurface defects. This will result in quantifying and mapping bridge deck deterioration. Ground penetrating radar (GPR) surveys on the same bridge decks will be executed in a series of lines spaced 3 feet transversely across the width of each deck. The GPR data will be analyzed and mapped by the University researchers to examine the IRT results. Subsequently, a bridge deck condition index based on the IRT data will be developed utilizing unsupervised data clustering of the artificial neural networks technique. An automated software will be developed thereafter to facilitate the implementation of the proposed system. It is believed that coupled IR and GPR surveys of bridge decks will position the industry partner on the cutting edge of technology in this field, enhancing its competitiveness and profitability.
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