Code-specified early delamination detection and quantification in a RC bridge deck: passive vs. active infrared thermography

IF 3.6 2区 工程技术 Q1 ENGINEERING, CIVIL
Haibin Zhang, Zhenhua Shi, Liujun Li, Pu Jiao, Bo Shang, Genda Chen
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Abstract

Delamination in reinforced concrete (RC) bridge decks can degrade the serviceability of entire bridges, leading to concrete spalling and steel rebar corrosion and eventually becoming a safety concern. Drone-based infrared thermography (IRT) offers a promising tool for rapid assessment of bridge deck delamination compared to labor-intensive coring and visual inspection methods. However, the performance of passive IRT in detecting the delamination of RC bridge decks at its minimum depth and size (i.e., spall 25 mm or less deep or 150 mm or less in diameter) stipulated under a ‘fair’ condition state in the 2019 AASHTO Manual for Bridge Element Inspection has not been verified adequately. In this study, four RC slabs of identical design were cast with embedded thin foam sheets to simulate a wide range of delamination in thickness, size, spacing, and depth. Together, the four slabs form a representative RC deck of a mark-up bridge. Controllable indoor active IRT tests of individual slabs were conducted to detect and quantify the foams that serve as a ground truth for the performance of drone-based passive IRT for deck delamination detection on the mark-up bridge as the embedded foams may be displaced during concrete slab casting and the slab support is altered during erection. Statistical analysis was carried out on the thermal contrasts of both passive and active IRT tests on the four slabs to investigate the effects of delamination geometry and embedment depth. Both the active and passive IRT methods proved successful in localizing delamination and identifying its equivalent thicknesses of as low as 1.63 mm and a size (150 mm in length or 25 mm in depth) corresponding to the ‘fair’ condition state in the AASHTO Manual for Bridge Element Inspection.

Abstract Image

规范规定的钢筋混凝土桥面早期分层检测和量化:被动红外热成像与主动红外热成像的比较
钢筋混凝土(RC)桥面的分层会降低整座桥梁的适用性,导致混凝土剥落和钢筋腐蚀,最终成为安全隐患。与劳动密集型取芯和目测方法相比,基于无人机的红外热成像(IRT)为快速评估桥面分层提供了一种很有前途的工具。然而,被动式 IRT 在 2019 年 AASHTO《桥梁构件检测手册》规定的 "尚可 "状态下的最小深度和尺寸(即深度不超过 25 毫米或直径不超过 150 毫米的剥落)下检测 RC 桥面分层的性能尚未得到充分验证。在本研究中,浇注了四块设计相同的 RC 板,并嵌入了泡沫薄板,以模拟厚度、尺寸、间距和深度等多种分层情况。这四块板共同构成了一座标志性桥梁的代表性 RC 桥面。对单个板进行了可控室内主动 IRT 试验,以检测和量化泡沫,作为基于无人机的被动 IRT 性能的基本事实,用于检测标记桥的桥面分层,因为嵌入的泡沫可能会在混凝土板浇注过程中发生位移,板支撑也会在安装过程中发生变化。对四块板上的被动和主动 IRT 测试的热对比进行了统计分析,以研究分层几何形状和嵌入深度的影响。事实证明,主动和被动 IRT 方法都能成功定位分层,并确定其等效厚度(低至 1.63 毫米)和大小(长度为 150 毫米或深度为 25 毫米),与《美国联邦公路与桥梁协会桥梁构件检测手册》中的 "尚可 "状态相对应。
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来源期刊
Journal of Civil Structural Health Monitoring
Journal of Civil Structural Health Monitoring Engineering-Safety, Risk, Reliability and Quality
CiteScore
8.10
自引率
11.40%
发文量
105
期刊介绍: The Journal of Civil Structural Health Monitoring (JCSHM) publishes articles to advance the understanding and the application of health monitoring methods for the condition assessment and management of civil infrastructure systems. JCSHM serves as a focal point for sharing knowledge and experience in technologies impacting the discipline of Civionics and Civil Structural Health Monitoring, especially in terms of load capacity ratings and service life estimation.
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