[关键词]
[摘要]
为了应对当前高层建筑外墙饰面砖频繁脱落造成的公共安全隐患,通过归纳总结饰面砖缺陷的成因机理,明确了空鼓缺陷为外墙脱落的前骤。基于无人机搭载红外热成像技术,通过开展空鼓缺陷检测室内试验,研究了无人机的最佳观测姿态,揭示了无人机旋翼工作对外墙温度的影响规律,探明了空鼓缺陷特征参数对识别效果的影响,提出了一种基于温差阈值的外墙饰面砖空鼓快速识别方法。研究表明:当无人机距外墙的距离为2~3m,立面观测角度为-30~30°、平面观测角度为-15~15°时,观测效果最佳;无人机旋翼会使外墙降温增速10%;空鼓尺寸越大、埋深越浅、厚度越大,识别效果越好;黑色和红色饰面砖内的空鼓缺陷温度偏高,淡黄色饰面砖内的空鼓缺陷温度偏低。在此基础上,开展了室外试验,通过与可见光图像识别方法进行对比,验证了所提出检测方法的有效性,为高层建筑饰面砖空鼓缺陷的快速识别提供了新途径。
[Key word]
[Abstract]
To mitigate the safety hazards posed by the frequent detachment of facade tiles in high-rise buildings, this study summarized the causes of these defects, identifying hollowing defects as precursors to exterior wall detachment. Based on the UAV equipped with infrared thermal imaging technology, the study conducted laboratory testing of hollowing detection. The optimal observation attitude of the UAV was investigated. The impact of defect characteristic parameters on identification accuracy and the effect of UAV rotor operation on the temperature of external walls were evaluated. Additionally, a rapid identification method for hollowing was proposed using a temperature difference threshold. The study indicated that optimal observation occurs when the UAV is 2 to 3 meters from the external wall, with a vertical angle of -30 to 30° and a horizontal angle of -15 to 15°. The drone rotors increase the cooling rate of external walls by 10%. Recognition improves with hollowing of larger sizes, shallower depths, and greater thicknesses. Hollowing defects in black and red tiles displayed higher temperatures, while those in yellowish tiles exhibited lower temperatures. On this basis, outdoor testing was conducted to verify the effectiveness of the proposed detection method by comparing it with visible light image recognition, which provides a novel approach for the rapid identification of hollowing defects in facade tiles of high-rise buildings.
[中图分类号]
xxx
[基金项目]
国家自然科学基金资助(52308330);四川省建筑设计研究院有限公司科研项目(KYYN202231)