工程结构运维多场景的敏感服役指标智能识别

Intelligent detection of sensitive service indicators in multiple scenarios of structural maintenance

随着我国土木工程行业由建造向运维逐渐转型,工程结构服役安全保障需求陡增,提质增效的结构智能诊断方法成为研究热点。结构服役性态指标是表征工程结构安全水平的要素,是工程结构诊断养护技术体系以及结构健康监测研究的基础,判断结构服役性态的敏感指标并进一步实现指标的智能识别是工程结构诊断智能化的首要任务。为此,围绕工程结构运维公共建筑、地铁隧道、公路桥梁、公路路面等多个场景中的敏感服役指标的智能识别开展综述研究;梳理关键敏感指标,进一步对指标的智能化识别方法进行归纳总结。结果表明,以深度学习为代表的新一代人工智能技术有效推动了结构服役敏感指标的感知识别研究与应用,其中数字图像方法与深度学习算法在工程结构变形、表面病害智能识别中取得了良好的效果,展现了全面的应用优势。

With the gradual transformation of the civil engineering industry of China from construction to operation and maintenance, the demand for service safety assurance of engineering structures has increased sharply, and structural intelligent diagnosis methods that improve quality and efficiency have become a research hotspot. Structural service behavior indicator is the element to characterize the safety level of engineering structure and is the basis of structural maintenance systems and structural health monitoring research. Sensitive indicators for evaluating the service behavior of structures and further realizing the intelligent identification of indicators are the primary tasks of intelligent structural diagnosis. This paper focused on the intelligent identification of sensitive service indicators in various scenarios such as engineering structure operation and maintenance of public buildings, subway tunnels, highway bridges, and highway pavements. This paper sorted out key sensitive indicators, and further summarized the intelligent identification methods of indicators. The results show that the new generation of artificial intelligence technology represented by deep learning has effectively promoted the research and application of perceptual identification of structural service-sensitive indicators, and the digital image method and deep learning algorithm have achieved comprehensive application advantages in the intelligent detection of structural deformation and surface diseases.