To assess the current conditions of bridges in practice, a manual visual inspection is commonly used for maintenance purposes. Because manual visual inspection is time consuming, expensive, and laborious, computer vision with deep learning techniques has recently been introduced as a promising tool for automatic damage detection and classification. However, damage localization is difficult in that close-up images do not contain the global structural context, while knowing the damage locations and the associated structural components is essential for interpreting the overall structural health. Compared with two-dimensional (2D) image data, the point clouds in the three-dimensional (3D) space with an extra dimension can be useful for bridge component recognition. However, previous research employing geometric features of bridges was only partially successful for relatively simple types of bridges without the background regions (e.g., ground, water, and vegetation). This study presents a methodology for automated bridge component recognition using deep learning. The proposed approach is designed for general bridges that may have curved decks or different pier heights. Furthermore, the proposed method can handle point clouds that have points in the background regions, significantly reducing the time-consuming preprocessing of the point cloud. For robust and automated segmentation, a set of point clouds is extracted from a bridge by subspace partition, and a deep leaning technique is employed to classify labels. Subsequently, the classification results are combined to determine the consensus label for each point based on a majority of estimated classes, thereby improving identification accuracy. The classification performance is experimentally validated using full-scale bridge.

Hyunjun Kim

Jinyoung Yoon

Sung‐Han Sim

Structural Control and Health Monitoring

期刊结构控制和健康监测涵盖结构控制、结构健康监测理论和智能材料和结构的所有理论和技术方面。该期刊专注于航空航天、民用、基础设施和机械工程应用。基于分析、计算和实验方法的原创投稿主要在监测、控制、智能材料和结构三大领域征集，涵盖系统识别、健康监测等学科、健康诊断、多功能材料、信号处理、传感器技术、被动、主动和半主动控制方案和实施、形状记忆合金、压电和机电一体化。还有致动器设计、动态系统、动态稳定性、人工智能工具、数据采集、无线通信、测量、用于局部损坏检测的 MEMS/NEMS 传感器、用于健康监测的光纤传感器、监测系统的远程控制、用于移动应用的传感器-记录器组合、腐蚀传感器、冲刷指示器和实验技术。

The journal "Structural Control and Health Monitoring" covers all theoretical and technical aspects of structural control, structural health monitoring theory, and smart materials and structures. It focuses on applications in aerospace, civil, infrastructure, and mechanical engineering. We welcome original submissions based on analytical, computational, and experimental methods, primarily in the three areas of monitoring, control, and smart materials and structures. Topics include system identification, health monitoring, health diagnosis, multifunctional materials, signal processing, sensor technology, passive, active, and semi-active control schemes and implementations, shape memory alloys, piezoelectric materials, and mechatronics. Additionally, it encompasses actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communication, measurement, MEMS/NEMS sensors for local damage detection, fiber optic sensors for health monitoring, remote control of monitoring systems, sensor-recorder combinations for mobile applications, corrosion sensors, scour indicators, and experimental techniques.

《Structural Control and Health Monitoring》期刊涵盖结构控制、结构健康监测理论以及智能材料和结构的各个理论和技术方面。该期刊专注于航空航天、民用、基础设施和机械工程等应用领域。我们欢迎基于分析、计算和实验方法的原创投稿，主要集中在监测、控制、智能材料和结构三个领域，涉及系统识别、健康监测、健康诊断、多功能材料、信号处理、传感器技术、被动、主动和半主动控制方案及其实施、形状记忆合金、压电材料和机电一体化等内容。此外，还包括致动器设计、动态系统、动态稳定性、人工智能工具、数据采集、无线通信、测量、用于局部损伤检测的MEMS/NEMS传感器、用于健康监测的光纤传感器、监测系统的远程控制、用于移动应用的传感器-记录器组合、腐蚀传感器、冲刷指示器以及实验技术等。

Automated bridge component recognition from point clouds using deep learning

This study presents a methodology for automated bridge component recognition using deep learning that can handle point clouds that have points in the background regions, significantly reducing the time‐consuming preprocessing of the point cloud.

Category	Quartile
工程技术	2
工程技术, 结构与建筑技术	2
工程技术, 工程土木	2
工程技术, 仪器仪表	2

Automated bridge component recognition from point clouds using deep learning

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Category	Quartile
INSTRUMENTS & INSTRUMENTATION	1