Use of Statistical Analysis, Computer Vision, and Reliability for Structural Health Monitoring

Author(s):  
F. Necati Catbas ◽  
Mustafa Gul ◽  
H. Burak Gokce ◽  
Taha Dumlupinar ◽  
Ricardo Zaurin
Author(s):  
Esraa Elhariri ◽  
Nashwa El-Bendary ◽  
Shereen A. Taie

Feature engineering is a key component contributing to the performance of the computer vision pipeline. It is fundamental to several computer vision tasks such as object recognition, image retrieval, and image segmentation. On the other hand, the emerging technology of structural health monitoring (SHM) paved the way for spotting continuous tracking of structural damage. Damage detection and severity recognition in the structural buildings and constructions are issues of great importance as the various types of damages represent an essential indicator of building and construction durability. In this chapter, the authors connect the feature engineering with SHM processes through illustrating the concept of SHM from a computational perspective, with a focus on various types of data and feature engineering methods as well as applications and open venues for further research. Challenges to be addressed and future directions of research are presented and an extensive survey of state-of-the-art studies is also included.


2020 ◽  
pp. 147592172093558 ◽  
Author(s):  
Chuan-Zhi Dong ◽  
F Necati Catbas

Structural health monitoring at local and global levels using computer vision technologies has gained much attention in the structural health monitoring community in research and practice. Due to the computer vision technology application advantages such as non-contact, long distance, rapid, low cost and labor, and low interference to the daily operation of structures, it is promising to consider computer vision–structural health monitoring as a complement to the conventional structural health monitoring. This article presents a general overview of the concepts, approaches, and real-life practice of computer vision–structural health monitoring along with some relevant literature that is rapidly accumulating. The computer vision–structural health monitoring covered in this article at local level includes applications such as crack, spalling, delamination, rust, and loose bolt detection. At the global level, applications include displacement measurement, structural behavior analysis, vibration serviceability, modal identification, model updating, damage detection, cable force monitoring, load factor estimation, and structural identification using input–output information. The current research studies and applications of computer vision–structural health monitoring mainly focus on the implementation and integration of two-dimensional computer vision techniques to solve structural health monitoring problems and the projective geometry methods implemented are utilized to convert the three-dimensional problems into two-dimensional problems. This review mainly puts emphasis on two-dimensional computer vision–structural health monitoring applications. Subsequently, a brief review of representative developments of three-dimensional computer vision in the area of civil engineering is presented along with the challenges and opportunities of two-dimensional and three-dimensional computer vision–structural health monitoring. Finally, the article presents a forward look to the future of computer vision–structural health monitoring.


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