A review of computer vision–based structural health monitoring at local and global levels

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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Efficient development of vision-based dense three-dimensional displacement measurement algorithms using physics-based graphics models

Structural Health Monitoring ◽

10.1177/1475921720939522 ◽

2020 ◽

pp. 147592172093952

Author(s):

Yasutaka Narazaki ◽

Fernando Gomez ◽

Vedhus Hoskere ◽

Matthew D Smith ◽

Billie F Spencer

Keyword(s):

Structural Health Monitoring ◽

Health Monitoring ◽

Dimensional Space ◽

Three Dimensional ◽

Displacement Measurement ◽

Camera Motion ◽

Two Dimensional ◽

Civil Structures ◽

Individual Measurement ◽

Structural Health

This research investigates a framework for the efficient development of vision-based dense three-dimensional displacement measurement algorithms to support reliable structural health monitoring of civil structures. The framework exploits the use of a photo-realistic synthetic model, termed a physics-based graphics model, to simulate the entire process of vision-based measurement. At the same time, the synthetic environment is used to evaluate the performance of different post-processing algorithms quantitatively for a given measurement scenario, such as camera selection and camera placement. The effectiveness of the framework is demonstrated by optimizing the algorithms for the three-dimensional displacement measurement of a 14-bay laboratory truss structure. The vision-based dense three-dimensional displacement estimation algorithms optimized in this study consist of four steps: (1) camera parameter estimation, (2) camera motion estimation and compensation, (3) vision-based two-dimensional tracking, and (4) projection of two-dimensional tracking results to three-dimensional space. The algorithms use the knowledge from the finite element model to facilitate the implementation and maximize the measurement outcome, that is, model-informed approach. To test and evaluate the model-informed approach, synthetic videos are rendered for two measurement scenarios, that is, using a Digital Single Lens Reflex camera mounted on a tripod and using an Unmanned Aerial Vehicle camera. Then, the performance of the model-informed approach is evaluated by comparing the estimated displacement with the ground truth values. Based on the performance evaluation, an algorithm with the highest expected performance is selected for each of the two measurement scenarios. Finally, the selected algorithm is tested in a laboratory experiment. In contrast to the existing literature that investigates fixed individual measurement scenarios, the proposed framework can be used to test different measurement scenarios and estimate the outcome of each scenario before performing actual tests, facilitating the implementation of vision-based measurement for the structural health monitoring of civil structures.

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Entropy-Based Structural Health Monitoring System for Damage Detection in Multi-Bay Three-Dimensional Structures

Entropy ◽

10.3390/e20010049 ◽

2018 ◽

Vol 20 (1) ◽

pp. 49 ◽

Cited By ~ 5

Author(s):

◽

Keyword(s):

Structural Health Monitoring ◽

Damage Detection ◽

Monitoring System ◽

Health Monitoring ◽

Three Dimensional ◽

Health Monitoring System ◽

Structural Health ◽

Structural Health Monitoring System

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Feature Engineering for Structural Health Monitoring (SHM)

Advances in Data Mining and Database Management - Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science ◽

10.4018/978-1-7998-6659-6.ch008 ◽

2021 ◽

pp. 138-163

Author(s):

Esraa Elhariri ◽

Nashwa El-Bendary ◽

Shereen A. Taie

Keyword(s):

Computer Vision ◽

Structural Health Monitoring ◽

Health Monitoring ◽

Structural Damage ◽

Emerging Technology ◽

The Other ◽

Feature Engineering ◽

Future Directions ◽

Structural Health ◽

Computational Perspective

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.

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