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Remote Sensing
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Vision-Based Sensing in Engineering Structures
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Vision-Based Sensing in Engineering Structures

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 16631

Special Issue Editors

Dr. Dongming Feng

E-Mail Website
Guest Editor
Senior Engineer, Thornton Tomasetti, Inc., New York City, NY, USA
Interests: computer vision; structural health monitoring; bridge engineering; data analytics; machine learning
Dr. Maria Q. Feng

E-Mail Website
Guest Editor
Renwick Professor, Department of Civil Engineering & Engineering Mechanics, Columbia University, Columbia, MA, USA
Interests: remote sensing; computer vision; sensor application for structural health monitoring; sustainability of civil infrastructural systems; smart structures
Dr. Aiqun Li

E-Mail Website
Guest Editor
Professor, Vice President, Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing, China
Interests: structural health monitoring; post-disaster evaluation; vibration control; seismic isolation and control; smart structures
Prof. Dr. Xiaowei Ye

E-Mail Website
Guest Editor
Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China
Interests: structural health monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advances in digital cameras and computer vision techniques make computer vision-based sensing a promising next-generation monitoring technology for engineering structures to complement traditional structural health monitoring (SHM) and nondestructive evaluation. Compared with conventional sensors, computer vision sensors are far more cost-effective and agile to set up, and provide significantly higher spatial-density measurements where each pixel could represent a measurement point.

Currently, computer vision sensing has been drawing attention and gaining popularity in two major areas: (1) vision-based sensors for dynamic response measurement and their SHM applications for modal/parameter identification, damage detection, force estimation, and model validation and updating; and (2) visual monitoring for structural surface defect detection and condition assessment. Despite the progress made in various state-of-the-art vision sensing methods for a wide range of applications, technical and practical issues arise when they are employed for the continuous monitoring of large-scale structures with complex geometries in difficult environments (e.g., changes in illumination/background, heat haze-induced image distortions, object occlusions, camera vibration, varying camera poses and distances, etc.).

This Special Issue is aimed at addressing the open research challenges and unsolved problems related to the vision-based sensing of engineering structures. Manuscripts are solicited that present new vision-based sensing systems useful in highly unstructured and dynamic environments, and innovative and efficient methods for interpreting and transforming monitoring results into actionable data for informed decision-making. Both original research articles and reviews are welcome.

Topics of interest include, but are not limited to:

  • Development of accurate vision-based displacement sensors;
  • SHM using computer vision and machine learning techniques;
  • Automated defect/damage detection and condition assessment of engineering structures via unmanned aerial vehicles;
  • Computer vision and artificial intelligence applications in defect/damage detection, quantification and localization;
  • Post-disaster assessment through vision data analytics;
  • Other new emerging vision-based sensing technologies.

Dr. Dongming Feng
Dr. Maria Q. Feng
Dr. Aiqun Li
Dr. Xiaowei Ye
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computer vision
  • vision sensor
  • remote sensing
  • dynamic response measurement
  • damage detection
  • structural health monitoring
  • unmanned aerial vehicles
  • machine learning
  • artificial intelligence
  • engineering structure

Published Papers (4 papers)

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Research

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22 pages, 9094 KiB  
Article
Estimation of the Lateral Dynamic Displacement of High-Rise Buildings under Wind Load Based on Fusion of a Remote Sensing Vibrometer and an Inclinometer
by Wei-Hua Hu, Zeng-Mao Xu, Ming-Yue Liu, De-Hui Tang, Wei Lu, Zuo-Hua Li, Jun Teng, Xiao-Hui Han, Samir Said and Rolf. G. Rohrmann
Remote Sens. 2020, 12(7), 1120; https://doi.org/10.3390/rs12071120 - 01 Apr 2020
Cited by 6 | Viewed by 2893
Abstract
This paper proposes a novel method to estimate the lateral displacement of high-rise structures under wind loads. The coefficient β(x) is firstly derived, reflecting the relation between the structural lateral dynamic displacement and the inclination angle at the height x [...] Read more.
This paper proposes a novel method to estimate the lateral displacement of high-rise structures under wind loads. The coefficient β(x) is firstly derived, reflecting the relation between the structural lateral dynamic displacement and the inclination angle at the height x of a structure. If the angle is small, it is the ratio between the structural fundamental mode shape and its first-order derivative without influence of external loads. Several dynamic experiments of structures are performed based on a laser remote sensing vibrometer and an inclinometer, which shows that the fundamental mode is dominated in the structural displacement response under different types of excitations. Once the coefficient β(x) is curve-fitted by measuring both the structural lateral dynamic displacement and the inclination angle synchronously, the real-time structural lateral displacement under operational conditions is estimated by multiplying the coefficient β(x) with the inclination angle. The advantage of the proposed method is that the coefficient β(x) can be identified by lateral dynamic displacement measured in high resolution by the remote sensing vibrometer, which is useful to reconstruct the displacement accurately by the inclination angle under operational conditions. Full article
(This article belongs to the Special Issue Vision-Based Sensing in Engineering Structures)
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14 pages, 5386 KiB  
Article
Thickness Measurement of Water Film/Rivulets Based on Grayscale Index
by Haiquan Jing, Yi Cheng, Xuhui He, Xu Zhou and Jia He
Remote Sens. 2019, 11(23), 2871; https://doi.org/10.3390/rs11232871 - 03 Dec 2019
Cited by 1 | Viewed by 4727
Abstract
This study proposed a nonintrusive and cost-efficient technique to measure the thickness of a thin water film/rivulet based on the grayscale index. This technique uses millions of probes and only needs a digital camera, fill lights, and pigment. For water colored with diluted [...] Read more.
This study proposed a nonintrusive and cost-efficient technique to measure the thickness of a thin water film/rivulet based on the grayscale index. This technique uses millions of probes and only needs a digital camera, fill lights, and pigment. For water colored with diluted pigment, the grayscale index of the water captured by a digital camera depends on the water thickness. This relationship can be utilized to measure the water thickness through digital image processing. In the present study, the relationship between the grayscale index and water thickness was theoretically and experimentally investigated. Theoretical derivation revealed that when the product of water thickness and the color density approaches to 0, the grayscale index is inversely proportional to the thickness. The experimental results show that under the color density of 0.05%, the grayscale index is inversely proportional to the thickness of water film when the thickness is less than 6 mm. This linear relationship was utilized to measure the distribution and profile of a water rivulet flowing on the lower surface of a cable model. Full article
(This article belongs to the Special Issue Vision-Based Sensing in Engineering Structures)
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21 pages, 6976 KiB  
Article
Infrastructure Safety Oriented Traffic Load Monitoring Using Multi-Sensor and Single Camera for Short and Medium Span Bridges
by Ye Xia, Xudong Jian, Bin Yan and Dan Su
Remote Sens. 2019, 11(22), 2651; https://doi.org/10.3390/rs11222651 - 13 Nov 2019
Cited by 53 | Viewed by 4671
Abstract
A reliable and accurate monitoring of traffic load is of significance for the operational management and safety assessment of bridges. Traditional weight-in-motion techniques are capable of identifying moving vehicles with satisfactory accuracy and stability, whereas the cost and construction induced issues are inevitable. [...] Read more.
A reliable and accurate monitoring of traffic load is of significance for the operational management and safety assessment of bridges. Traditional weight-in-motion techniques are capable of identifying moving vehicles with satisfactory accuracy and stability, whereas the cost and construction induced issues are inevitable. A recently proposed traffic sensing methodology, combining computer vision techniques and traditional strain based instrumentation, achieves obvious overall improvement for simple traffic scenarios with less passing vehicles, but are enfaced with obstacles in complicated traffic scenarios. Therefore, a traffic monitoring methodology is proposed in this paper with extra focus on complicated traffic scenarios. Rather than a single sensor, a network of strain sensors of a pre-installed bridge structural health monitoring system is used to collect redundant information and hence improve accuracy of identification results. Field tests were performed on a concrete box-girder bridge to investigate the reliability and accuracy of the method in practice. Key parameters such as vehicle weight, velocity, quantity, type and trajectory are effectively identified according to the test results, in spite of the presence of one-by-one and side-by-side vehicles. The proposed methodology is infrastructure safety oriented and preferable for traffic load monitoring of short and medium span bridges with respect to accuracy and cost-effectiveness. Full article
(This article belongs to the Special Issue Vision-Based Sensing in Engineering Structures)
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Other

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13 pages, 6273 KiB  
Technical Note
Crack Propagation and Fracture Process Zone (FPZ) of Wood in the Longitudinal Direction Determined Using Digital Image Correlation (DIC) Technique
by Ying Yu, Weihang Zeng, Wen Liu, He Zhang and Xiaohong Wang
Remote Sens. 2019, 11(13), 1562; https://doi.org/10.3390/rs11131562 - 02 Jul 2019
Cited by 20 | Viewed by 3729
Abstract
As a state-of-the-art method, the digital image correlation (DIC) technique is used to capture the fracture properties of wood along the longitudinal direction, such as the crack propagation, the strain field, and the fracture process zone (FPZ). Single-edge notched (SEN) specimens made of [...] Read more.
As a state-of-the-art method, the digital image correlation (DIC) technique is used to capture the fracture properties of wood along the longitudinal direction, such as the crack propagation, the strain field, and the fracture process zone (FPZ). Single-edge notched (SEN) specimens made of Douglas fir (Pseudotsuga menziesii) from Canada with different notch-to-depth ratios are tested by three-point-bending (3-p-b) experiment. The crack mouth opening displacements (CMOD) measured by the clip gauge and DIC technique agree well with each other, verifying the applicability of the DIC technique. Then, the quasi-brittle fracture process of wood is analyzed by combing the load-CMOD curve and the strain field in front of the preformed crack. Additionally, the equivalent elastic crack length is calculated using the linear superposition hypothesis. The comparison between the FPZ evolution and the equivalent elastic crack shows that specimens with higher notch-to-depth ratios have better cohesive effect and higher cracking resistance. Full article
(This article belongs to the Special Issue Vision-Based Sensing in Engineering Structures)
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