Noncontact cable force estimation with unmanned aerial vehicle and computer vision

Tian, Yongding; Zhang, Cheng; Jiang, Shang; Zhang, Jian; Duan, Wenhui

doi:10.1111/mice.12567

Cited by 119 publications

(73 citation statements)

References 52 publications

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“…Automatic proximity‐monitoring methods identify the distance between construction objects, analyze potentially hazardous situations, and communicate the information to workers or equipment operators, thus helping to prevent struck‐by accidents. Camera‐mounted unmanned aerial vehicles (UAVs) can perform such monitoring effectively at a wide range of construction sites (Bang, Kim, & Kim, 2017; Ham, Han, Lin, & Golparvar‐Fard, 2016; Kang & Cha, 2018; Tian, Zhang, Jiang, Zhang, & Duan, 2020; Zhang & Elaksher, 2012). UAVs can access areas where the deployment of sensors is difficult and obtain visual data from construction sites.…”

Section: Introductionmentioning

confidence: 99%

Proactive proximity monitoring with instance segmentation and unmanned aerial vehicle‐acquired video‐frame prediction

Bang

Hong

Kim

2021

Computer aided Civil Eng

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To prevent struck-by accidents at construction sites, construction object movement should be predicted to avoid dangerous situations. This paper proposes a vision-based proactive proximity-monitoring method based on predictions of unmanned aerial vehicle (UAV)-acquired video frames. The method has three modules. The first module recognizes workers, excavators, and dump trucks on a pixel level. The second module predicts construction objects' future locations and postures. The third module generates proactive safety information, including the future direction and speed of objects moving toward the worker. For the evaluation of the method, 1,940 images extracted from nine UAV-acquired videos recorded at real construction sites were used as the dataset. The method recognized construction objects with a mean average precision of 94.32%, predicted the future frame after 1 s with an F-measure of 80.59%, and recorded mean proximity errors of 0.43, 0.91, and 1.22 m for the frames after 1, 2, and 3 s, respectively.

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Section: Introductionmentioning

confidence: 99%

Proactive proximity monitoring with instance segmentation and unmanned aerial vehicle‐acquired video‐frame prediction

Bang

Hong

Kim

2021

Computer aided Civil Eng

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“…Similarly, due to signal error and sampling rate limitations, GPS methods are not universally applicable to estimate the dynamics of stay cables during traffic (Kaloop, 2012). As one of the typical noncontact methods, the vision‐based method, which extracts dynamic characteristics of an object from video images through the template matching techniques, has received increasing attention for monitoring the dynamic characteristics of stay cables (Tian, Zhang, Jiang, Zhang, & Duan, 2020). For example, S. Kim, Jeon, Cheung, Kim, and Park (2017) developed a vision‐based monitoring system that uses image‐processing technology to estimate the cable force during traffic and installed a remotely controlled pan/tilt drive to estimate the force on multiple cables using a single system.…”

Section: Introductionmentioning

confidence: 99%

Cable force estimation of a long‐span cable‐stayed bridge with microwave interferometric radar

Zhao

Zhang

2020

Computer aided Civil Eng

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Since cables are the critical structural components for ensuring the overall structural integrity and safety of cable-stayed bridges, the portable and rapid cable force measurement has a very important practical significance in the bridge health monitoring. In this paper, a method of simultaneous and continuous estimation of the forces of multiple cables using a self-developed microwave interferometric radar was proposed. In the proposed approach, first, the time-varying modal frequencies are identified by the Hilbert transform algorithm from the displacements of multiple cables, which are monitored by the self-developed radar, and then, the time-varying cable forces are obtained by the vibration method. In addition, for the problem of multiple cables located in the same range bin when distance test or the test angle is nonideal, a singlechannel blind source separation algorithm based on vibrational mode decomposition (VMD) and time-frequency analysis was proposed to separate the cable signals. Furthermore, a method based on the spectrum correlation coefficients was proposed to determine the number of decomposition layers in VMD. The "Nanjing Eye" cablestayed footbridge was used to conduct field measurement to validate the proposed method. Field measurement results show a good agreement with reference measurements, which demonstrate that the proposed method can perform well in an actual project for portable and rapid cable force estimation. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Structural health monitoring (SHM) technologies are useful for data anomaly detection, 1 cable tension force estimation, 2,3 and dynamic properties investigation 4 of long‐span bridges. For middle/short span bridges, regular inspection is usually utilized for condition assessment, in which vibration‐based method is often used for investigating dynamic properties of bridge deck.…”

Section: Introductionmentioning

confidence: 99%

Time‐varying frequency‐based scaled flexibility identification of a posttensioned concrete bridge through vehicle–bridge interaction analysis

Tian

Wang

Zhang

2020

Struct Control Health Monit

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Dynamic testing methods have been widely used in engineering practice for investigating dynamic properties of bridges; however, only basic dynamic parameters (i.e., natural frequencies, damping ratios, and unscaled modal shapes) can be identified, which are insufficient for condition assessment and health monitoring. To address this limitation, this article takes a three-span posttensioned concrete bridge as testbed; more useful structural parameters including mass-normalized mode shapes and scaled flexibility matrix are intended to be identified from moving vehicle-induced responses. For identifying scaled flexibility matrix, structural scaling factor between arbitrarily scaled mode shapes and mass-normalized mode shapes needs to be calculated firstly. In this article, time-varying frequencies of vehicle-bridge interaction (VBI) system were adopted for structural scaling factor identification, and the sensitivity of parameters identification error on calculated scaling factor was also investigated. The effectiveness of the proposed method was verified by in situ measurements of the studied three-span concrete bridge. In field testing, static load test and classical impact vibration testing were performed on this bridge for verifying the correctness of the proposed method. The good agreement between predicted deflections by using the scaled flexibility and reference values illustrates the reliability of the proposed method.

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Noncontact cable force estimation with unmanned aerial vehicle and computer vision

Cited by 119 publications

References 52 publications

Proactive proximity monitoring with instance segmentation and unmanned aerial vehicle‐acquired video‐frame prediction

Proactive proximity monitoring with instance segmentation and unmanned aerial vehicle‐acquired video‐frame prediction

Cable force estimation of a long‐span cable‐stayed bridge with microwave interferometric radar

Time‐varying frequency‐based scaled flexibility identification of a posttensioned concrete bridge through vehicle–bridge interaction analysis

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