Distributed Displacement Response Investigation Technique for Bridge Structures Using Smartphones

Zhao, Xuefeng; Zhao, Qingan; Yu, Yan; Chen, Yuting; Li, Mingchu; Ou, Jinping

doi:10.1061/(asce)cf.1943-5509.0001025

Cited by 14 publications

(6 citation statements)

References 27 publications

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“…All these parameters were later used for local to global structural axis transformations. Zhao et al measured displacement response of bridge by photogrammetry technique using a mobile application, namely “D‐Viewer.” The application was based on characteristics of a 2D image regarding light intensity and color. A laser projection method was used to evaluate the displacement by measuring variation in the position of pixels in the picture using iPhone 6 and iPhone 6+.…”

Section: Next‐generation Sensing Methodsmentioning

confidence: 99%

A literature review of next-generation smart sensing technology in structural health monitoring

Sony

Laventure

Sadhu

2019

Struct Control Health Monit

401

183

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Summary Advent of computationally efficient smartphones, inexpensive high‐resolution cameras, drones, and robotic sensors has brought a new era of next‐generation intelligent monitoring systems for civil infrastructure. Vibration‐based condition assessment has garnered as a prominent method of evaluating the health of large‐scale infrastructure. The use of contact‐based sensors for acquiring vibration data becomes uneconomical and tedious due to their instrumentation cost, centralized nature, and densification required to collect sufficient data for system identification of modern complex structures. A need to advance and develop alternative methods for efficient sensing system results in next‐generation measurement technology of structural health monitoring. The abundance of handheld smartphones with easily programmable framework has helped in modifying relevant software to acquire vibration data using embedded sensors in the smartphone. The inexpensive cameras have been used to capture images and videos that are utilized to understand the structural behavior with the aid of advanced signal processing techniques. The inaccessible components of structures require noncontact sensors such as unmanned aerial vehicles (UAVs) or so‐called drones and mobile sensors to acquire structural data. To the authors' knowledge, this paper first time presents a comprehensive review of a suite of next‐generation smart sensing technology that has been developed in recent years within the context of structural health monitoring. The state‐of‐the‐art methods have been presented by conducting a detailed literature review of the recent applications of smartphones, UAVs, cameras, and robotic sensors used in acquiring and analyzing the vibration data for structural condition monitoring and maintenance.

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Section: Next‐generation Sensing Methodsmentioning

confidence: 99%

A literature review of next-generation smart sensing technology in structural health monitoring

Sony

Laventure

Sadhu

2019

Struct Control Health Monit

401

183

View full text Add to dashboard Cite

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“…However, collecting data using various sensors and monitoring every historic structure in a short time require a large expense. Moreover, SHM systems that are integrated and installed into historic structures also require professionals to operate them, and the distributed devices are difficult to implement in large‐scale structures (Zhao et al., ). Therefore, it may not be a cost‐effective choice.…”

Section: Introductionmentioning

confidence: 99%

Damage Classification for Masonry Historic Structures Using Convolutional Neural Networks Based on Still Images

Wang

Zhao

et al. 2018

Computer aided Civil Eng

Self Cite

114

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Manual inspection (i.e., visual inspection and/or with professional equipment) is the most predominant approach for identifying and assessing superficial damage of masonry historic structures at present. However, this method is costly and at times difficult to apply to remote structures or components. Existing convolutional neural network (CNN)‐based damage detection methods have not been specifically designed for the multiple damage identification of masonry historic structures. To overcome these limits, a deep architecture of CNN damage classification techniques for masonry historic structures is proposed in this article using a sliding window‐based CNN method to identify and locate four categories of damage (intact, crack, efflorescence, and spall) with an accuracy of 94.3%. This is the first attempt to identify the multidamage of historic masonry structures based on CNN techniques and achieve excellent classification results. The data are only trained and tested from images of the Forbidden City Wall in China, and the pixel resolutions of stretcher brick images and header brick images are 480 × 105 and 210 × 105, respectively. Two CNNs (AlexNet and GoogLeNet) are both trained on a small dataset (2,000 images for training, 400 images for validation and testing) and a large dataset (20,000 images for training, 4,000 images for validation and testing). The performance of the trained model (94.3% accuracy) is examined on five new images with 1,860 × 1,260 pixel resolutions.

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“…The app monitors dynamically structural displacements by means of a common laser device and a projection plate. Papers by this research group focus on the development and enhancement of the app [ 53 , 101 , 102 ] and an application for bridge response measurements [ 52 , 94 , 103 ]. The limitation of the app is its ability to track a single target in the form of a blob, although multiple clusters of pixels defining a target can be tracked in an image frame.…”

Section: Computer Vision-based Smartphone Sensing For Bridge Monitoringmentioning

confidence: 99%

Smartphone Prospects in Bridge Structural Health Monitoring, a Literature Review

Ozer,

Kromanis

2024

Sensors

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Bridges are critical components of transportation networks, and their conditions have effects on societal well-being, the economy, and the environment. Automation needs in inspections and maintenance have made structural health monitoring (SHM) systems a key research pillar to assess bridge safety/health. The last decade brought a boom in innovative bridge SHM applications with the rise in next-generation smart and mobile technologies. A key advancement within this direction is smartphones with their sensory usage as SHM devices. This focused review reports recent advances in bridge SHM backed by smartphone sensor technologies and provides case studies on bridge SHM applications. The review includes modelbased and data-driven SHM prospects utilizing smartphones as the sensing and acquisition portal and conveys three distinct messages in terms of the technological domain and level of mobility: (i) vibration-based dynamic identification and damage-detection approaches; (ii) deformation and condition monitoring empowered by computer vision-based measurement capabilities; (iii) drive-by or pedestrianized bridge monitoring approaches, and miscellaneous SHM applications with unconventional/emerging technological features and new research domains. The review is intended to bring together bridge engineering, SHM, and sensor technology audiences with decade-long multidisciplinary experience observed within the smartphone-based SHM theme and presents exemplary cases referring to a variety of levels of mobility.

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Distributed Displacement Response Investigation Technique for Bridge Structures Using Smartphones

Cited by 14 publications

References 27 publications

A literature review of next-generation smart sensing technology in structural health monitoring

A literature review of next-generation smart sensing technology in structural health monitoring

Damage Classification for Masonry Historic Structures Using Convolutional Neural Networks Based on Still Images

Smartphone Prospects in Bridge Structural Health Monitoring, a Literature Review

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