Smartphone-Based Bridge Seismic Monitoring System and Long-Term Field Application Tests

Shrestha, Ashok K.; Dang, Ji; Wang, Xin; Matsunaga, Shingo

doi:10.1061/(asce)st.1943-541x.0002513

Cited by 23 publications

(10 citation statements)

References 28 publications

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“…To date, studies on smartphone-based SHM techniques are mostly focused on application to bridge condition assessment [70,71], where the use of smartphone has shown to be a promising alternative to up-to-date SHM technologies. In this regard, some of the main issues related to application of smartphone for buildings SHM is associated with the deployment of these devices in buildings.…”

Section: Current and Future Challengesmentioning

confidence: 99%

“…In any case, however, accuracy and reliability of sensors, especially MEMS accelerometers, in common smartphones, represent to date the major obstacle to the development of this technology [71,72]. The resolution of smartphone acceleration measurements is not as precise as those of conventional sensors, and different generations of smartphones provide different acceleration measurement resolutions, which in some cases may not be precise enough to capture smaller and ambient vibration data.…”

Section: Current and Future Challengesmentioning

confidence: 99%

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Roadmap on measurement technologies for next generation structural health monitoring systems

Laflamme

Ubertini

Matteo

et al. 2023

Meas. Sci. Technol.

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Structural Health Monitoring (SHM) is the automation of the condition assessment process of an engineered system. When applied to geometrically large components or structures, such as those found in civil and aerospace infrastructure and systems, a critical challenge is in designing the sensing solution that could yield actionable information. This is a difficult task to conduct cost-effectively, because of the large surfaces under consideration and the localized nature of typical defects and damages. There has been significant research efforts in empowering conventional measurement technologies for applications to SHM in order to improve performance of the condition assessment process. Yet, the field implementation of these SHM solutions is still in its infancy, attributable to various economic and technical challenges. The objective of this Roadmap publication is to discuss modern measurement technologies that were developed for SHM purposes, along with their associated challenges and opportunities, and to provide a path to research and development efforts that could yield impactful field applications.

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Section: Current and Future Challengesmentioning

confidence: 99%

Section: Current and Future Challengesmentioning

confidence: 99%

Roadmap on measurement technologies for next generation structural health monitoring systems

Laflamme

Ubertini

Matteo

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…With extremely low initial and running costs, and the ability to develop custom analysis software, smartphones can essentially provide a complete monitoring solution. Shrestha et al 325 investigated the feasibility of a long-term bridge health monitoring of Japan's Takamatsu bridge. In more than 1 year operation period, seismic and traffic-induced vibrations from the smartphones were captured and validated against the reference seismometers to verify their viability and accuracy.…”

Section: Mobile-assisted Shmmentioning

confidence: 99%

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Malekloo

Özer

AlHamaydeh

et al. 2021

Structural Health Monitoring

270

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Conventional damage detection techniques are gradually being replaced by state-of-the-art smart monitoring and decision-making solutions. Near real-time and online damage assessment in structural health monitoring (SHM) systems is a promising transition toward bridging the gaps between the past’s applicative inefficiencies and the emerging technologies of the future. In the age of the smart city, Internet of Things (IoT), and big data analytics, the complex nature of data-driven civil infrastructures monitoring frameworks has not been fully matured. Machine learning (ML) algorithms are thus providing the necessary tools to augment the capabilities of SHM systems and provide intelligent solutions for the challenges of the past. This article aims to clarify and review the ML frontiers involved in modern SHM systems. A detailed analysis of the ML pipelines is provided, and the in-demand methods and algorithms are summarized in augmentative tables and figures. Connecting the ubiquitous sensing and big data processing of critical information in infrastructures through the IoT paradigm is the future of SHM systems. In line with these digital advancements, considering the next-generation SHM and ML combinations, recent breakthroughs in (1) mobile device-assisted, (2) unmanned aerial vehicles, (3) virtual/augmented reality, and (4) digital twins are discussed at length. Finally, the current and future challenges and open research issues in SHM-ML conjunction are examined. The roadmap of utilizing emerging technologies within ML-engaged SHM is still in its infancy; thus, the article offers an outlook on the future of monitoring systems in assessing civil infrastructure integrity.

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“…Thus, a new database had to be assembled from scratch to test the proposed method. Therefore, smartphones' recorded data during long-term vibration measurement of a bridge [42] were utilized to provide the necessary resources for creating a dataset. The bridge was instrumented with six smartphones (iPhone 5s) inside of the box girder at six different locations, continually measuring acceleration vibrations as shown in Figure 1.…”

Section: Data Collectionmentioning

confidence: 99%

Deep Learning-Based Real-Time Auto Classification of Smartphone Measured Bridge Vibration Data

Shrestha¹,

Dang

2020

Sensors

Self Cite

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In this study, a simple and customizable convolution neural network framework was used to train a vibration classification model that can be integrated into the measurement application in order to realize accurate and real-time bridge vibration status on mobile platforms. The inputs for the network model are basically the multichannel time-series signals acquired from the built-in accelerometer sensor of smartphones, while the outputs are the predefined vibration categories. To verify the effectiveness of the proposed framework, data collected from long-term monitoring of bridge were used for training a model, and its classification performance was evaluated on the test set constituting the data collected from the same bridge but not used previously for training. An iOS application program was developed on the smartphone for incorporating the trained model with predefined classification labels so that it can classify vibration datasets measured on any other bridges in real-time. The results justify the practical feasibility of using a low-latency, high-accuracy smartphone-based system amid which bottlenecks of processing large amounts of data will be eliminated, and stable observation of structural conditions can be promoted.

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Smartphone-Based Bridge Seismic Monitoring System and Long-Term Field Application Tests

Cited by 23 publications

References 28 publications

Roadmap on measurement technologies for next generation structural health monitoring systems

Roadmap on measurement technologies for next generation structural health monitoring systems

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Deep Learning-Based Real-Time Auto Classification of Smartphone Measured Bridge Vibration Data

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