Frequency Identification of Bridges Using Smartphones on Vehicles with Variable Features

Shirzad-Ghaleroudkhani, Nima; Mei, Qipei; Gül, Mustafa

doi:10.1061/(asce)be.1943-5592.0001565

Cited by 32 publications

(9 citation statements)

References 38 publications

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“…New innovative ways of generating data from structures such as drive-by sensing 329 are compatible with the everyday smart devices placed in vehicles. 330 If successful crowdsourcing mechanisms are embedded into vehicular SHM, smartphones are likely to occupy more space in the next decade of SHM research. 311,315,331 Smartphone-based SHM can digitally incorporate up-to-date advances in system identification, ML, and data mining that encompasses a fully connected smart city platform.…”

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

177

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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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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

177

View full text Add to dashboard Cite

show abstract

“…In another study, the possibility of capturing bridge frequencies using smartphones was investigated by Matarazzo et al [31] in a real-life experiment. In another study, authors [32] investigated the robustness of indirect monitoring methods against vehicle features, such as suspension stiffness and speed, by performing a lab-scale experiment. All these studies note that the vibration of the vehicle is more dominated by vehicle features-such as suspension, mass, speed, etc.-than bridge frequencies.…”

Section: Of 19mentioning

confidence: 99%

Inverse Filtering for Frequency Identification of Bridges Using Smartphones in Passing Vehicles: Fundamental Developments and Laboratory Verifications

Shirzad-Ghaleroudkhani

Gül

2020

Sensors

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This paper puts forward a novel methodology of employing inverse filtering technique to extract bridge features from acceleration signals recorded on passing vehicles using smartphones. Since the vibration of a vehicle moving on a bridge will be affected by various features related to the vehicle, such as suspension and speed, this study focuses on filtering out these effects to extract bridge frequencies. Hence, an inverse filter is designed by employing the spectrum of vibration data of the vehicle when moving off the bridge to form a filter that will remove the car-related frequency content. Later, when the same car is moving on the bridge, this filter is applied to the spectrum of recorded data to suppress the car-related frequencies and amplify the bridge-related frequencies. The effectiveness of the proposed methodology is evaluated with experiments using a custom-built robot car as the vehicle moving over a lab-scale simply supported bridge. Nine combinations of speed and suspension stiffness of the car have been considered to investigate the robustness of the proposed methodology against car features. The results demonstrate that the inverse filtering method offers significant promise for identifying the fundamental frequency of the bridge. Since this approach considers each data source separately and designs a unique filter for each data collection device within each car, it is robust against device and car features.

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“…It was shown that the bridge frequency could be extracted from the vibration of the vehicle. Subsequently, a number of studies followed up on the notion of mobile sensing through analytical/numerical analyses, [30][31][32][33][34] as well as laboratory-scale, 28,[35][36][37][38][39][40] and real-life experiments. 19,41 One way to assess the condition of a bridge through mobile sensing is based on modal characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…It was shown that the bridge frequency could be extracted from the vibration of the vehicle. Subsequently, a number of studies followed up on the notion of mobile sensing through analytical/numerical analyses, 30–34 as well as laboratory‐scale, 28,35–40 and real‐life experiments 19,41 …”

Section: Introductionmentioning

confidence: 99%

Bridge mode shape identification using moving vehicles at traffic speeds through non‐parametric sparse matrix completion

Mei

Shirzad-Ghaleroudkhani

Gül

et al. 2021

Struct Control Health Monit

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Summary Advances in smart infrastructure produces a natural demand of system identification techniques for structural health and performance monitoring that can be scaled to regions and large asset inventories. Conventional approaches require sensors to be installed, often in long‐term deployments, on the monitored infrastructure systems, which is a costly undertaking when thousands of systems (e.g., bridges) need to be monitored. This paper presents a novel mode shape identification method for bridges that uses data collected from moving vehicles as input—a paradigm that can overcome limitations associated with conventional approaches. The method consists of two steps. First, the data collected from moving measurement points are mapped to virtual fixed points to generate a sparse matrix. Then, a “soft‐imputing” technique is employed to fill the sparse matrix. Finally, a singular value decomposition is applied to extract the mode shapes of the bridge. Experiments with synthetic, yet realistic, data are conducted to verify the method. The sensitivity of the proposed approach to different factors, including the number of vehicles, car speed, road roughness, and measurement errors, are also investigated. The results show that the proposed method is capable of identifying the mode shapes of the bridge accurately.

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Frequency Identification of Bridges Using Smartphones on Vehicles with Variable Features

Cited by 32 publications

References 38 publications

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

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

Inverse Filtering for Frequency Identification of Bridges Using Smartphones in Passing Vehicles: Fundamental Developments and Laboratory Verifications

Bridge mode shape identification using moving vehicles at traffic speeds through non‐parametric sparse matrix completion

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