Determination of road profile using multiple passing vehicle measurements

Keenahan, Jennifer; Ren, Yifei; O’Brien, Eugene J.

doi:10.1080/15732479.2019.1703757

Cited by 23 publications

(33 citation statements)

References 28 publications

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“…On the other hand, methods for estimating mechanical vehicle parameters simultaneously with estimating road unevenness have been proposed. Keenahan et al [ 53 ] proposed a method for estimating road unevenness from the vibrations of multiple vehicles traveling on the same route. Their research is interesting because it proposes a method that can integrally process multiple vehicles and driving data.…”

Section: Related Workmentioning

confidence: 99%

Discussion on a Vehicle–Bridge Interaction System Identification in a Field Test

Shin

Okada

Yamamoto

2023

Sensors

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For infrastructures to be sustainable, it is essential to improve maintenance and management efficiency. Vibration-based monitoring methods are being investigated to improve the efficiency of infrastructure maintenance and management. In this paper, signals from acceleration sensors attached to vehicles traveling on bridges are processed. Methods have been proposed to individually estimate the modal parameters of bridges and road unevenness from vehicle vibrations. This study proposes a method to simultaneously estimate the mechanical parameters of the vehicle, bridge, and road unevenness with only a few constraints. Numerical validation examined the effect of introducing the Kalman filter on the accuracy of estimating the mechanical parameters of vehicles and bridges. In field tests, vehicle vibration, bridge vibration, and road unevenness were measured and verified, respectively. The road surface irregularities estimated by the proposed method were compared with the measured values, which were somewhat smaller than the measured values. Future studies are needed to improve the efficiency of vehicle vibration preprocessing and optimization methods and to establish a methodology for evaluating accuracy.

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Section: Related Workmentioning

confidence: 99%

Discussion on a Vehicle–Bridge Interaction System Identification in a Field Test

Shin

Okada

Yamamoto

2023

Sensors

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“…Therefore, more research on vehicle-bridge-interaction (VBI) encompassing vehicle fleets is suggested. 356 • For the transition toward the future of sustainable SHM systems, integration of SHM with digital twin plus blockchain is of utmost obscurity. Considering the many aspects of the ecosystem, the coherent and synergetic connection of multiple emerging technologies is detrimental, providing better QoS to the user and increase overall system efficiency and integrity.…”

Section: Open Research Issuesmentioning

confidence: 99%

“…Therefore, more research on vehicle-bridge-interaction (VBI) encompassing vehicle fleets is suggested. 356…”

Section: Open Research Issuesmentioning

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

266

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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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“…A half-car vehicle model is used to represent a two-axle vehicle [37]. The coupled vehicle-bridge system, shown in Figure 1, has been used in many other studies [32][33][34][35][36][37][38]. The half-car model has a total of four degrees of freedom, namely: bounce displacement (y s ) of sprung mass (m s ), pitch rotation (θ s ) of sprung mass, and two-axle hop displacements (y u,1 and y u,2 ).…”

Section: Bridge and Vehicle Modelsmentioning

confidence: 99%

A Novel Acceleration-Based Moving Force Identification Algorithm to Detect Global Bridge Damage

2021

Self Cite

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This paper presents a new moving force identification (MFI) algorithm that uses measured accelerations to infer applied vehicle forces on bridges. Previous MFI algorithms use strain or deflection measurements. Statistics of the inferred forces are used in turn as indicators of global bridge damage. The new acceleration-based MFI algorithm (A-MFI) is validated through numerical simulations with a coupled vehicle-bridge dynamic interaction model programmed in MATLAB. A focussed sensitivity study suggests that results are sensitive to the accuracy of the vehicle velocity data. The inferred Gross Vehicle Weight (GVW), calculated by A-MFI, is proposed as the bridge damage indicator. A real weigh-in-motion database is used with a simulation of vehicle/bridge interaction, to validate the concept. Results show that the standard deviation of inferred GVWs has a good correlation with the global bridge damage level.

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Determination of road profile using multiple passing vehicle measurements

Cited by 23 publications

References 28 publications

Discussion on a Vehicle–Bridge Interaction System Identification in a Field Test

Discussion on a Vehicle–Bridge Interaction System Identification in a Field Test

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

A Novel Acceleration-Based Moving Force Identification Algorithm to Detect Global Bridge Damage

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