Indirect Damage Identification in Bridges Based on Dynamic Tyre Pressure Monitoring

Kumar, G. Sai; Krishnanunni, C.G.; Rao, B. N.

doi:10.1142/s0219455421500565

Cited by 10 publications

(3 citation statements)

References 32 publications

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“…As a crucial reference of structural characteristics, bridge modal parameters provide comprehensive information for healthy bridge structures and reflect the damage to some extent [ 2 , 3 , 4 , 5 ]. Consequently, acquiring structural modal parameters has become a global focus of attention.…”

Section: Introductionmentioning

confidence: 99%

Subspace Identification of Bridge Frequencies Based on the Dimensionless Response of a Two-Axle Vehicle

Quan,

Zeng,

Jin

et al. 2024

Sensors

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As an essential reference to bridge dynamic characteristics, the identification of bridge frequencies has far-reaching consequences for the health monitoring and damage evaluation of bridges. This study proposes a uniform scheme to identify bridge frequencies with two different subspace-based methodologies, i.e., an improved Short-Time Stochastic Subspace Identification (ST-SSI) method and an improved Multivariable Output Error State Space (MOESP) method, by simply adjusting the signal inputs. One of the key features of the proposed scheme is the dimensionless description of the vehicle–bridge interaction system and the employment of the dimensionless response of a two-axle vehicle as the state input, which enhances the robustness of the vehicle properties and speed. Additionally, it establishes the equation of the vehicle biaxial response difference considering the time shift between the front and the rear wheels, theoretically eliminating the road roughness information in the state equation and output signal effectively. The numerical examples discuss the effects of vehicle speeds, road roughness conditions, and ongoing traffic on the bridge identification. According to the dimensionless speed parameter Sv1 of the vehicle, the ST-SSI (Sv1 < 0.1) or MOESP (Sv1 ≥ 0.1) algorithm is applied to extract the frequencies of a simply supported bridge from the dimensionless response of a two-axle vehicle on a single passage. In addition, the proposed methodology is applied to two types of long-span complex bridges. The results show that the proposed approaches exhibit good performance in identifying multi-order frequencies of the bridges, even considering high vehicle speeds, high levels of road surface roughness, and random traffic flows.

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Section: Introductionmentioning

confidence: 99%

Subspace Identification of Bridge Frequencies Based on the Dimensionless Response of a Two-Axle Vehicle

Quan,

Zeng,

Jin

et al. 2024

Sensors

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“…For example, Lin et al (2017) used the virtual distortion method to detect the damage of cable structures in bridge engineering. Kumar et al (2021) proposed a bridge indirect damage identification method based on dynamic tire pressure monitoring. A wavelet-based method called the Wavelet Energy Accumulation Method is developed by Han et al (2005), Ren and Sun (2008), and Machorro-Lopez et al (2021) to detect damage in early stages.…”

Section: Introductionmentioning

confidence: 99%

Structural health monitoring on Yangluo Yangtze River Bridge: Implementation and demonstration

Zhu

Ren

Wang

2022

Advances in Structural Engineering

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Long-span cable-supported bridge is one of crucial transportation infrastructures. Structural health monitoring (SHM) system that is used to assess structural service conditions and detect early damage has drawn a growing attention in engineering structural society. It provides a real-time monitoring of various structural changes under operational and extreme conditions. Yangluo Bridge over Yangtze River in Wuhan city of China is a 1280 m central span suspension bridge with an orthotropic steel box girder. Taking the SHM system of Yangluo Yangtze River Bridge as an example, this paper presents an implementation of SHM system on the bridge in terms of sensor layout, data acquisition strategy, data transmission, data processing, and abnormal alarm framework. With such an SHM system, the typical monitoring results are demonstrated such as operational environments, loadings, structural deformation, strains, dynamic properties, and structural responses. The SHM system of Yangluo Yangtze River Bridge is continuously gathering data and monitoring structural behavior. It is anticipated that it will provide either scientific or technological supports to the service comfort and safety of the bridge.

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“…Sujon et al [21] presented a state of art review for highlighting practices in Weigh-in-Motion (WIM) in transport applications by emphasizing on profits, limits, cost-effectiveness, and usage of data. Kumar et al [22] proposed an indirect condition monitoring approach considering thermodynamics principle relates to a variation in vehicle-bridge interaction loads to a variation in dynamic pressure of a tyre. The approach was executed in two phases.…”

Section: Introductionmentioning

confidence: 99%

Tyre Pressure Supervision of Two Wheeler Using Machine Learning

Pardeshi¹,

Patange²,

Jegadeeshwaran³

et al. 2022

Structural Durability &Amp; Health Monitoring

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The regulation of tyre pressure is treated as a significant aspect of 'tyre maintenance' in the domain of autotronics. The manual supervision of a tyre pressure is typically an ignored task by most of the users. The existing instrumental scheme incorporates stand-alone monitoring with pressure and/or temperature sensors and requires regular manual conduct. Hence these schemes turn to be incompatible for on-board supervision and automated prediction of tyre condition. In this perspective, the Machine Learning (ML) approach acts appropriate as it exhibits comparison of specific performance in the past with present, intended for predicting the same in near future. The current investigation experimentally assesses the suitability of ML scheme for vibration based on-board supervision of tyre pressure of two wheeled vehicle. In order to examine the vibration response of a wheel hub, the in-house design & development of DAQ (Data Acquisition System) is described. Micro Electro-Mechanical Scheme (MEMS) built accelerometer is incorporated with open source hardware and software to collect and store the data. This framework is easy to develop, monitor and can be retrofitted in two wheeled vehicle. For various pressure conditions, the change in response of wheel hub vibration with respect to time is collected. The statistical parameters describing these vibration signals are determined and the decision tree is applied to select distinguishing parameters between extracted parameters. The classification of different conditions of tyre pressure is carried out using ML classifiers.

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Indirect Damage Identification in Bridges Based on Dynamic Tyre Pressure Monitoring

Cited by 10 publications

References 32 publications

Subspace Identification of Bridge Frequencies Based on the Dimensionless Response of a Two-Axle Vehicle

Subspace Identification of Bridge Frequencies Based on the Dimensionless Response of a Two-Axle Vehicle

Structural health monitoring on Yangluo Yangtze River Bridge: Implementation and demonstration

Tyre Pressure Supervision of Two Wheeler Using Machine Learning

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