Research and practice of health monitoring for long-span bridges in the mainland of China

Li, Hui; Ou, Jinping; Zhang, Xigang; Pei, Minshan; Li, Na

doi:10.12989/sss.2015.15.3.555

Cited by 34 publications

(18 citation statements)

References 22 publications

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“…By identifying the event points, two wave velocities can be determined as 2352 m/s and 3636 m/s. These two velocities are consistent with the shear wave velocity ( ) and the compression wave velocity ( ) of concrete, which can be determined by (6) as 2328 m/s and 3800 m/s, respectively. …”

Section: Propagation Velocities Of Vibration Waves In the Undergroundsupporting

confidence: 81%

“…Meanwhile, based on the "cpsd" and "pwelch" functions in Matlab, a transfer function can be defined in (6) to illustrate the vibration transmission properties between the soil and the structure. Figure 14(c) shows the curve of the transfer function.…”

Section: Characteristics Of Frequency Spectrum For a Stoppingmentioning

confidence: 99%

“…Since 1970s, vibration based health monitoring system has been widely used for the real-time monitoring of structural properties for infrastructures above the ground [5][6][7][8]. The vibration characteristics of structures, determined by either from the properties of wave propagation within the structures or from the vibration modes, are the key to apply the vibration based health monitoring methods.…”

Section: Introductionmentioning

confidence: 99%

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Vibration Characteristics of Underground Structure and Surrounding Soil Underneath High Speed Railway Based on Field Vibration Tests

Zhou

Zhang

Xie

2018

Shock and Vibration

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A series of field vibration tests were carried out at an underground metro station underneath the high speed railway by installing accelerometers both on the side wall of the metro station and in the surrounding soil. Within the frequency domain of 0-200 Hz, the attenuation, transmission, and frequency response properties of vibration for both the underground structure and the surrounding soil were analyzed and compared. The attenuation index is found to be decreased with the increase of underground structure stiffness. The existence of damping and coupling effect of the surrounding soil, as well as the interference of axle spectrum from excitation sources, makes it very challenging to separate the frequency response characteristics of structures from soil at FFT (Fast Fourier Transform) spectrum. The combined NExT (Natural Excitation Technique) and HHT (Hilbert-Huang Transform) method are thus used to study the waveforms and propagation velocities of vibration waves in underground structure and surrounding soil. The wave types and their speeds are determined and used for evaluating the structural elastic modulus. Compared with the attenuation index or natural frequency, wave velocity is easier to be recognized, is sensitive to the change of the structural stiffness, and requires limited number of sensors in the field. Based on the properties of the vibration characteristics studied in this work, the wave velocity based method is recommended for the health monitoring of underground structures.

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Section: Propagation Velocities Of Vibration Waves In the Undergroundsupporting

confidence: 81%

Section: Characteristics Of Frequency Spectrum For a Stoppingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vibration Characteristics of Underground Structure and Surrounding Soil Underneath High Speed Railway Based on Field Vibration Tests

Zhou

Zhang

Xie

2018

Shock and Vibration

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“…Accelerometers are the most commonly used sensors in a typical SHM system. They are usually set up with high sampling frequency; thus, a large volume of monitoring data is continually collected (S. Li et al., ; H. Li, Ou, Zhang, Pei, & Li, ). Handling of massive data in SHM is an emerging challenge due to diverse nature of data.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for data anomaly detection and data compression of a long‐span suspension bridge

Zhang

Noori

2019

Computer aided Civil Eng

200

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As intelligent sensing and sensor network systems have made progress and low-cost online structural health monitoring has become possible and widely implemented, large quantities of highly heterogeneous data can be acquired during the monitoring. This has resulted in exceeding the capacity of traditional data analytics techniques, especially in monitoring large-scale or critical civil structures. In particular, data storage has become a big challenge, hence, resulting in the emergence of data compression and reconstruction as a new area in structural health monitoring (SHM) of large infrastructure systems. SHM data generally include anomalies that can disturb structural analysis and assessment. The fundamental reasons for the abnormality of data are extremely complex. Therefore, reconstruction of the abnormal data is generally difficult and poses serious challenges to achieve high-accuracy after data has been compressed. Considering these significant challenges, in this paper, a novel deeplearning-enabled data compression and reconstruction framework is proposed that can be divided into two phases: (a) a one-dimensional Convolutional Neural Network (CNN) that extracts features directly from the input signals is designed to detect abnormal data with validated high accuracy; (b) a new SHM data compression and reconstruction method based on Autoencoder structure is further developed, which can recover the data with high-accuracy under such a low compression ratio. To validate the proposed approach, acceleration data from the SHM system of a long-span bridge in China are employed. In the abnormal data detection phase, the results show that the proposed method can detect anomaly with high accuracy. Subsequently, smaller reconstruction errors can be achieved even by using only 10% compression ratio for the normal data.

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“…In this study, for an efficient risk assessment and mitigation, structural health monitoring approaches have been adopted (Li et al, 2015). Risk assessment and mitigation approach have been applied on chosen types of low-to mid-rise R/C buildings.…”

Section: Representative Buildingsmentioning

confidence: 99%

Structural Risk Assessment and Mitigation for Low- to Mid-Rise Residential Buildings in China

Korkmaz

2017

Front. Built Environ.

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Research and practice of health monitoring for long-span bridges in the mainland of China

Cited by 34 publications

References 22 publications

Vibration Characteristics of Underground Structure and Surrounding Soil Underneath High Speed Railway Based on Field Vibration Tests

Vibration Characteristics of Underground Structure and Surrounding Soil Underneath High Speed Railway Based on Field Vibration Tests

Deep learning for data anomaly detection and data compression of a long‐span suspension bridge

Structural Risk Assessment and Mitigation for Low- to Mid-Rise Residential Buildings in China

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