Analysis and assessment of bridge health monitoring mass data—progress in research/development of “Structural Health Monitoring”

Li, Aiqun; Ding, Youliang; Wang, Hao; Guo, Tong

doi:10.1007/s11431-012-4818-5

Cited by 52 publications

(23 citation statements)

References 24 publications

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“…Since there are 6 types of vehicles, a total number of 6 functions are necessary for the entire trucks. First of all, the upper and lower bound of the GVW should be determined and then generate several uniformly distributed samples in the defined region via a uniform design (UD) approach [34,35]. Subsequently, conduct the finite element analysis to estimate the stress histories under individual truck passage, and then convert the stress histories into stress blocks via the rain-flow method.…”

Section: Approximating Response Surfacementioning

confidence: 99%

Fatigue Reliability Assessment of Orthotropic Bridge Decks under Stochastic Truck Loading

Liu

Xiao

et al. 2016

Shock and Vibration

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A steady traffic growth has posed a threat to the fatigue safety of existing bridges. Uncertainties in traffic flows add to the challenge of an accurate fatigue safety assessment. This article utilizes a stochastic traffic load model to evaluate the fatigue reliability of orthotropic steel bridge decks. The traffic load model is simulated by site-specific weigh-in-motion measurements. A response surface method is presented to solve the time-consuming problem caused by hotspot stress simulations in the finite element model. Applications of the stochastic traffic load model for probabilistic modeling and fatigue reliability assessment are demonstrated in the case study of a steel box-girder bridge. Numerical results indicate that the growth rate of the gross vehicle weight leads to a rapid decrease of the fatigue reliability in comparison to the traffic volume growth. Even though the traffic volume growth is rapid, the control of overloaded trucks in comparison to the traffic volume is an effective way to ensure the fatigue safety of the steel bridges.

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Section: Approximating Response Surfacementioning

confidence: 99%

Fatigue Reliability Assessment of Orthotropic Bridge Decks under Stochastic Truck Loading

Liu

Xiao

et al. 2016

Shock and Vibration

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“…The changes of weather, environmental erosion, natural disasters and increasing traffic loadings can continually modify the behavior and even cause deterioration of bridges in their long-term service [1]. Application of BHM has been recognized as an attractive tool to improve the health and safety of bridge and provide early warning on structure damage [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Data mining algorithms for bridge health monitoring: Kohonen clustering and LSTM prediction approaches

Guo

Jiang²,

Jiang³

et al. 2019

J Supercomput

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In recent years, bridge health monitoring system has been widely used to deal with massive data produced with the continuous growth of monitoring time. However, how to effectively use these data to comprehensively analyze the state of a bridge and provide early warning of bridge structure changes is an important topic in bridge engineering research. This paper utilizes two algorithms to deal with the massive data, namely Kohonen neural network and long short-term memory (LSTM) neural network. The main contribution of this study is using the two algorithms for health state evaluation of bridges. The Kohonen clustering method is shown to be effective for getting classification pattern in normal operating condition and is straightforward for outliers detection. In addition, the LSTM prediction method has an excellent prediction capability which can be used to predict the future deflection values with good accuracy and mean square error. The predicted deflections agree with the true deflections, which indicate that the LSTM method can be utilized to obtain the deflection value of structure. What's more, we can observe the changing trend of bridge structure by comparing the predicted value with its limit value under normal operation.

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“…[20][21][22][23][24][25][26] A structure installed with a SHMS can be considered as a full-scale experimental platform so that the surrounding environment information can be accumulated accordingly. [27][28][29] Nowadays, many SHMSs have been implemented on long-span cable-supported bridges, such as the Akashi Kaikyo Bridge in Japan, Runyang Suspension Bridge (RSB) in China, Jindo Bridge in Korea, etc.…”

Section: Introductionmentioning

confidence: 99%

Study on Wind Characteristics of Runyang Suspension Bridge Based on Long-Term Monitored Data

Wang

Guo

Tao

et al. 2016

Int. J. Str. Stab. Dyn.

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The slender and°exible characteristics make Runyang Suspension Bridge (RSB) sensitive to the wind actions. In order to obtain reliable statistical wind characteristics and monitor the wind environment at the bridge site, two anemometers are included in the structural health monitoring system (SHMS) to collect extensive wind data. In this paper, the recorded real-time wind data are analyzed in detail to acquire the wind rose diagram, mean wind speed and direction, turbulence intensity, turbulence integral scale, and power spectral density (PSD). Three strong wind events including Typhoon Matsa, Typhoon Khanun and Northern wind are comparatively analyzed to show the inhomogeneous wind characteristics. And the measured wind characteristics are compared with the recommended values in current speci¯cation of China. The analytical results show that the wind speed and wind direction at RSB site are greatly in°uenced by the subtropical monsoon climate and typhoon climate. And the traditional known rules concerning turbulence intensity are not proper for strong winds, while the rules can be validated by long-term monitored data. Meanwhile, the recommended values of turbulence integral scale by current speci¯cation are suitable for long-term monitored wind, but the adaptability to strong wind is still defective. In addition, the measured along-wind spectra match generally well with the commonly used PSDs of turbulence. However, more researches are still needed to re¯ne current PSDs of turbulence to perfectly satisfy with the measured spectrum. The conclusions can provide references for the wind-resistant analysis of RSB and structures in similar areas.

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Analysis and assessment of bridge health monitoring mass data—progress in research/development of “Structural Health Monitoring”

Cited by 52 publications

References 24 publications

Fatigue Reliability Assessment of Orthotropic Bridge Decks under Stochastic Truck Loading

Fatigue Reliability Assessment of Orthotropic Bridge Decks under Stochastic Truck Loading

Data mining algorithms for bridge health monitoring: Kohonen clustering and LSTM prediction approaches

Study on Wind Characteristics of Runyang Suspension Bridge Based on Long-Term Monitored Data

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