Structural health monitoring of a cable-stayed bridge with Bayesian neural networks

Many physics-based and surrogate models used in structural health monitoring are affected by different sources of uncertainty such as model approximations and simplified assumptions. Optimal structural health monitoring and prognostics are only possible with uncertainty quantification that leads to an informed course of action. In this article, a Bayesian neural network using variational inference is applied to learn a damage feature from a high-fidelity finite element model. Bayesian neural networks can learn from small and noisy data sets and are more robust to overfitting than artificial neural networks, which make it very suitable for applications such as structural health monitoring. Also, uncertainty estimates obtained from a trained Bayesian neural network model are used to build a cost-informed decision-making process. To demonstrate the applicability of Bayesian neural networks, an example of this approach applied to miter gates is presented. In this example, a degradation model based on real inspection data is used to simulate the damage evolution.

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Wireless structural health monitoring of cable-stayed bridge using Imote2-platformed smart sensors

10.1117/12.917543 ◽

2012 ◽

Cited By ~ 3

Author(s):

Duc-Duy Ho ◽

Khac-Duy Nguyen ◽

Po-Young Lee ◽

Dong-Soo Hong ◽

So-Young Lee ◽

...

Keyword(s):

Structural Health Monitoring ◽

Health Monitoring ◽

Smart Sensors ◽

Structural Health ◽

Cable Stayed Bridge

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Prototyping and Validation of MEMS Accelerometers for Structural Health Monitoring—The Case Study of the Pietratagliata Cable-Stayed Bridge

Journal of Sensor and Actuator Networks ◽

10.3390/jsan7030030 ◽

2018 ◽

Vol 7 (3) ◽

pp. 30 ◽

Cited By ~ 35

Author(s):

Chiara Bedon ◽

Enrico Bergamo ◽

Matteo Izzi ◽

Salvatore Noè

Keyword(s):

Structural Health Monitoring ◽

Health Monitoring ◽

Full Scale ◽

Shaking Table ◽

Efficient Design ◽

Dynamic Identification ◽

Structural Health ◽

Cable Stayed Bridge ◽

Mems Accelerometers

In recent years, thanks to the simple and yet efficient design, Micro Electro-Mechanical Systems (MEMS) accelerometers have proven to offer a suitable solution for Structural Health Monitoring (SHM) in civil engineering applications. Such devices are typically characterised by high portability and durability, as well as limited cost, hence resulting in ideal tools for applications in buildings and infrastructure. In this paper, original self-made MEMS sensor prototypes are presented and validated on the basis of preliminary laboratory tests (shaking table experiments and noise level measurements). Based on the well promising preliminary outcomes, their possible application for the dynamic identification of existing, full-scale structural assemblies is then discussed, giving evidence of their potential via comparative calculations towards past literature results, inclusive of both on-site, Experimental Modal Analysis (EMA) and Finite Element Analytical estimations (FEA). The full-scale experimental validation of MEMS accelerometers, in particular, is performed using, as a case study, the cable-stayed bridge in Pietratagliata (Italy). Dynamic results summarised in the paper demonstrate the high capability of MEMS accelerometers, with evidence of rather stable and reliable predictions, and suggest their feasibility and potential for SHM purposes.

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Structural Health Monitoring and Impact Detection Using Neural Networks for Damage Characterization

47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference<BR> 14th AIAA/ASME/AHS Adaptive Structures Conference<BR> 7th ◽

10.2514/6.2006-2117 ◽

2006 ◽

Author(s):

Richard Ross

Keyword(s):

Neural Networks ◽

Structural Health Monitoring ◽

Health Monitoring ◽

Structural Health ◽

Impact Detection ◽

Damage Characterization

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A multi-way data analysis approach for structural health monitoring of a cable-stayed bridge

Structural Health Monitoring ◽

10.1177/1475921718790727 ◽

2018 ◽

Vol 18 (1) ◽

pp. 35-48 ◽

Cited By ~ 7

Author(s):

Mehrisadat Makki Alamdari ◽

Nguyen Lu Dang Khoa ◽

Yang Wang ◽

Bijan Samali ◽

Xinqun Zhu

Keyword(s):

Data Analysis ◽

Structural Health Monitoring ◽

Health Monitoring ◽

Large Scale ◽

Damage Identification ◽

Tensor Analysis ◽

Support Vector ◽

Damage Scenarios ◽

Structural Health ◽

Cable Stayed Bridge

A large-scale cable-stayed bridge in the state of New South Wales, Australia, has been extensively instrumented with an array of accelerometer, strain gauge, and environmental sensors. The real-time continuous response of the bridge has been collected since July 2016. This study aims at condition assessment of this bridge by investigating three aspects of structural health monitoring including damage detection, damage localization, and damage severity assessment. A novel data analysis algorithm based on incremental multi-way data analysis is proposed to analyze the dynamic response of the bridge. This method applies incremental tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies. A total of 15 different damage scenarios were investigated; damage was physically simulated by locating stationary vehicles with different masses at various locations along the span of the bridge to change the condition of the bridge. The effect of damage on the fundamental frequency of the bridge was investigated and a maximum change of 4.4% between the intact and damage states was observed which corresponds to a small severity damage. Our extensive investigations illustrate that the proposed technique can provide reliable characterization of damage in this cable-stayed bridge in terms of detection, localization and assessment. The contribution of the work is threefold; first, an extensive structural health monitoring system was deployed on a cable-stayed bridge in operation; second, an incremental tensor analysis was proposed to analyze time series responses from multiple sensors for online damage identification; and finally, the robustness of the proposed method was validated using extensive field test data by considering various damage scenarios in the presence of environmental variabilities.

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