scholarly journals Deep learning for structural health monitoring under environmental and operational variations

2021 ◽  
Author(s):  
Mohsen Mousavi ◽  
Amir H. Gandomi
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Health

scholarly journals Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2778 ◽  
Author(s):  
Mohsen Azimi ◽  
Armin Eslamlou ◽  
Gokhan Pekcan
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Speed ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Driven ◽  
Structural Health ◽  
Promising Tool ◽  
Significant Attention

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

Structural Health Monitoring using deep learning with optimal finite element model generated data

2020 ◽  
Vol 145 ◽  
pp. 106972 ◽  
Author(s):  
Panagiotis Seventekidis ◽  
Dimitrios Giagopoulos ◽  
Alexandros Arailopoulos ◽  
Olga Markogiannaki
Keyword(s):  
Finite Element ◽  
Deep Learning ◽  
Structural Health Monitoring ◽  
Finite Element Model ◽  
Health Monitoring ◽  
Element Model ◽  
Structural Health

Computer vision and deep learning–based data anomaly detection method for structural health monitoring

2018 ◽  
Vol 18 (2) ◽  
pp. 401-421 ◽  
Author(s):  
Yuequan Bao ◽  
Zhiyi Tang ◽  
Hui Li ◽  
Yufeng Zhang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Deep Neural Networks ◽  
Detection Method ◽  
Monitoring Systems ◽  
Structural Health ◽  
Health Monitoring Systems ◽  
Incorrect Data

The widespread application of sophisticated structural health monitoring systems in civil infrastructures produces a large volume of data. As a result, the analysis and mining of structural health monitoring data have become hot research topics in the field of civil engineering. However, the harsh environment of civil structures causes the data measured by structural health monitoring systems to be contaminated by multiple anomalies, which seriously affect the data analysis results. This is one of the main barriers to automatic real-time warning, because it is difficult to distinguish the anomalies caused by structural damage from those related to incorrect data. Existing methods for data cleansing mainly focus on noise filtering, whereas the detection of incorrect data requires expertise and is very time-consuming. Inspired by the real-world manual inspection process, this article proposes a computer vision and deep learning–based data anomaly detection method. In particular, the framework of the proposed method includes two steps: data conversion by data visualization, and the construction and training of deep neural networks for anomaly classification. This process imitates human biological vision and logical thinking. In the data visualization step, the time series signals are transformed into image vectors that are plotted piecewise in grayscale images. In the second step, a training dataset consisting of randomly selected and manually labeled image vectors is input into a deep neural network or a cluster of deep neural networks, which are trained via techniques termed stacked autoencoders and greedy layer-wise training. The trained deep neural networks can be used to detect potential anomalies in large amounts of unchecked structural health monitoring data. To illustrate the training procedure and validate the performance of the proposed method, acceleration data from the structural health monitoring system of a real long-span bridge in China are employed. The results show that the multi-pattern anomalies of the data can be automatically detected with high accuracy.

scholarly journals Online structural health monitoring by model order reduction and deep learning algorithms

2021 ◽  
Vol 255 ◽  
pp. 106604
Author(s):  
Luca Rosafalco ◽  
Matteo Torzoni ◽  
Andrea Manzoni ◽  
Stefano Mariani ◽  
Alberto Corigliano
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Model Order Reduction ◽  
Learning Algorithms ◽  
Order Reduction ◽  
Model Order ◽  
Structural Health

scholarly journals A Hybrid Structural Health Monitoring Approach Based on Reduced-Order Modelling and Deep Learning

Proceedings ◽  
2020 ◽  
Vol 42 (1) ◽  
pp. 67
Author(s):  
Luca Rosafalco ◽  
Alberto Corigliano ◽  
Andrea Manzoni ◽  
Stefano Mariani
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Learning Strategies ◽  
Health Monitoring ◽  
Structural Damage ◽  
Multivariate Time Series ◽  
Safety Level ◽  
Reduced Order ◽  
Structural Health ◽  
Fully Convolutional Networks

Recent advances in sensor technologies coupled with the development of machine/deep learning strategies are opening new frontiers in Structural Health Monitoring (SHM). Dealing with structural vibrations recorded with pervasive sensor networks, SHM aims at extracting meaningful damage-sensitive features from the data, shaped as multivariate time series, and taking real-time decisions concerning the safety level. Within this context, we discuss an approach able to detect and localize a structural damage avoiding any pre-processing of the acquired data. The method takes advantage of the capability of Deep Learning of Fully Convolutional Networks, trained during an offline SHM phase. As a hybrid model- and data-based solution is looked for, Reduced Order Models are also built in the offline phase to reduce the computational burden of the whole monitoring approach. Through a numerical benchmark test, we show how the proposed method can recognize and localize different damage states.

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