scholarly journals Review of Vibration-Based Structural Health Monitoring Using Deep Learning

2020 ◽  
Vol 10 (5) ◽  
pp. 1680 ◽  
Author(s):  
Gyungmin Toh ◽  
Junhong Park
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Large Deviation ◽  
Machine Learning Algorithms ◽  
Structural Vibration ◽  
Current Status ◽  
Measured Signal ◽  
Learning Technology ◽  
Structural Health

With the rapid progress in the deep learning technology, it is being used for vibration-based structural health monitoring. When the vibration is used for extracting features for system diagnosis, it is important to correlate the measured signal to the current status of the structure. The measured vibration responses show large deviation in spectral and transient characteristics for systems to be monitored. Consequently, the diagnosis using vibration requires complete understanding of the extracted features to discard the influence of surrounding environments or unnecessary variations. The deep-learning-based algorithms are expected to find increasing application in these complex problems due to their flexibility and robustness. This review provides a summary of studies applying machine learning algorithms for fault monitoring. The vibration factors were used to categorize the studies. A brief interpretation of deep neural networks is provided to guide further applications in the structural vibration analysis.

scholarly journals A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring

2021 ◽  
Vol 11 (21) ◽  
pp. 10072
Author(s):  
Die Liu ◽  
Yihao Bao ◽  
Yingying He ◽  
Likai Zhang
Keyword(s):  
Missing Data ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Measured Data ◽  
Machine Learning Algorithms ◽  
Measure Data ◽  
Measured Signal ◽  
Negative Effects ◽  
Missing Data Imputation ◽  
Structural Health

Missing data caused by sensor faults is a common problem in structural health monitoring systems. Due to negative effects, many methods that adopt measured data to infer missing data have been proposed to tackle this problem in previous studies. However, capturing complex correlations from measured data remains a significant challenge. In this study, empirical mode decomposition (EMD) combined with a bidirectional gated recurrent unit (BiGRU) is proposed for the recovery of the measured data. The proposed EMD-BiGRU converts the missing data task as predicted task of time sequence. The core of the method is to predict missing data using the raw data and decomposed subsequence as the decomposed subsequence can improve the predicted accuracy. In addition, the BiGRU in the hybrid model can extract the pre-post correlations of subsequence compared with traditional artificial neural networks. Raw acceleration data collected from a three-story structure are used to evaluate the performance of the EMD-BiGRU for missing data imputation. The recovery results of measure data show that the EMD-BiGRU exhibits excellent performance from two perspectives. First, the decomposed subsequence can improve the accuracy of the BiGRU predicted model. Second, the BiGRU outperforms other machine learning algorithms because it captures more microscopic changes of measured data. The experimental analysis suggests that the change patterns of raw measured signal data are complex, and therefore it is significant to extract the features before modeling.

NEW DIRECTIONS IN STRUCTURAL HEALTH MONITORING

2019 ◽  
Vol 2 (Special Issue on First SACEE'19) ◽  
pp. 77-112 ◽  
Author(s):  
Khalid Mosalam ◽  
Sifat Muin ◽  
Yuqing Gao
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Earthquake Engineering ◽  
Health Monitoring ◽  
Damage Identification ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Engineering Research ◽  
Structural Health ◽  
Cumulative Absolute Velocity

This paper presents two on-going efforts of the Pacific Earthquake Engineering Research (PEER) center in the area of structural health monitoring. The first is data-driven damage assessment, which focuses on using data from instrumented buildings to compute the values of damage features. Using machine learning algorithms, these damage features are used for rapid identification of the level and location of damage after earthquakes. One of the damage features identified to be highly efficient is the cumulative absolute velocity. The second is vision-based automated damage identification and assessment from images. Deep learning techniques are used to conduct several identification tasks from images, examples of which are the structural component type, and level and type of damage. The objective is to use crowdsourcing, allowing the general public to take photographs of damage and upload them to a server where damage is automatically identified using deep learning algorithms. The paper also introduces PEER.s effort and preliminary results in engaging the engineering and computer science communities in such developments through the PEER Hub Image-Net (F-Net) challenge.

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.

scholarly journals Structural Health Monitoring using Varied Machine Learning Algorithms

2018 ◽  
Vol 7 (3.12) ◽  
pp. 793 ◽  
Author(s):  
B Shanthi ◽  
Mahalakshmi N ◽  
Shobana M
Keyword(s):  
Machine Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
Structural Health

Structural Health Monitoring is essential in today’s world where large amount of money and labour are involved in building a structure. There arises a need to periodically check whether the built structure is strong and flawless, also how long it will be strong and if not how much it is damaged. These information are needed so that the precautions can be made accordingly. Otherwise, it may result in disastrous accidents which may take away even human lives. There are various methods to evaluate a structure. In this paper, we apply various classification algorithms like J48, Naive Bayes and many other classifiers available, to the dataset to check on the accuracy of the prediction determined by all of these classification algorithms and ar-rive at the conclusion of the best possible classifier to say whether a structure is damaged or not.  

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
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