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 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 of a Concrete Frame Subjected to Shake Table Excitations Using Smart Aggregates

2014 ◽  
Author(s):  
Wei Chang ◽  
Juin-Fu Chai ◽  
Wen-I Liao
Keyword(s):  
Structural Health Monitoring ◽  
Earthquake Engineering ◽  
Health Monitoring ◽  
Health Condition ◽  
Frame Structure ◽  
Shake Table ◽  
Rc Structures ◽  
Engineering Research ◽  
Structural Health ◽  
Concrete Frame

Structural health monitoring of RC structures under seismic loads has recently attracted dramatic attention in the earthquake engineering research community. In this paper, a piezoceramic-based device called “smart aggregate” was used for the health monitoring of a two stories one bay RC frame structure under earthquake excitations. The RC moment frame instrumented with smart aggregates was tested using a shake table with different ground excitation intensities. The distributed piezoceramic-based smart aggregates embedded in the RC structure were used to monitor the health condition of the structure during the tests. The sensitiveness and effectiveness of the proposed piezoceramic-based approach were investigated and evaluated by analyzing the measured responses.

Structural Health Monitoring for Local Damages of RC Walls Using Piezoceramic-Based Sensors Under Seismic Loading

2017 ◽  
Author(s):  
Wen-I Liao ◽  
Wen-Yu Jean
Keyword(s):  
Structural Health Monitoring ◽  
Earthquake Engineering ◽  
Health Monitoring ◽  
Shear Walls ◽  
Seismic Loading ◽  
The Other ◽  
Loading Test ◽  
Rc Structures ◽  
Engineering Research ◽  
Structural Health

Structural health monitoring of reinforced concrete (RC) structures under seismic loads have recently attracted dramatic attention in the earthquake engineering research community. In this study, reversed cyclic loading test of structural health monitoring of RC shear walls using piezoceramic (PZT)-based sensors are presented. The piezoceramic-based sensors called “smart aggregate (SA)”, was pre-embedded before casting of concrete and adopted for the structural health monitoring of the RC shear wall under seismic loading. Two RC walls were adopted in this test, one is the wall having damages in the boundary columns and foundation of the specimen, and the other is the wall having damages in the upper part of the wall panel. During the test, SAs embedded in the foundation were used as actuators to generate propagating waves, and the other selected SAs were used to detect the waves. By analyzing the wave response, the existence and locations of cracks and damages can be detected and the severity can be estimated. The experimental results demonstrate the sensitiveness and the effectiveness of the piezoceramic-based approach in the structural health monitoring and the identification of damage locations of shear governed concrete structures under seismic loading.

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 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 Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features

2021 ◽  
Vol 11 (12) ◽  
pp. 5727
Author(s):  
Sifat Muin ◽  
Khalid M. Mosalam
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Degree Of Freedom ◽  
Absolute Velocity ◽  
Support Vector ◽  
Damage State ◽  
Structural Health ◽  
Cumulative Absolute Velocity

Machine learning (ML)-aided structural health monitoring (SHM) can rapidly evaluate the safety and integrity of the aging infrastructure following an earthquake. The conventional damage features used in ML-based SHM methodologies face the curse of dimensionality. This paper introduces low dimensional, namely, cumulative absolute velocity (CAV)-based features, to enable the use of ML for rapid damage assessment. A computer experiment is performed to identify the appropriate features and the ML algorithm using data from a simulated single-degree-of-freedom system. A comparative analysis of five ML models (logistic regression (LR), ordinal logistic regression (OLR), artificial neural networks with 10 and 100 neurons (ANN10 and ANN100), and support vector machines (SVM)) is performed. Two test sets were used where Set-1 originated from the same distribution as the training set and Set-2 came from a different distribution. The results showed that the combination of the CAV and the relative CAV with respect to the linear response, i.e., RCAV, performed the best among the different feature combinations. Among the ML models, OLR showed good generalization capabilities when compared to SVM and ANN models. Subsequently, OLR is successfully applied to assess the damage of two numerical multi-degree of freedom (MDOF) models and an instrumented building with CAV and RCAV as features. For the MDOF models, the damage state was identified with accuracy ranging from 84% to 97% and the damage location was identified with accuracy ranging from 93% to 97.5%. The features and the OLR models successfully captured the damage information for the instrumented structure as well. The proposed methodology is capable of ensuring rapid decision-making and improving community resiliency.

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