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Lost Vibration Test Data Recovery Using Convolutional Neural Network: A Case Study | ||
| AUT Journal of Civil Engineering | ||
| مقاله 11، دوره 5، شماره 4، اسفند 2021، صفحه 701-714 اصل مقاله (1.63 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajce.2022.20641.5773 | ||
| نویسندگان | ||
| pouya moeinifard؛ Mohammad Sadra Rajabi؛ maryam bitaraf* | ||
| School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| چکیده | ||
| Data loss in Structural Health Monitoring (SHM) networks has recently become one of the main challenges for engineers. Therefore, a data recovery method for SHM, generally an expensive procedure, is essential. Lately, some techniques offered to recover this valuable raw data using Neural Network (NN) algorithms. Among them, the convolutional neural network (CNN) based on convolution, a mathematical operation, can be applied to non-image datasets such as signals to extract important features without human supervision. However, the effect of different parameters has not been studied and optimized for SHM applications. Therefore, this paper aims to propose different architectures and investigate the effects of different hyperparameters for one of the newest proposed methods, which is based on a CNN algorithm for the Alamosa Canyon Bridge as a real structure. For this purpose, three different CNN models were considered to predict one and two malfunctioned sensors by finding the correlation between other sensors, respectively. Then the CNN algorithm was trained by experimental data, and the results showed that the method had a reliable performance in predicting Alamosa Canyon Bridge's missed data. The accuracy of the model was increased by adding a convolutional layer. Also, a standard neural network with two hidden layers was trained with the same inputs and outputs as the CNN models. Based on the results, the CNN model had higher accuracy, lower computational cost, and was faster than the standard neural network. | ||
| کلیدواژه‌ها | ||
| Structural health monitoring؛ Convolutional neural network؛ Machine learning (ML)؛ Acceleration monitoring؛ Data recovery method | ||
| مراجع | ||
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[1] S. Sharma, S. Sen, One-dimensional convolutional neural network-based damage detection in structural joints, Journal of Civil Structural Health Monitoring, 10 (2020) 1057-1072.
[2] A. Hamdan, M.T.H. Sultan, F. Mustapha, Structural health monitoring of biocomposites, fibre-reinforced composites, and hybrid composite, Structural Health Monitoring of Biocomposites, Fibre-Reinforced Composites and Hybrid Composites, (2019) 227-242.
[3] J. Ou, H. Li, Structural health monitoring research in China: trends and applications, Structural Health Monitoring of Civil Infrastructure Systems, (2009) 463-516.
[4] R. Ghiasi, P. Torkzadeh, M. Noori, A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function, Structural Health Monitoring: An International Journal, 15 (2016) 302-316.
[5] D.H. Sigurdardottir, J. Stearns, B. Glisic, Error in the determination of the deformed shape of prismatic beams using the double integration of curvature, Smart Materials and Structures, 26 (2017) 075002.
[6] V.G.M. Annamdas, S. Bhalla, C.K. Soh, Applications of structural health monitoring technology in Asia, Structural Health Monitoring: An International Journal, 16 (2017) 324-346.
[7] B.K. Oh, J.W. Hwang, Y. Kim, T. Cho, H.S. Park, Vision-based system identification technique for building structures using a motion capture system, Journal of Sound and Vibration, 356 (2015) 72-85.
[8] D. Anastasopoulos, M. De Smedt, L. Vandewalle, G. De Roeck, E.P.B. Reynders, Damage identification using modal strains identified from operational fiber-optic Bragg grating data, Structural Health Monitoring, 17 (2018) 1441-1459.
[9] J.M.W. Brownjohn, A. De Stefano, Y.-L. Xu, H. Wenzel, A.E. Aktan, Vibration-based monitoring of civil infrastructure: challenges and successes, Journal of Civil Structural Health Monitoring, 1 (2011) 79-95.
[10] J.M. Ko, Y.Q. Ni, Technology developments in structural health monitoring of large-scale bridges, Engineering Structures, 27 (2005) 1715-1725.
[11] S. Nagarajaiah, K. Erazo, Structural monitoring and identification of civil infrastructure in the United States, Structural Monitoring and Maintenance, 3 (2016) 51-69.
[12] H. Li, J. Ou, X. Zhang, M. Pei, N. Li, Research and practice of health monitoring for long-span bridges in the mainland of China, Smart Structures and Systems, 15 (2015) 555-576.
[13] L. Sun, Z. Shang, Y. Xia, S. Bhowmick, S. Nagarajaiah, Review of Bridge Structural Health Monitoring Aided by Big Data and Artificial Intelligence: From Condition Assessment to Damage Detection, Journal of Structural Engineering (United States), 146 (2020) 04020073.
[14] H.-P. Wan, Y.-Q. Ni, Bayesian multi-task learning methodology for reconstruction of structural health monitoring data, Structural Health Monitoring, 18 (2019) 1282-1309.
[15] Z. Zhang, Y. Luo, Restoring method for missing data of spatial structural stress monitoring based on correlation, Mechanical Systems and Signal Processing, 91 (2017) 266-277.
[16] H.M. Lee, J.M. Kim, K. Sho, H.S. Park, A wireless vibrating wire sensor node for continuous structural health monitoring, Smart Materials and Structures, 19 (2010) 055004.
[17] B.K. Oh, B. Glisic, Y. Kim, H.S. Park, Convolutional neural network-based data recovery method for structural health monitoring, Structural Health Monitoring, 19 (2020) 1821-1838.
[18] H. Liu, Y.L. Ding, H.W. Zhao, M.Y. Wang, F.F. Geng, Deep learning-based recovery method for missing structural temperature data using LSTM network, Structural Monitoring and Maintenance, 7 (2020) 109-124.
[19] Y. Yu, F. Han, Y. Bao, J. Ou, A Study on Data Loss Compensation of WiFi-Based Wireless Sensor Networks for Structural Health Monitoring, IEEE Sensors Journal, 16 (2016) 3811-3818.
[20] Y. Bao, H. Li, X. Sun, Y. Yu, J. Ou, Compressive sampling-based data loss recovery for wireless sensor networks used in civil structural health monitoring, Structural Health Monitoring: An International Journal, 12 (2013) 78-95.
[21] W. Lu, J. Teng, C. Li, Y. Cui, Reconstruction to Sensor Measurements Based on a Correlation Model of Monitoring Data, Applied Sciences, 7 (2017) 243.
[22] Z. Chen, H. Li, Y. Bao, Analyzing and modeling inter-sensor relationships for strain monitoring data and missing data imputation: a copula and functional data-analytic approach, Structural Health Monitoring, 18(4) (2019) 1168-1188.
[23] J. Schmidhuber, Deep learning in neural networks: An overview, Neural Networks, 61 (2015) 85-117.
[24] Y. Bengio, Learning Deep Architectures for AI, Foundations and Trends® in Machine Learning, 2 (2009) 1-127.
[25] Y. Bao, Z. Tang, H. Li, Y. Zhang, Computer vision and deep learning-based data anomaly detection method for structural health monitoring, Structural Health Monitoring, 18 (2019) 401-421.
[26] K.A. Bani-Hani, Vibration control of wind-induced response of tall buildings with an active tuned mass damper using neural networks, Structural Control and Health Monitoring, 14 (2007) 83-108.
[27] O. Payán-Serrano, E. Bojórquez, J. Bojórquez, R. Chávez, A. Reyes-Salazar, M. Barraza, A. López-Barraza, H. Rodríguez-Lozoya, E. Corona, Prediction of Maximum Story Drift of MDOF Structures under Simulated Wind Loads Using Artificial Neural Networks, Applied Sciences, 7 (2017) 563.
[28] C.A. Perez-Ramirez, J.P. Amezquita-Sanchez, M. Valtierra-Rodriguez, H. Adeli, A. Dominguez-Gonzalez, R.J. Romero-Troncoso, Recurrent neural network model with Bayesian training and mutual information for response prediction of large buildings, Engineering Structures, 178 (2019) 603-615.
[29] Y. Xu, Y. Bao, J. Chen, W. Zuo, H. Li, Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images, Structural Health Monitoring, 18 (2019) 653-674.
[30] D.J. Atha, M.R. Jahanshahi, Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection, Structural Health Monitoring, 17 (2018) 1110-1128.
[31] C.M. Yeum, J. Choi, S.J. Dyke, Automated region-of-interest localization and classification for vision-based visual assessment of civil infrastructure, Structural Health Monitoring, 18 (2019) 675-689.
[32] H. Khodabandehlou, G. Pekcan, M.S. Fadali, Vibration‐based structural condition assessment using convolution neural networks, Structural Control and Health Monitoring, 26 (2018) e2308.
[33] Z. Tang, Z. Chen, Y. Bao, H. Li, Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring, Structural Control and Health Monitoring, 26 (2019) e2296.
[34] G. Fan, J. Li, H. Hao, Lost data recovery for structural health monitoring based on convolutional neural networks, Structural Control and Health Monitoring, 26 (2019).
[35] S.E. Mudiyanselage, P.H.D. Nguyen, M.S. Rajabi, R. Akhavian, Automated Workers’ Ergonomic Risk Assessment in Manual Material Handling Using sEMG Wearable Sensors and Machine Learning, Electronics, 10 (2021) 2558.
[36] C.R. Farrar, P.J. Cornwell, S.W. Doebling, M.B. Prime, Structural health monitoring studies of the Alamosa Canyon and I-40 bridges, Los Alamos National Lab., NM (US), 2000.
[37] S.W. Doebling, C.R. Farrar, P.J. Cornwell, Development of a general purpose code to couple experimental modal analysis and damage identification algorithms, in, Los Alamos National Lab., NM (United States), 1998.
[38] R. Brincker, L. Zhang, P. Andersen, Modal identification from ambient responses using frequency domain decomposition, in: Proceeding of the 18*‘International Modal Analysis Conference (IMAC), San Antonio, Texas, 2000. | ||
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