Structural health monitoring research traditionally focuses on detecting damage in members excluding the possibility of weakened joint conditions. Efficient model-based joint damage detection algorithms demand computationally expensive model that may affect the promptness of detection. Deep learning techniques have recently come up as efficient alternative to this cause. These techniques help in predicting occurrence and location of damage in structures based on some automatically identified features embedded in the measured structural response. This article proposes an output-only approach for joint damage detection in which a 1D-convolutional neural network (CNN) has been introduced to locate weakened joints in semi-rigid frames. CNN architecture merges feature extraction and classification simultaneously within a single learning block to automatically extract abstract features from typically 2D/3D signals. Proposed approach further modifies the usual CNN architecture to enable it to handle 1D response signals. Numerical validation is performed on a 2D-steel frame under different damage locations and severities followed by experimental validation on a steel frame structure. The method is observed to be very precise and prompt in detecting single as well as multiple damage scenarios. False alarm sensitivity of the proposed algorithm is also tested and found to be well within acceptable limits.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Abdel-Hamid O, Mohamed AR, Jiang H, Penn G (2012) Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition. In: 2012 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 4277–4280
Abdeljaber O, Avci O, Kiranyaz S, Gabbouj M, Inman DJ (2017) Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J Sound Vib 388:154–170
Abdeljaber O, Avci O, Kiranyaz MS, Boashash B, Sodano H, Inman DJ (2018) 1-D CNNS for structural damage detection: verification on a structural health monitoring benchmark data. Neurocomputing 275:1308–1317
An D, Kim NH, Choi JH (2015) Practical options for selecting data-driven or physics-based prognostics algorithms with reviews. Reliab Eng Syst Saf 133:223–236
Avci O, Abdeljaber O (2015) Self-organizing maps for structural damage detection: a novel unsupervised vibration-based algorithm. J Perform Constr Facilit 30(3):04015043
Avci O, Abdeljaber O, Kiranyaz S, Inman D (2017) Structural damage detection in real time: implementation of 1D convolutional neural networks for SHM applications. In: Structural health monitoring & damage detection, vol 7. Springer, pp 49–54
Bakhary N, Hao H, Deeks AJ (2007) Damage detection using artificial neural network with consideration of uncertainties. Eng Struct 29(11):2806–2815
Bandara RP, Chan TH, Thambiratnam DP (2014) Frequency response function based damage identification using principal component analysis and pattern recognition technique. Eng Struct 66:116–128
Cabrero J, Bayo E (2005) Development of practical design methods for steel structures with semi-rigid connections. Eng Struct 27(8):1125–1137
Cha YJ, You K, Choi W (2016) Vision-based detection of loosened bolts using the hough transform and support vector machines. Autom Constr 71:181–188
Cha YJ, Choi W, Büyüköztürk O (2017) Deep learning-based crack damage detection using convolutional neural networks. Comput Aided Civ Infrastruct Eng 32(5):361–378
Chen Y, Feng MQ (2009) Structural health monitoring by recursive Bayesian filtering. J Eng Mech 135(4):231–242
Pj Chun, Yamashita H, Furukawa S et al (2015) Bridge damage severity quantification using multipoint acceleration measurement and artificial neural networks. Shock Vib 2015:789384. https://doi.org/10.1155/2015/789384
Dackermann U, Li J, Samali B (2010) Dynamic-based damage identification using neural network ensembles and damage index method. Adv Struct Eng 13(6):1001–1016
Das S, Saha P, Patro S (2016) Vibration-based damage detection techniques used for health monitoring of structures: a review. J Civ Struct Health Monit 6(3):477–507
Diez A, Khoa NLD, Alamdari MM, Wang Y, Chen F, Runcie P (2016) A clustering approach for structural health monitoring on bridges. J Civ Struct Health Monit 6(3):429–445
Figueiredo E, Park G, Farrar CR, Worden K, Figueiras J (2011) Machine learning algorithms for damage detection under operational and environmental variability. Struct Health Monit 10(6):559–572
Gonzalez I, Karoumi R (2015) Bwim aided damage detection in bridges using machine learning. J Civ Struct Health Monit 5(5):715–725
Gulgec NS, Takáč M, Pakzad SN (2019) Convolutional neural network approach for robust structural damage detection and localization. J Comput Civ Eng 33(3):04019005
Ince T, Kiranyaz S, Eren L, Askar M, Gabbouj M (2016) Real-time motor fault detection by 1-D convolutional neural networks. IEEE Trans Ind Electron 63(11):7067–7075
Jarrett K, Kavukcuoglu K, Ranzato M, LeCun Y (2009) What is the best multi-stage architecture for object recognition? In: 2009 IEEE 12th international conference on computer vision, IEEE, pp 2146–2153
Jiang SF, Zhang CM, Koh C (2006) Structural damage detection by integrating data fusion and probabilistic neural network. Adv Struct Eng 9(4):445–458
Jiang SF, Zhang CM, Zhang S (2011) Two-stage structural damage detection using fuzzy neural networks and data fusion techniques. Expert Syst Appl 38(1):511–519
Jin C, Jang S, Sun X, Li J, Christenson R (2016) Damage detection of a highway bridge under severe temperature changes using extended Kalman filter trained neural network. J Civ Struct Health Monit 6(3):545–560
Kassimali A (2012) Matrix analysis of structures SI version, 2nd edn. Cengage Learning, Stamford, pp 537–541
Katkhuda HN, Dwairi HM, Shatarat N (2010) System identification of steel framed structures with semi-rigid connections. Struct Eng Mech 34(3):351
Kim Y (2014) Convolutional neural networks for sentence classification. arXiv:14085882
Kiranyaz S, Ince T, Gabbouj M (2015) Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans Biomed Eng 63(3):664–675
Kostić B, Gül M (2017) Vibration-based damage detection of bridges under varying temperature effects using time-series analysis and artificial neural networks. J Bridge Eng 22(10):04017065
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
LeCun Y, Boser BE, Denker JS, Henderson D, Howard RE, Hubbard WE, Jackel LD (1990) Handwritten digit recognition with a back-propagation network. In: Advances in neural information processing systems, pp 396–404
Liu YY, Ju YF, Duan CD, Zhao XF (2011) Structure damage diagnosis using neural network and feature fusion. Eng Appl Artif Intell 24(1):87–92
Mehrjoo M, Khaji N, Moharrami H, Bahreininejad A (2008) Damage detection of truss bridge joints using artificial neural networks. Expert Syst Appl 35(3):1122–1131
Monforton G, Wu TS (1963) Matrix analysis of semi-rigid connected frames. J Struct Div 89(6):13–24
Neves A, Gonzalez I, Leander J, Karoumi R (2017) Structural health monitoring of bridges: a model-free ANN-based approach to damage detection. J Civ Struct Health Monit 7(5):689–702
Ni Y, Zhou X, Ko J, Wang B (2000) Vibration-based damage localization in ting Kau bridge using probabilistic neural network. Adv Struct Dyn 2:1069–1076
Santos A, Figueiredo E, Silva M, Sales C, Costa J (2016) Machine learning algorithms for damage detection: Kernel-based approaches. J Sound Vib 363:584–599
Smarsly K, Dragos K, Wiggenbrock J (2016) Machine learning techniques for structural health monitoring. In: Proceedings of the 8th European workshop on structural health monitoring (EWSHM 2016), Bilbao, Spain, pp 5–8
Weng JH, Loh CH, Yang JN (2009) Experimental study of damage detection by data-driven subspace identification and finite-element model updating. J Struct Eng 135(12):1533–1544
Wilson DR, Martinez TR (2001) The need for small learning rates on large problems. In: IJCNN’01. International joint conference on neural networks. Proceedings (Cat. No. 01CH37222), IEEE, vol 1, pp 115–119
Yan L, Elgamal A, Cottrell GW (2011) Substructure vibration Narx neural network approach for statistical damage inference. J Eng Mech 139(6):737–747
Yun CB, Yi JH, Bahng EY (2001) Joint damage assessment of framed structures using a neural networks technique. Eng Struct 23(5):425–435
Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213–237
Zhou X, Ni Y, Zhang F (2014) Damage localization of cable-supported bridges using modal frequency data and probabilistic neural network. Math Probl Eng 2014:837963. https://doi.org/10.1155/2014/837963
Zhu W, He K (2013) Detection of damage in space frame structures with l-shaped beams and bolted joints using changes in natural frequencies. J Vib Acoust 135(5):051001
Conflict of Interest:
The authors declare that they have no conflict of interest.
This study was funded by Aeronautics Research & Development Board (DRDO), New Delhi, India through grant file no. ARDB/01/1051907/M/I.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Sharma, S., Sen, S. One-dimensional convolutional neural network-based damage detection in structural joints. J Civil Struct Health Monit 10, 1057–1072 (2020). https://doi.org/10.1007/s13349-020-00434-z
- Structural health monitoring (SHM)
- Joint damage detection
- Machine learning (ML)