The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
A novel machine learning‐based algorithm to detect damage in high‐rise building structures
Abstract
Summary: A novel model is presented for global health monitoring of large structures such as high‐rise building structures through adroit integration of 2 signal processing techniques, synchrosqueezed wavelet transform and fast Fourier transform, an unsupervised machine learning technique, the restricted Boltzmann machine, and a recently developed supervised classification algorithm called neural dynamics classification (NDC) algorithm. The model extracts hidden features in the frequency domain of the denoised measured response signals recorded by sensors on different elevations or floors of a structure. The extracted features are used as an input of the NDC to detect and classify the global health of the structure into categories such as healthy, light damage, moderate damage, severe damage, and near collapse. The proposed model is validated using the data obtained from a 3D 1:20 scaled 38‐story reinforced concrete building structure. The results are compared with 3 other supervised classification algorithms:k‐nearest neighbor (KNN), probabilistic neural networks (PNN), and enhanced PNN (EPNN). NDC, EPNN, PNN, and KNN yield maximum average accuracies of 96%, 94%, 92%, and 82%, respectively.
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Cites
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool
Daubechies, I., Lu, J., & Wu, H.-T. (2011) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2010.08.002 · Cited by 2,489
Gilles, J. (2013) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2013.2265222 · Cited by 2,210
IEEE Transactions on Neural Networks and Learning Systems
(2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.5962385 · Cited by 1,147
Vision‐Based Automated Crack Detection for Bridge Inspection
Yeum, C. M., & Dyke, S. J. (2015) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12141 · Cited by 517
Signal Processing Techniques for Vibration-Based Health Monitoring of Smart Structures
Amezquita-Sanchez, J. P., & Adeli, H. (2016) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-014-9135-7 · Cited by 487
Self-citationNair, K. K., Kiremidjian, A. S., & Law, K. H. (2006) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2005.06.016 · Cited by 420
Wavelets in medicine and biology
(1997) · Choice Reviews Online · doi:10.5860/choice.34-3901 · Cited by 398
Dynamic Fuzzy Wavelet Neural Network Model for Structural System Identification
Adeli, H., & Jiang, X. (2006) · Journal of Structural Engineering · doi:10.1061/(asce)0733-9445(2006)132:1(102 · Cited by 347
Self-citationA Novel Machine Learning Model for Estimation of Sale Prices of Real Estate Units
Rafiei, M. H., & Adeli, H. (2016) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0001047 · Cited by 287
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Cited by
Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Gabbouj, M., & Inman, D. J. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.107077 · Cited by 1,442
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
Haghighat, E., Raissi, M., Moure, A., Gomez, H., & Juanes, R. (2021) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2021.113741 · Cited by 1,305
Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images
Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., & Omata, H. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12387 · Cited by 870
Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network
Yang, X., Li, H., Yu, Y., Luo, X., Huang, T., & Yang, X. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12412 · Cited by 852
Automated EEG-based screening of depression using deep convolutional neural network
Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., Adeli, H., & Subha, D. P. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.04.012 · Cited by 650
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Metadata
- Authors
- Rafiei, M. H., & Adeli, H.
- Venue
- The Structural Design of Tall and Special Buildings
- Publisher
- Wiley
- Published
- 2017-07-07
- Volume
- 26
- Issue
- 18
- ISSN
- 1541-7794 (ISSN-L)
- 1541-7808
- OpenAlex
- W2729478895
- Cited by
- 346
- References
- 38
- FWCI
- 33.11
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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