- نظری بیگدیلو ر, ایراندوست پاکچین ص. تشخیص وجود ترک در تیر یکسرگیردار با استفاده از روش حسگری فشرده(CS). مهندسی مکانیک دانشگاه تبریز. 1401; 52(4): 145-152. doi: 10.22034/jmeut.2022.49808.3041
- نظامیوند چگینی س, باقری الف, رمضانی دشتمیان م, احمدی ب. طبقهبندی چند عصبی برای چرخدندهها بر پایهی تبدیل موجک گسسته، انتخاب مناسبترین ویژگی و ماشین بردار پشتیبان بهبود یافته. مهندسی مکانیک دانشگاه تبریز. 1401; 52(2): 361-370. doi: 10.22034/jmeut.2020.11607
- Dang HV, Raza M, Nguyen TV, Bui-Tien T, Nguyen HX. Deep learning-based detection of structural damage using time-series data. Structure and Infrastructure Engineering. 2021 Oct 11;17(11):1474-93.
- Abdeljaber O, Avci O, Kiranyaz S, Gabbouj M, Inman DJ. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. Journal of sound and vibration. 2017 Feb 3;388:154-70.
- Guo T, Wu L, Wang C, Xu Z. Damage detection in a novel deep-learning framework: a robust method for feature extraction. Structural Health Monitoring. 2020 Mar;19(2):424-42.
- Teng Z, Teng S, Zhang J, Chen G, Cui F. Structural damage detection based on real-time vibration signal and convolutional neural network. Applied Sciences. 2020 Jul 9;10(14):4720.
- Sharma S, Sen S. One-dimensional convolutional neural network-based damage detection in structural joints. Journal of Civil Structural Health Monitoring. 2020 Nov;10(5):1057-72.
- Liu T, Xu H, Ragulskis M, Cao M, Ostachowicz W. A data-driven damage identification framework based on transmissibility function datasets and one-dimensional convolutional neural networks: Verification on a structural health monitoring benchmark structure. Sensors. 2020 Feb 15;20(4):1059.
- اصغرزاده بناب الف, کلب خانی ه, بیژنوند س. ارائه روشی مبتنی بر یادگیری ماشین برای تشخیص آسیبهای خطی و غیرخطی سازه با ترکیب ویژگیهای عمیق زمانی و زمان – فرکانس. مهندسی مکانیک دانشگاه تبریز. 1403; 54(1): 71-80. doi: 10.22034/jmeut.2024.60099.3368
- Figueiredo E, Park G, Figueiras J, Farrar C, Worden K. Structural health monitoring algorithm comparisons using standard data sets. Los Alamos National Lab (LANL), Los Alamos, NM (United States); 2009 Mar 1.
- Cha YJ, Ali R, Lewis J, Büyükӧztürk O. Deep learning-based structural health monitoring. Automation in Construction. 2024 May 1;161:105328.
- Avci O, Abdeljaber O, Kiranyaz S, Hussein M, Gabbouj M, Inman DJ. A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications. Mechanical systems and signal processing. 2021 Jan 15;147:107077.
- O'Shea K. An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458. 2015.
- Marcus G. Deep Learning: A Critical Appraisal. arXiv preprint arXiv:1801.00631. 2018.
- Dong S, Wang P, Abbas K. A survey on deep learning and its applications. Computer Science Review. 2021 May 1;40:100379.
- Schmidt RM. Recurrent neural networks (rnns): A gentle introduction and overview. arXiv preprint arXiv:1912.05911. 2019 Nov 23.
- Van Houdt G, Mosquera C, Nápoles G. A review on the long short-term memory model. Artificial Intelligence Review. 2020 Dec;53(8):5929-55.
- Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555. 2014 Dec 11.
- Taud H, Mas JF. Multilayer perceptron (MLP). Geomatic approaches for modeling land change scenarios. 2018:451-5.
- Michelucci U. An introduction to autoencoders. arXiv preprint arXiv:2201.03898. 2022 Jan 11.
- Gharehbaghi VR, Noroozinejad Farsangi E, Yang TY, Hajirasouliha I. Deterioration and damage identification in building structures using a novel feature selection method. Structures. 2020 Dec 9;29:458–70.
- Gharehbaghi VR, Nguyen A, Noroozinejad Farsangi E, Yang TY. Supervised damage and deterioration detection in building structures using an enhanced autoregressive time-series approach. Journal of Building Engineering. 2020 Feb 20;30:101292.
|