[1] McCulloch, Warren; Walter Pitts (1943). "A Logical Calculus of Ideas Immanent in Nervous Activity". Bulletin of Mathematical Biophysics. 5 (4): 115–133.
[2] Morris RG. D.O. Hebb: The Organization of Behavior, Wiley: New York; 1949. Brain Res Bull. 1999 Nov-Dec;50(5-6):437. doi: 10.1016/s0361-9230(99)00182-3. PMID: 10643472.
[3] Schmidhuber, J. (2015). "Deep Learning in Neural Networks: An Overview". Neural Networks. 61: 85 117. arXiv:1404.7828. doi:10.1016/j.neunet.2014.09.003. PMID 25462637. S2CID 11715509
[4] Ivakhnenko, A. G. (1973). Cybernetic Predicting Devices. CCM Information Corporation.
[5] Ivakhnenko, A. G.; Grigorʹevich Lapa, Valentin (1967). Cybernetics and forecasting techniques. American Elsevier Pub. Co.
[6] Smolensky, P. (1986). "Information processing in dynamical systems: Foundations of harmony theory.". In D. E. Rumelhart; J. L. McClelland; PDP Research Group (eds.). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. 1. pp. 194–281
[7] G. E. Hinton and R. R. Salakhutdinov. Reducing the Dimensionality of Data with Neural Networks 28 JULY 2006 VOL 313
[8] Schmidhuber, Jürgen (2015). "Deep Learning". Scholarpedia. 10 (11):32832. Bibcode:2015SchpJ..1032832S. doi:10.4249/scholarpedia.32832.
[9] Hinton, G.E. (2009). "Deep belief networks". Scholarpedia. 4 (5): 5947. Bibcode:2009SchpJ...4.5947H
[10] Bengio, Y.; Courville, A.; Vincent, P. (2013). "Representation Learning: A Review and New Perspectives". IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (8): 1798 1828. arXiv:1206.5538. doi:10.1109/tpami.2013.50. PMID 23787338. S2CID 393948.
[11] Roohollah Hanteh, Mojtaba Hanteh, Ali Kheyroddin, Omid Rezaifar Determination of Strength Parameters in Roller Compacted Concrete (RCC) Dams using Laboratory Results and
Forecasting based on Artificial Neural Networks- Modares Civil Engineering journal. IQBQ. 2020; 20 (2) :55-70
URL: http://mcej.modares.ac.ir/article-16-46458-fa.html
[12] Panagiotis Sevente,kidis Dimitrios, Giagopoulos Alexandros, Arail opoulos, Olga Markogiannaki- Structural Health Monitoring using deep learning with optimal finite element model generated data- Mechanical Systems and Signal Processing-Volume 145, November–December 2020, 106972
[13] Mengying Li, Ziyan Wu, Dawei Jia, Shumao Qiu, Wei He, Structural damage identification using strain mode differences by the iFEM based on the convolutional neural network (CNN), Mechanical Systems and Signal Processing, 10.1016/j.ymssp.2021.108289, 165, (108289)
[14] Yeum CM, Dyke SJ. Vision based automated crack detection for bridge inspection. Comput-Aided Civil Infrastruct Eng 2015;30(10):759–70.
[15] Young-Jin Cha*, Wooram Choi, Gahyun Suh & Sadegh Mahmoudkhani . Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types. Computer-Aided Civil and Infrastructure Engineering 00 (2017) 1–17
[16] S. Yin, X. Li, H. Gao, O. Kaynak, Data-based techniques focused on modern industry: an overview, IEEE Trans. Industr. Electron. 62 (1) (2015) 657–667, ISSN 0278-0046
[17] S. Jeschke, C. Brecher, H. Song, D.B. Rawat, Industrial Internet of Things, Springer, 2017.
[18] D. Lund, C. MacGillivray, V. Turner, M. Morales, Worldwide and regional internet of things (iot) 2014–2020 forecast: A virtuous circle of proven value and demand, International Data Corporation (IDC), Tech. Rep
[19] Zhong Y, Xiang J. A two-dimensional plum-blossom sensor array-based multiple signal classification method for impact localization in composite structures. Comput-Aided Civil Infrastruct Eng 2016;31(8):633–43
[20] Shan J, Ouyang Y, Yuan HW, Shi W. Seismic data driven identification of linear physical models for building structures using performance and stabilizing objectives. Comput- Aided Civil Infrastruct Eng 2016;31(11):846–70.
[21] Shan J, Shi W, Lu X. Model reference health monitoring of hysteretic building structure using acceleration measurement with test validation. Comput-Aided Civil Infrastruct Eng
2016;31(6):449–64.
[22] Lei Y, Zhou H, Lai ZL. A computationally compact algorithm for real-time detection of abrupt structural stiffness degradations. Comput-Aided Civil Infrastruct Eng
2016;31(6):465–80
[23] Sarkar, S., Reddy, K. K., Giering, M., & Giering, M. (2016). Deep Learning for Structural Health Monitoring: A Damage Characterization Application. Annual Conference of the PHM Society, 8(1). https://doi.org/10.36001/phmconf.2016.v8i1.2544
[24] Y. Li, T. Kurfess, S. Liang, Stochastic prognostics for rolling element bearings, Mech. Syst. Signal Process. 14 (5) (2000) 747–762.
[25] Nur Sila Gulgec, S.M.ASCE; Martin Taká; and Shamim N. Pakzad, A.M.ASCE. Convolutional Neural
Network Approach for Robust Structural Damage Detection and Localization . J. Comput. Civ. Eng., 2019, 33
[26] Entezami, A.; Sarmadi, H.; Mariani, S. An Unsupervised Learning Approach for Early Damage Detection by Time Series Analysis and Deep Neural Network to Deal with Output-Only (Big) Data. Eng. Proc. 2020, 2, 17. https://doi.org/10.3390/ecsa-7-08281
[27] Amir Zayeri baghlani nejad, Mussa Mahmoudi Sahebi- A new method for determining the natural frequencies of structures from their ambient vibration-
Modares Civil Engineering journal. 2020; 20 (5) :89-102
URL: http://mcej.modares.ac.ir/article-16-38767-en.html
[28] Mohammad Hossein Rafiei1 | Hojjat Adeli. A novel machine learning‐based algorithm to detect damage in high‐rise building structures . Struct Design Tall Spec Build. 2017;e1400.
[29] Mohammad Hossein Rafiei, Hojjat Adeli A Novel Machine Learning Model for Estimation of Sale Prices of Real Estate Units. Journal of Construction Engineering and Management. Volume 142 Issue 2 - February 2016
[30] Mohammad Hossein Rafiei, Hojjat Adeli . A novel unsupervised deep learning model for global and local health condition assessment of structures. Engineering Structures 156 (2018) 598–607
[31] Chathurdara Sri NadithPathirage,JunLibStructural damage identification based on autoencoder neural networks and deep learning-Engineering Structures
Volume 172, 1 October 2018, Pages 13-28
[32] Chathurdara Sri Nadith Pathirage, Jun Li, Ling Li, Hong Hao, Wanquan Liu, Pinghe Ni. Structural damage identification based on autoencoder neural networks and deep learning. Engineering Structures 172 (2018) 13–28
[33] ZohrehMousavi,Mir MohammadEttefagh et all-
Developing deep neural network for damage detection of beam-like structures using dynamic response based on FE model and real healthy state-Applied Acoustics
Volume 168, November 2020, 107402
[34] Oh BK, Glisic B, Kim Y, Park HS. Convolutional neural network–based data recovery method for structural health monitoring. Structural Health Monitoring. 2020;19(6):1821-1838. doi:10.1177/1475921719897571
[35] Qarib H, Adeli H. A new adaptive algorithm for automated feature extraction in exponentially
damped signals for health monitoring of smart structures. Smart Mater Struct
2015;24(12):125040
[36] Menegotto M, Pinto PE. Method of analysis for cyclically loaded R.C. frames
including changes in geometry and non-elastic behavior of elements under combined normal force and bending. Istituto di Scienza e Tecnica delle Costruzioni, University of Rome; 1972.
مهندسی عمران مدرس