PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Theoretical investigations for the verification of shear centre and deflection of sigma section by back propagation neural network using Python

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The most important challenges in the construction field is to do the experimentation of the designing at real time. It leads to the wastage of the materials and time consuming process. In this paper, an artificial neural network based model for the verification of sigma section characteristics like shear centre and deflection are designed and verified. The physical properties like weight, depth, flange, lip, outer web, thickness, and area to bring shear centre are used in the model. Similarly, weight, purlin centres with allowable loading of different values used in the model for deflection verification. The overall average error rate as 1.278 percent to the shear centre and 2.967 percent to the deflection are achieved by the model successfully. The proposed model will act as supportive tool to the steel roof constructors, engineers, and designers who are involved in construction as well as in the section fabricators industry.
Rocznik
Strony
181--192
Opis fizyczny
Bibliogr. 12 poz., il., tab.
Twórcy
autor
autor
  • Department of Civil Engineering, Government College of Technology, Coimbatore, India.
  • Mithran Structures, Coimbatore, India
Bibliografia
  • 1. Jason Z. Kim, Jonathan M. Soffer, Ari E. Kahn, Jean M. Vettel, Fabio Pasqualetti, and Danielle S. Bassett, “Role of graph architecture in controlling dynamical networks with applications to neural systems”, Nature Physics,14, 91 - 98, 2018.
  • 2. Paulo Leitão, Vladimír Mařík, and Pavel Vrba, “Past, Present, and Future of Industrial Agent Applications”, IEEE Transactions on Industrial Informatics, 9, 4, 2360 - 2372, 2013.
  • 3. Iman Mansouri, Aliakbar Gholampour, Ozgur Kisi, and Togay Ozbakkaloglu, “Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques”, Neural Computing and Applications, 29, 3, 873 - 888, 2018.
  • 4. Ashok, V. and Nirmal Kumar, A. “Determination of Blood Glucose Concentration by Using Wavelet Transform and Neural Networks”, Iranian Journal of Medical Sciences, 38, 1, 51 - 56, 2013.
  • 5. Bernard Widrow and Michael Lehr, A. “30 Years of Adaptive Neural Networks: Perceptron, Madaline, and Backpropagation”, Proceedings of IEEE, 78, 9, 1415-1442, 1990.
  • 6. Samson B. Cooper and Dario Di Maio, “Static load estimation using artificial neural network: Application on a wing rib”, Advances in Engineering Software, 125, 113 - 125, 2018.
  • 7. Nick Hand, Yu Feng, Florian Beutler, Yin Li, Chirag Modi, Uroš Seljak, and Zachary Slepian, “nbodykit: An Open-source, Massively Parallel Toolkit for Large-scale Structure”, The Astronomical Journal, 156, 4, 2018.
  • 8. David M. Rosenberg and Charles C. Horn, “Neurophysiological analytics for all! Free open-source software tools for documenting, analyzing, visualizing, and sharing using electronic notebooks”, Journal of Neurophysiology, 116, 2, 252 - 262, 2016.
  • 9. Adam J.Sadowski, Ludovica Pototschnig, and Petrina Constantinou, “The ‘panel analysis’ technique in the computational study of axisymmetric thin-walled shell systems”, Finite Elements in Analysis and Design, 152, 55 - 68, 2018.
  • 10. Minjie Zhu, Frank Mc Kenna, and Michael H.Scott, “Open Sees Py: Python library for the OpenSees finite element framework”, SoftwareX, 7, 6 - 11, 2018.
  • 11. Jianhua Zhou, Fengchong Lan, Jiqing Chen, and Fanjie Lai, “Uncertainty Optimization Design of a Vehicle Body Structure Considering Random Deviations”, Automotive Innovation, 1, 4, 342 - 351, 2018.
  • 12. Ashok,V., Rajan Singh, S. and Nirmalkumar, A. “Determination of blood glucose concentration by back propagation neural network”, Indian J. of Science and Technology, 3, 8, 916 - 918, 2010.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-512c7309-a739-406f-94d8-a7d00cfccdc9
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.