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

• Autorzy: Janani S. , Thenmozhi R. , Jayagopal L. S.

Identyfikatory

DOI

10.2478/ace-2019-0027

• 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.

Słowa kluczowe

EN

artificial neural network; propagation; sigma section; shear centre; deflection; roof construction

PL

sieć neuronowa sztuczna; propagacja; sekcja sigma; centrum ścinania; ugięcie; konstrukcja dachu

• Wydawca: Komitet Inżynierii Lądowej i Wodnej PAN ; Politechnika Warszawska, Wydział Inżynierii Lądowej
• Czasopismo: Archives of Civil Engineering
• Rocznik: 2019
• Tom: Vol. 65, nr 2
• Strony: 181--192
• Opis fizyczny: Bibliogr. 12 poz., il., tab.

Twórcy

autor

Janani S.

• Department of Civil Engineering, Anna University, Chennai, India

autor

Thenmozhi R.

• Department of Civil Engineering, Government College of Technology, Coimbatore, India.

autor

Jayagopal L. S.

• Mithran Structures, Coimbatore, India

Bibliografia

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• 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.
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• 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).