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http://dx.doi.org/10.12989/sem.2017.61.1.015

Shear forces amplification due to torsion, explicit reliance on structural topology. Theoretical and numerical proofs using the Ratio of Torsion (ROT) concept  

Bakas, Nikolaos (School of Architecture, Engineering, Land and Environmental Sciences, Neapolis University Pafos)
Publication Information
Structural Engineering and Mechanics / v.61, no.1, 2017 , pp. 15-29 More about this Journal
Abstract
The recently introduced index Ratio Of Torsion (ROT) quantifies the base shear amplification due to torsional effects on shear cantilever types of building structures. In this work, a theoretical proof based on the theory of elasticity is provided, depicting that the ratio of torsion (ROT) is independent of the forces acting on the structure, although its definition stems from the shear forces. This is a particular attribute of other design and evaluation criteria against torsion such as center of rigidity and center of strength. In the case of ROT, this evidence could be considered as inconsistent, as ROT is a function solely of the forces acting on structural members, nevertheless it is proven to be independent of them. As ROT is the amplification of the shear forces due to in-plan irregularities, this work depicts that this increase of internal shear forces rely only on the structural topology. Moreover, a numerical verification of this theoretical finding was accomplished, using linear statistics interpretation and nonlinear neural networks simulation for an adequate database of structures.
Keywords
torsional coupling; shear center; center of rigidity; center of twist; strength center; ratio of torsion; linear statistics; neural networks; solid mechanics;
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1 Alapour, T.F., Kazemian, A., Hasaninejad, A., Ghanbari, A. and Ramezanianpour, A.A. (2013), "Prediction of lightweight concrete strength by categorized regression, MLR and ANNs" Comput. Concrete, 12(2), 151-167.   DOI
2 Anagnostopoulos, S.A., Kyrkos, M.T. and Stathopoulos, K.G. (2015), "Earthquake induced torsion in buildings: critical review and state of the art", Earthq. Struct., 8(2), 305-377.   DOI
3 Beycioglu, A., Emiroglu, M., Kocak, Y. and Subas, S. (2015), "Analyzing the compressive strength of clinker mortars using approximate reasoning approaches-ANN vs MLR", Comput. Concrete, 15(1), 89-101.   DOI
4 Boresi, A.P., Schmidt, R.S. and Sidebottom, O.M. (1993), Advanced Mechanics Of Materials, Vol. 6, Wiley, New York, USA.
5 Box, G.E.P. and Muller, M.E. (1958) "A note on the generation of random normal deviates", Ann. Math. Statist., 29(2), 610-611   DOI
6 Computers and Structures (1997), SAP2000: Integrated Finite Element Analysis and Design of Structures, Analysis Reference, Computers and Structures, Berkeley, California, USA.
7 Hakim, S.J.S. and Razak, H.A. (2013), "Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANNs) for structural damage identification", Struct. Eng. Mech., 45(6), 779-802.   DOI
8 De La Llera, J.C. and Chopra, A.K. (1995), "Understanding the inelastic seismic behavior of asymmetric-plan buildings", Earthq. Eng. Struct. Dyn., 24(4), 549-572.   DOI
9 Engin, S., Ozturk, E.O. and Okay, F. (2015), "Estimation of ultimate torque capacity of the SFRC beams using ANN", Struct. Eng. Mech., 53(5), 939-956.   DOI
10 Goldberg, D.E. and Holland, J.H. (1998) "Genetic algorithms and machine learning", Mach. Learn., 3(2), 95-99.   DOI
11 Haykin, S. (2004), Neural Networks. A Comprehensive Foundation, Prentice Hall, Upper Saddle River, New Jersey, USA.
12 Hejal, R. and Chopra, A.K. (1987). "Earthquake response of torsionally-coupled buildings", Research Report No. UCBIEERC-87/20; Earthquake Engineering Research Center, University of California, Berkeley, USA.
13 Kan, C.L. and Chopra A.K. (1977) "Elastic earthquake analysis of torsionally coupled multistorey buildings", Earthq. Eng. Struct. Dyn., 5(4), 395-412.   DOI
14 Makridakis, S., Wheelwright, S.C. and Hyndman, R.J. (2008), Forecasting Methods And Applications. John Wiley & Sons, Hoboken, New Jersey, USA.
15 Marquardt, D.W. (1963) "An algorithm for least-squares estimation of nonlinear parameters", J. Soc. Indus. Appl. Math., 11(2), 431-441.   DOI
16 Mohammadhassani, M., Nezamabadi-pour, M., Suhatril, M. and Shariati, M. (2013), "Identification of a suitable ANN architecture in predicting strain in tie section of concrete deep beams", Struct. Eng. Mech., 46(6), 853-868.   DOI
17 Mylimaj, B. and Tso, W. K. (2002), "A strength distribution criterion for minimizing torsional response of asymmetric walltype systems", Earthq. Eng. Struct. Dyn., 31(1), 99-120.   DOI
18 Paulay, Th. (1998), "Torsional mechanisms in ductile building systems", Earthq. Eng. Sstruct. Dyn., 27(10), 1101-1121.   DOI
19 Peng-hui, L., Hong-ping, Z., Hui, L. and Shun, W. (2015), "Structural damage identification based on genetically trained ANNs in beams", Smart Struct. Syst., 15(1), 227-244.   DOI
20 Stathi, C.G., Bakas, N.P., Lagaros, N.D. and Papadrakakis, M. (2015), "Ratio of Torsion (ROT): an index for assessing the global induced torsion in plan irregular buildings", Earthq. Struct., 9(1), 145-171.   DOI
21 Tavakkol, S., Alapour, F., Kazemian, A., Hasaninejad, A., Ghanbari, A. and Ramezanianpour, A.A. (2013), "Prediction of lightweight concrete strength by categorized regression, MLR and ANN", Comput. Concrete, 12, 151-167.   DOI
22 Tso, W.K. (1990), "Static eccentricity concept for torsional moment estimations", J. Struct. Eng., 116(5) 1199-1212.   DOI
23 Yavuz, G. (2016), "Shear strength estimation of RC deep beams using the ANN and strut-and-tie approaches", Struct. Eng. Mech., 57(4), 657-680.   DOI