Steel and Composite Structures

  • 제21권3호
  • /
  • Pages.679-688
  • /
  • 2016
  • /
  • 1229-9367(pISSN)
  • /
  • 1598-6233(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam's shear strength

  • Safa, M. (Department of Civil Engineering, University of Malaya) ;
  • Shariati, M. (Department of Civil Engineering, University of Malaya) ;
  • Ibrahim, Z. (Department of Civil Engineering, University of Malaya) ;
  • Toghroli, A. (Department of Civil Engineering, University of Malaya) ;
  • Baharom, Shahrizan Bin (Department of Civil and Structural Engineering, National University of Malaysia) ;
  • Nor, Norazman M. (National Defence University of Malaysia) ;
  • Petkovic, Dalibor (University of Nis, Faculty of Mechanical Engineering, Department of Mechatronics and Control)
  • 투고 : 2015.12.21
  • 심사 : 2016.05.21
  • 발행 : 2016.06.30
⟨ 이전 논문 다음 논문 ⟩

초록

Structural design of a composite beam is influenced by two main factors, strength and ductility. For the design to be effective for a composite beam, say an RC slab and a steel I beam, the shear strength of the composite beam and ductility have to carefully estimate with the help of displacements between the two members. In this investigation the shear strengths of steel-concrete composite beams was analyzed based on the respective variable parameters. The methodology used by ANFIS (Adaptive Neuro Fuzzy Inference System) has been adopted for this purpose. The detection of the predominant factors affecting the shear strength steel-concrete composite beam was achieved by use of ANFIS process for variable selection. The results show that concrete compression strength has the highest influence on the shear strength capacity of composite beam.

키워드

과제정보

연구 과제 주관 기관 : University of Malaya

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  85. Influence of porosity and cement grade on concrete mechanical properties vol.10, pp.5, 2016, https://doi.org/10.12989/acc.2020.10.5.393
  86. Application of discrete element method (DEM) in characterization of bond-slip behavior in RC beams with confinement subjected to corrosion vol.28, pp.None, 2016, https://doi.org/10.1016/j.istruc.2020.10.026
  87. Prediction of total sediment load: A case study of Wadi Arbaat in eastern Sudan vol.26, pp.6, 2016, https://doi.org/10.12989/sss.2020.26.6.781
  88. Optimized AI controller for reinforced concrete frame structures under earthquake excitation vol.11, pp.1, 2021, https://doi.org/10.12989/acc.2021.11.1.001
  89. In-plane varying bending force effects on wave dispersion characteristics of single-layered graphene sheets vol.10, pp.2, 2021, https://doi.org/10.12989/anr.2021.10.2.101
  90. Ionic liquid coated magnetic core/shell CoFe2O4@SiO2 nanoparticles for the separation/analysis of trace gold in water sample vol.10, pp.3, 2016, https://doi.org/10.12989/anr.2021.10.3.295
  91. Optimization algorithms for composite beam as smart active control of structures using genetic algorithms vol.27, pp.6, 2016, https://doi.org/10.12989/sss.2021.27.6.1041
  92. Assessment of microstructure and surface effects on vibrational characteristics of public transportation vol.11, pp.1, 2021, https://doi.org/10.12989/anr.2021.11.1.101
  93. Smart estimation of automatic approach in enhancing the road safety under AASHTO Standard specification and STM vol.79, pp.3, 2021, https://doi.org/10.12989/sem.2021.79.3.389
  94. Application of multi-hybrid metaheuristic algorithm on prediction of split-tensile strength of shear connectors vol.28, pp.2, 2016, https://doi.org/10.12989/sss.2021.28.2.167
  95. Development of a Hybrid Artificial Neural Network-Particle Swarm Optimization Model for the Modelling of Traffic Flow of Vehicles at Signalized Road Intersections vol.11, pp.18, 2016, https://doi.org/10.3390/app11188387
  96. Analyzing shear strength of steel-concrete composite beam with angle connectors at elevated temperature using finite element method vol.40, pp.6, 2016, https://doi.org/10.12989/scs.2021.40.6.853
  97. Analyzing the energy performance of buildings by neuro-fuzzy logic based on different factors vol.23, pp.12, 2016, https://doi.org/10.1007/s10668-021-01382-4
  98. Evaluation and monitoring of impact resistance of fiber reinforced concrete by adaptive neuro fuzzy algorithm vol.34, pp.None, 2016, https://doi.org/10.1016/j.istruc.2021.09.072
  99. Adaptive neuro fuzzy evaluation of energy and non‐energy material productivity impact on sustainable development based on circular economy and gross domestic product vol.31, pp.1, 2016, https://doi.org/10.1002/bse.2878
  100. Application of ANFIS in the preparation of expert opinions and evaluation of building design variants in the context of processing large amounts of data vol.133, pp.None, 2016, https://doi.org/10.1016/j.autcon.2021.104045
  101. Engine performance fueled with jojoba biodiesel and enzymatic saccharification on the yield of glucose of microbial lipids biodiesel vol.239, pp.no.pd, 2016, https://doi.org/10.1016/j.energy.2021.122390
  102. Experimental and numerical studies on novel stiffener-enhanced steel-concrete-steel sandwich panels subjected to impact loading vol.45, pp.None, 2022, https://doi.org/10.1016/j.jobe.2021.103479
  103. Numerical and theoretical studies on shear behavior of steel-UHPC composite beams with waffle slab vol.47, pp.None, 2022, https://doi.org/10.1016/j.jobe.2021.103913