Assessment of artificial neural network and genetic programming as predictive tools |
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| Affiliation: | 1. BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824, USA;2. Department of Civil Engineering, The University of Akron, Akron, OH 44325, USA;1. Computer Science Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro 1, Puebla 72840, Mexico;2. INFOTEC – Centro de Investigación e Innovación, en Tecnologías de la Información y Comunicación, Cátedras CONACyT, Aguascalientes, Mexico;1. Department of Civil Engineering, The University of Akron, Akron, OH, USA;2. Civil Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran;3. School of Building Engineering and Technical Architecture, University of A Coruña, Spain;4. Department of Civil and Environmental Engineering, Engineering Building, Michigan State University, East Lansing, MI 48824, USA;1. Department of Construction Technology, University of A Coruña, E.T.S.I. Caminos, Canales, Puertos. Campus Elviña s/n, 15071 La Coruña, Spain;2. Department of Construction Technology, University of A Coruña, E.U. Arquitectura Técnica, Campus Zapateira s/n, 15071 La Coruña, Spain |
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| Abstract: | Soft computing techniques have been widely used during the last two decades for nonlinear system modeling, specifically as predictive tools. In this study, the performances of two well-known soft computing predictive techniques, artificial neural network (ANN) and genetic programming (GP), are evaluated based on several criteria, including over-fitting potential. A case study in punching shear prediction of RC slabs is modeled here using a hybrid ANN (which includes simulated annealing and multi-layer perception) and an established GP variant called gene expression programming. The ANN and GP results are compared to values determined from several design codes. For more verification, external validation and parametric studies were also conducted. The results of this study indicate that model acceptance criteria should include engineering analysis from parametric studies. |
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| Keywords: | Artificial neural networks Genetic programming Over-fitting Explicit formulation Punching shear RC slabs Parametric study |
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