Open AccessOptimal Method for Identification of Cracks in Different Beams using Fuzzy with Elephant Based Neural Network
Author(s) -
D Pitchaiah,
P. Srinivasa Rao
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.a5013.129219
Subject(s) - artificial neural network , stiffness , flexibility (engineering) , beam (structure) , retrofitting , computer science , component (thermodynamics) , structural engineering , extreme learning machine , computation , identification (biology) , artificial intelligence , engineering , algorithm , mathematics , physics , statistics , botany , biology , thermodynamics
Failure of Structures i.e., beams can be avoided by identifying the damage in the structure at its beginning and proper retrofitting. Recently, the researchers created a structure to recognize crack damage using a cracked beam component model that originates from the fracture mechanics and local flexibility rules. The present work exhibits the analysis of cracked beam with a machine learning model to assess the stiffness of the structure. Here Fuzzy Optimal Neural Network (FONN) is considered, in addition, the stiffness reduction technique, especially concerning thick beams, is featured with a survey of other crack models. The extricated model data are utilized to conversely recognize the cracks with the cracked beam component model through a model updating technique. The optimal Neural Network based stiffness computation utilizes a global searching procedure using Adaptive Elephant Herding Optimization (AEHO) to identify the number of cracks in various beams. From the proposed model, the attained results are compared with the existing research work, and other optimization and machine learning models.