Effectiveness of Optimal Back Propagation Neural Network for Dynamic Behavior Prediction for Steel and Tubes with Light Weight Concrete
Composite circular steel tubes—with light weight concrete as infill for three different grades of light weight concrete say M20, M30, and M40 are tested for loading. In developing countries like India and other countries the use of extensive reinforced construction works from
the low cost building materials. In this research analysis light weight concrete materials are utilized to prepare the mix proportions to identify the dynamic behaviors of structures using machine learning technique. Initially consider the mixing materials with curing time 7 and 28, to train
the Back Propagation neural Network (BPN) with SCG training technique. For improve the performance research work optimize weights in trained structure using Opposition Based Improved GWO (OIGWO) technique. All the ideal results exhibit the way that the accomplished mistake values between the
yield of the trial values and the predictable behavior are resolutely equivalent to zero in the planned system. From that outcome proposed optimal model exactness is 98.23% with other machine learning techniques.
Keywords: COMPOSITE COLUMNS; LIGHT WEIGHT CONCRETE; NEURAL NETWORK; OPTIMIZATION; PREDICTION; TRAINING
Document Type: Research Article
Publication date: 01 January 2018
- Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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