Creep predicting model in masonry structure utilizing dynamic neural network
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Copyright: © 2020 Mustafa M. Abed, A. El-Shafie and Siti Aminah Bt. Osman. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Problem statement: When the loads are applied to a brickwork structure, visco-elastic behavior upon their stress-strain relationships is exhibited, where the response can be classified into two separate parts: an instantaneous elastic strains and time-dependent creep strains. The creep strain represents the non- instantaneous strain that happens with time when the stress is sustained. Through the previous century, along with the alter in brickwork construction, A chain of creep tests on brickwork has shown that creep in brickwork be able to result in deformation that rise gradually with the way of time. Brickwork has considerable creep strain that is complicated to predict because of its reliance on several unrestrained parameters (e.g., relative humidity, time of load application, stress level). Dependable and precise prediction models for the long term, time-dependent creep deformation of brickwork structures are required. Artificial Neural Network (ANN) models have been determined useful and efficient especially in such problems for which the characteristics of the processes are difficult to describe using numerical models. Approach: This study introduces a creep prediction model based Focused Time-Delay Neural Network (FTDNN) which could detect and consider within its architecture the time dependency which is major factor in creep deformation in brickwork structure. Results: Performance of the proposed FTDNN model was examined with experimental creep data from brickwork assemblages collected over the last 15 years. Results showed that the FTDNN model has a relatively small prediction error compared to the other models with the error less than 15%. Conclusion: The results showed that the FTDNN model outperformed the existing ANN models and significantly enhance the accuracy of creep prediction.
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- Dynamic neural network
- creep predicting