MATEC Web Conf.
Volume 138, 2017The 6th International Conference of Euro Asia Civil Engineering Forum (EACEF 2017)
|Number of page(s)||9|
|Section||2-Structural and Construction Engineering|
|Published online||30 December 2017|
Damage Level Prediction of Reinforced Concrete Building Based on Earthquake Time History Using Artificial Neural Network
1 Department of Civil Engineering, Faculty of Engineering, University of Riau, Pekanbaru, Indonesia
2 Department of Civil Engineering, Sekolah Tinggi Teknologi Pekanbaru, Indonesia
* Corresponding author: firstname.lastname@example.org
The strong motion earthquake could cause the building damage in case of the building not considered in the earthquake design of the building. The study aims to predict the damage-level of building due to earthquake using Artificial Neural Networks method. The building model is a reinforced concrete building with ten floors and height between floors is 3.6 m. The model building received a load of the earthquake based on nine earthquake time history records. Each time history scaled to 0,5g, 0,75g, and 1,0g. The Artificial Neural Networks are designed in 4 architectural models using the MATLAB program. Model 1 used the displacement, velocity, and acceleration as input and Model 2 used the displacement only as the input. Model 3 used the velocity as input, and Model 4 used the acceleration just as input. The output of the Neural Networks is the damage level of the building with the category of Safe (1), Immediate Occupancy (2), Life Safety (3) or in a condition of Collapse Prevention (4). According to the results, Neural Network models have the prediction rate of the damage level between 85%-95%. Therefore, one of the solutions for analyzing the structural responses and the damage level promptly and efficiently when the earthquake occurred is by using Artificial Neural Network
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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