Pendekatan Artificial Neural Network untuk Mengestimasi Dimensi Optimum dan Rasio Tulangan Gedung

Kinanti Faradiba Harahap, Universitas Gadjah Mada, Indonesia
Akhmad Aminullah, Universitas Gadjah Mada, Indonesia
Henricus Priyosulistyo, Universitas Gadjah Mada, Indonesia


The conceptual design stage is necessary because it is considered as a fundamental input in decision making for maximizing the performance of a building. On the other hand, to maximize the performance of the building, there are many things that need to be considered. Therefore, an estimation of the optimum dimensions and the reinforcement ratios of beam and column was carried out at the conceptual design stage using the artificial neural network (ANN). ANN is a network based method that allows to get an accurate approach even with the limited information provided. This study aims to help engineers shorten the time for trial at the conceptual design stage. A total of 36 building variations modelling were prepared as the training data for the set up ANN model. Eight parameters used which consist of earthquake accelarations, soil sites class, joint types, beam spans, number of storey, high of storey,  concrete strengths and diameters of the reinforcement. There are 16 empirical formulas for estimating the optimum dimensions and the reinforcement ratios of beam and column. The results showed that the dimensional regression values and the reinforcement ratio were 98.53% and 96.06% respectively. This value indicates that ANN can estimate well.


Estimation; Neural Network; Optimum Dimension; Reinforcement Ratio; Conceptual Design

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