Prediksi Tingkat Keruntuhan Kolom Beton Bertulang Akibat Pembebanan Statik Menggunakan Jaringan Saraf Tiruan (JST) - Nopember Toni; Reni Suryanita; Ismeddiyanto Ismeddiyanto (Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains, 2015)
Column failure is one of failure condition in building that most anticipated in civil engineering world, so in designing column required more accurate calculation. One of solution in order to calculating column failure faster and more accurate is using Artificial Neural Network (ANN). ANN imitate how brain working and used to predict column failure. In this research, ANN used to predict reinforced concrete column damage level (DL) thatloaded by static load with variation in: column section dimension, concrete ultimate capacity, longitudinal reinforcement, and steel ultimate capacity. With all variation, total data used in this research is 10962 data. In this research, training and testing composition used is 70:30, hence total data for training data is 7673 data and for testing is 3289 data. Damage level calculated by dividing column strain from finite element software analysis with strain limit from SNI 2847-2013. In this research, column damage level noted as 0 if DL less than 1 and that mean column do not reach failure level, while column damage level noted as 1 if DL more equal than 1 and that mean column reach failure level. Result from testing show that ANN accuracy in predicting damage level reach 98%. This results show ANN can be used for predicting damage level faster and accurate, as well can be used as reference for designing column.Key Words: Artificial Neural Network, ANN, damage level, column, reinforced concrete, static load.