Response prediction of multi-story building using backpropagation neural networks method

• Published in: MATEC Web of Conferences Vol. 276; p. 1011
• Main Authors: Suryanita, Reni, Maizir, Harnedi, Firzal, Yohannes, Jingga, Hendra, Yuniarto, Enno
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 2019
• Subjects: Acceleration; Artificial neural networks; Aseismic buildings; Back propagation; Building design; Casualties; Catastrophic collapse; Earthquake resistance; Floorplans; Ground motion; Multistory buildings; Neural networks; Predictions; Property damage; Seismic design; Seismic hazard; Seismic response; Soil conditions; Structural analysis; Indonesia
• ISSN: 2261-236X; 2274-7214; 2261-236X
• DOI: 10.1051/matecconf/201927601011

The active ground motion in Indonesia might cause a catastrophic collapse of the building which leads to casualties and property damages. Therefore, it is imperative to design the structural response of building against seismic hazard correctly. Seismic-resistant building design process requires structural analysis to be performed to obtain the necessary building responses. However, the structural analysis could be difficult and time-consuming. This study aims to predict the structural response includes displacement, velocity, and acceleration of multi-story building with the fixed floor plan using Backpropagation Neural Network (BPNN) method. By varying the building height, soil condition, and seismic location in 47 cities in Indonesia, 6345 datasets were obtained and fed into the BPNN model for the learning process. The trained BPNN is capable of predicting the displacement, velocity, and acceleration responses with up to 96% of the expected rate.