This study utilizes Artificial Neural Network (ANN) to predict structural responses of multi-storey reinforced concrete building based on ground acceleration. The strong ground acceleration might cause catasthropic collapse of multi-storey buildings, which leads to casualties and property damages. Therefore, it is imperative to design the multi-storey building against the seismic hazard properly. The seismic-resistant building design process requires structural analysis to be performed to obtain the necessary building responses. Modal response spectrum analysis is performed to simulate ground acceleration and produce structural response data for further use in the ANN. The ANN architecture comprises three layers: an input layer, a hidden layer, and an output layer. Ground acceleration parameters from 34 provinces in Indonesia, soil condition, and building geometry are selected as input parameters. In contrast, structural responses consisting of acceleration, velocity, and displacement (story drift) are selected as output parameters for the ANN; as many as 6345 data sets are used to train the ANN. From the overall data sets, 4590 data sets (72%) are used for the training process, 877 data sets (14%) for the validation process, and 878 data sets (14%) for testing. The trained ANN is capable of predicting structural responses based on ground acceleration at (96%) rate of prediction and the calculated Mean-Squared Errors (MSE) as low as 1.2.10−4. The high accuracy of structural
response prediction can greatly assist the engineer in identifying the building condition rapidly and planning the building maintenance routinely.

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R. Suryanita, H. Maizir and H. Jingga, “Prediction of structural response based on ground acceleration using artificial neural networks,” International Journal of Technology and Engineering Studies, vol. 3, no. 2, pp. 74-83, 2017