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

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

Prediction of Structural Response Based on Ground Acceleration using Artificial Neural Networks

This study utilizes Artificial Neural Networks to predict the structural responses multi-story reinforced concrete building based on ground acceleration. The strong ground acceleration might cause the catastrophic collapse of the multi-story building which leads to casualties and property damages. Therefore, it is imperative to properly design the multi-story building against the seismic hazard. 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 of 3 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, whereas structural responses consisting of acceleration, velocity, and displacement (story drift) are selected as output parameters for the ANN. As many as 6345 datasets are used to train the ANN. From the overall datasets, 4590 data sets (72%) are used for training process, 877 data sets (14%) for the validation process, and 878 data sets (14%) for testing. The trained ANN is capable to predict 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 to identify the building condition rapidly and plan the building maintenance routinely

Damage Level Prediction of Reinforced Concrete Building Based on Earthquake Time History Using Artificial Neural Network

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 Networ

Damage Level Prediction of Reinforced Concrete Building Based on Earthquake Time History Using Artificial Neural Network

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 Networ

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

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

Damage Level Prediction of Reinforced Concrete Building Based on Earthquake Time History Using Artificial Neural Network

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 Networ