Artificial Neural Networks for seismic demand prediction of a single  degree of freedom

BENBOKHARI, Abdellatif; BENAZOUZ, Chikh; MEBARKI, Ahmed

doi:10.59287/icaens.995

Cited by 1 publication

(3 citation statements)

References 7 publications

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“…A Backpropagation algorithm was used, and the "Adam" algorithm was selected as an optimization technique. As shown in [11], the method showed remarkable accuracy and simplicity in estimating the MIRs. It can be seen when comparing the generated IDA curves, the median IDA curve, and the fragility curves using 30 unseen AGMs.…”

Section: The Artificial Neural Network Predictionmentioning

confidence: 90%

“…The seismic response is one of the most important indicators in performance evaluation and vulnerability assessment of existing buildings [1] . Many procedures and approaches have been proposed to estimate the seismic demand and response [2] [3] [4] .…”

Section: Introductionmentioning

confidence: 99%

“…The target displacement represents the maximum response Figure 1 The used ANN method for the target dispalcement prediction likely when the building is subjected to a ground motion. FEMA-356 proposes a simplified formula to calculate the target response using some coefficients Ci as shown in equation (1).…”

Section: Introductionmentioning

confidence: 99%

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A comparative study between the ANN-based and the current methods for seismic response estimation in the case of single degree of freedom systems

2023

1st International Conference on Modern and Advanced Research Icmar 2023

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The target displacement is one of the most critical performance indicators in the seismic vulnerability assessment. The Nonlinear Time History Analysis (NL-THA) is the most reliable method for calculating the seismic response of any building by solving the differential equation of motion. However, this procedure is considered time-consuming, and it needs expertise to perform. For that reason, many codes and standards have proposed and adopted various methods and procedures to estimate and predict the seismic response and target displacement. These alternative methods represent some prediction uncertainties. Machine Learning (ML) algorithms became an exciting tool in earthquake engineering due to their performance and prediction simplicity. This paper compares the target displacement prediction of a novel Artificial Neural Networks (ANNs) method to the Displacement coefficient Method (DCM) adopted by FEMA-356, the Modified Coefficient Method (MCM) adopted by FEMA-440, and the NL-THA. The comparison is performed to 10 Single degrees of Freedom (SDOF) with different vibration periods and yielding forces (fy). The ANN model uses the SDOF characteristics and the ground motion (GM) parameters to estimate the maximum inelastic response. The results show a high performance of the ANNbased method in terms of Mean squared Errors (MSE), Mean Relative Error (MRE), and Mean Absolute Error (MAE).

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Section: The Artificial Neural Network Predictionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%