Estimation of dynamic design parameters for buildings with multiple sliding non-structural elements using machine learning

Challagulla, S.P.; Parimi, Chandu; Pradeep, S.; Farsangi, Ehsan Noroozinejad

doi:10.1504/ijstructe.2021.114262

Cited by 4 publications

(1 citation statement)

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“…To forecast the Peak Ground Acceleration (PGA), a Neural Network based prediction relationship has been generated. Previous research has shown that such relationships can be useful, and numerous studies have found identical ANN-based prediction correlations concerning various problems [35][36][37][38]. In the MATLAB R2019b environment, a feed-forward neural network was built.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Application of metaheuristic algorithms in prediction of earthquake peak ground acceleration

Challagulla,

Suluguru,

Noroozinejad Farsangi

et al. 2023

The Journal of Engineering

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The seismic resilience of a structure has been evaluated using peak ground acceleration (PGA). Ground motion parameters such as source characteristics, local site conditions are used to forecast the PGA of the ground motion. This paper aims to develop an Artificial Neural Network (ANN) based model to predict the PGA. Here, hypocentral distance (Rhypo${R}_{hypo}$), shear wave velocity (Vs30${V}_{s30}$), and moment magnitude (Mw${M}_w$), are used as input parameters. The model uses 12,706 ground motion recordings from 283 earthquakes from the revised NGA‐West2 database supplied by Pacific Engineering Research Centre. Among the whole data, 70% of the data is set for training, 15% for validation, and 15% for testing the network. The R value derived from the testing dataset is 0.952, indicating the excellent performance of a network. An extensive parametric study is conducted with the PGA values, and the results indicate that the PGA increases with the magnitude and decreases with the hypocentral distance. The predicted PGA values from the present study are comparable with those from the existing relationships in the global database. The generated ANN model is further verified by comparing the predicted and recorded PGA values of an actual recorded event.

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Section: Artificial Neural Networkmentioning

confidence: 99%