Prediction of spectral acceleration of a light structure with a flexible secondary system using artificial neural networks

Challagulla, S.P.; Parimi, Chandu; Anmala, Jagadeesh

doi:10.1504/ijstructe.2020.109857

Cited by 9 publications

(2 citation statements)

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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%

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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“…As a result, this study proposed a prediction model for the CDAF spectrum based on data-driven methods. Datadriven methods like Machine learning (ML) techniques are superior in the establishment of relations between various input and output variables than conventional regression analysis [10,[30][31][32]. To be more specific, an ML model including Artificial Neural Network (ANN) was utilized to develop the CDAF spectra.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Seismic Design of Non-structural Components Implementing Artificial Intelligence Approach: Predicting Component Dynamic Amplification Factors

Bhavani,

Challagulla,

Noroozinejad Farsangi

et al. 2023

IJE

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The seismic performance of non-structural components (NSCs) has been the focus of intensive study during the last few decades. Modern building codes define design forces on components using too simple relationships. The component accelerates faster than the floor acceleration to which it is connected. Therefore, component dynamic amplification factors (CDAFs) are calculated in this work to quantify the amplification in the acceleration of NSCs for the various damping ratios and tuning ratios of the NSC, and the primary structural periods. From the analysis results, it was observed that CDAF peaks are either underestimated or overestimated by the code-based formulae. A prediction model to ascertain the CDAFs was also developed using artificial neural networks (ANNs). Following that, the suggested model is contrasted with the established relationships from the past research. The ANN model's coefficient of correlation (𝑅) was 0.97. Hence, using an ANN algorithm reduces the necessity of laborious and complex analysis.

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Examining random forests for predicting elastic floor response spectra involving dynamic primary-secondary structure interaction

Latha,

Lingeshwaran,

Challagulla

et al. 2024

J Build Rehabil

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Prediction of spectral acceleration of a light structure with a flexible secondary system using artificial neural networks

Cited by 9 publications

References 0 publications

Application of metaheuristic algorithms in prediction of earthquake peak ground acceleration

Application of metaheuristic algorithms in prediction of earthquake peak ground acceleration

Enhancing Seismic Design of Non-structural Components Implementing Artificial Intelligence Approach: Predicting Component Dynamic Amplification Factors

Examining random forests for predicting elastic floor response spectra involving dynamic primary-secondary structure interaction

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