Evaluation of damping modification factors for floor response spectra via machine learning model

Challagulla, S.P.; Bhargav, N.C.; Parimi, Chandu

doi:10.1016/j.istruc.2022.03.071

Cited by 11 publications

(3 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…In the preceding section, it was examined how the damping and mass ratios of the SS affected the FRS for a specific primary structure vibration period (𝑇 𝑝 = 0.5 sec). The dynamic characteristics of the primary structure substantially affect the secondary structure's seismic demands [26], [27]. As a result, an effort has been made to investigate the influence of a PS vibration period on the FRS for a specific mass and damping ratio of the SS in this section.…”

Section: Effect Of Vibration Period Of the Ps On Frsmentioning

confidence: 99%

Numerical analysis of the primary and secondary structural dynamic interaction effects on elastic floor response spectra

Annamdasu,

Challagulla,

Pesaralanka

et al. 2023

Res. Eng. Struct. Mat.

View full text Add to dashboard Cite

In modern seismic design, the assessment of seismic behavior in secondary structures relies on the evaluation of the primary structure's acceleration at the support of the secondary structure. To enable effective secondary structure design, a thorough understanding of the interaction between the primary and secondary structures is essential. This article conducts an analysis based on parametric data, delving into the dynamic interaction between these structures. In this study, both the elastic primary and secondary structures are represented as single-degree-of-freedom systems. The governing equations of motion for both the coupled and uncoupled systems are derived and solved using the numerical method. Subsequently, the floor response spectrum (FRS) is computed for both coupled and uncoupled configurations. This investigation focuses on the impact of three crucial parameters: the tuning ratio (T_r), the mass ratio (μ), and the damping ratio (ξ_s) on the FRS. The analytical findings reveal that dynamic interaction does not significantly affect the FRS when the mass ratio is very low, at 0.1%. However, for a range of 0.8 ≤ T_r ≤ 1.2, dynamic interaction has a substantial influence on the FRS. Additionally, this study underscores that lower damping ratios in the secondary structure result in a more pronounced coupling effect on the FRS.

show abstract

“…The floor response spectrum (FRS) method is an analytical approach that operates by separating various considerations [7][8][9][10][11][12][13]. Initially, the primary structure undergoes dynamic analysis independently, without factoring in the secondary system's influence.…”

Section: Introductionmentioning

confidence: 99%

Investigation on floor response spectra of a three-story building exposed to near- and far-field earthquakes

Pesaralanka,

Burugapalli,

Challagulla

2024

Res. Eng. Struct. Mat.

View full text Add to dashboard Cite

Historical earthquakes highlighted structural collapses and non-structural component (NSC) failures. This study aims to analyze NSC behavior during near and far-field earthquakes in a three-story building by studying elastic and inelastic acceleration responses. The investigation delves into floor response spectra and analyzes how NSCs affect floor acceleration responses, aiming to understand the performance of these components. The findings from the analysis indicated that the Floor Response Spectra (FRS) consistently align peaks with elastic and inelastic modal periods. Inelastic FRS notably show reduced floor spectral accelerations compared to elastic FRS. Near-field earthquakes induce 25-30% higher floor acceleration demands in NSCs compared to far-field earthquakes. Peak Component Acceleration (PCA) values differ significantly between near and far field earthquakes, with near-field ones exhibiting notably higher values across all floors. Higher damping ratios in NSCs lead to decreased peaks in the Component Dynamic Amplification Factor (CDAF) spectrum. The inelastic model notably reduces peak values of CDAF by approximately 49.53% to 69.3% for near-field and 51.81% to 64.47% for far-field earthquakes compared to the elastic model. In summary, the examination of peak floor responses against the formulation based on building codes highlights variations, with instances where the formulation either underestimates or overestimates the peak response demands.

show abstract

Evaluation of damping modification factors for floor response spectra via machine learning model

Cited by 11 publications

References 53 publications

Application of metaheuristic algorithms in prediction of earthquake peak ground acceleration

Application of metaheuristic algorithms in prediction of earthquake peak ground acceleration

Numerical analysis of the primary and secondary structural dynamic interaction effects on elastic floor response spectra

Investigation on floor response spectra of a three-story building exposed to near- and far-field earthquakes

Contact Info

Product

Resources

About