Computational intelligence-based models for estimating the fundamental period of infilled reinforced concrete frames

M Mirrashid, H Naderpour - Journal of Building Engineering, 2022 - Elsevier
One of the most important parameters used in the frame design process is the fundamental
period. Numerous relationships are provided in the regulations and articles to determine the …

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

SP Challagulla, NC Bhargav, C Parimi - Structures, 2022 - Elsevier
In the last few decades, the seismic performance of Non-structural components (NSCs) has
been a subject of extensive research. The dynamic behavior of NSCs, can cause damage to …

Artificial neural network-based prediction model of elastic floor response spectra incorporating dynamic primary-secondary structure interaction

ML Annamdasu, SP Challagulla, DPN Kontoni… - Soil Dynamics and …, 2024 - Elsevier
The evaluation of the Floor Response Spectrum (FRS) holds paramount significance in
assessing the seismic behavior of secondary structures. Precise FRS prediction empowers …

Predicting the maximum seismic response of the soil-pile-superstructure system using random forests

X Zhang, J Chen, Y Wu, L Tang… - Journal of Earthquake …, 2022 - Taylor & Francis
Seismic fragility analysis has been considered an efficient method for seismic performance
assessment of soil-pile-superstructure systems (SPSSs). However, seismic fragility analysis …

Application of metaheuristic algorithms in prediction of earthquake peak ground acceleration

SP Challagulla, AK Suluguru… - The Journal of …, 2023 - Wiley Online Library
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 …

Neural networks-based spring element for second-order analysis of pile-supported structures with nonlinear soil-structure interaction

W Ouyang, L Chen, SW Liu - Engineering Structures, 2024 - Elsevier
This paper proposes a novel structural analysis approach, the neural networks-based spring
element (NNSE) method, to synergize machine learning (ML) techniques with the line finite …

Utilizing Artificial Neural Networks and Random Forests to Forecast the Dynamic Amplification Factors of Non-Structural Components

P Vyshnavi, SP Challagulla, M Adamu, F Vicencio… - Applied Sciences, 2023 - mdpi.com
Soft stories in buildings are well-known to present structural vulnerabilities during seismic
events, and the failure of non-structural components (NSCs) has been evident in past …

A Practical Method for Determining Dynamic Characteristics of Buildings Under the Effect of Foundation Rotations

KB Bozdogan, E Keskin, D Ozturk - International Journal of …, 2024 - World Scientific
In multi-storey buildings, unforeseen foundation rotations can change the building's
behavior. Therefore, these effects should be taken into account in the analysis. In the studies …

Seismic response prediction for buildings with sliding live loads using neural networks

SP Challagulla, F Vicencio, M Jameel… - Structures, 2024 - Elsevier
The seismic response of primary structures (PS) can be significantly influenced by live loads,
particularly when these loads consist of stacked bodies that are capable of sliding during …

A Method for Determination of Moment Contribution Ratio under Foundation Rotation in Shear Wall-Frame Systems

KB Bozdogan, E Keskin - Buildings, 2024 - mdpi.com
In shear wall-frame systems, the foundation rotation that may occur under the shear walls
changes the displacements and interstory drift ratios and changes the internal force …