An improved rapid visual screening method for seismic vulnerability assessment of reinforced concrete buildings in Indian Himalayan region

Aggarwal, Yati; Saha, Sandip Kumar

doi:10.1007/s10518-022-01537-2

Cited by 12 publications

(5 citation statements)

References 22 publications

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“…Many researchers developed methodologies and approaches to study and estimate structures' seismic vulnerability and fragility [5][6][7][8][9][10][11]. The most reliable and accurate approach is the Nonlinear Time History Analysis (NL-THA) [12].…”

Section: Introductionmentioning

confidence: 99%

Seismic response prediction using a hybrid unsupervised and supervised machine learning in case of 3D RC frame buildings

Abdellatif,

Chikh,

Ahmed

2024

Res. Eng. Struct. Mat.

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The seismic vulnerability assessment represents an important step in monitoring the buildings’ capacity and checking their performance during and after earthquake events. The Nonlinear Time History Analysis (NL-THA) is considered the most reliable method that is used to calculate the exact structural behavior of any building. However, this sophisticated method is known for its complexity, the use of Finite Element (FE) software, and computational time consuming, especially in the case of tall buildings. For that reason, An Artificial Neural network (ANN) is used to develop a new model able to predict the essential Engineering Demand Parameters (EDPs), i.e., the Maximum Base Shear (MBS), the Maximum Inter-story Drift (MIDR) and the Maximum Roof Drift Ratio (RDR). Unsupervised algorithms such as the Principal Component Analysis PCA and the Autoencoder are coupled with the ANN to reduce the dimensionality, improve the dataset quality, and reduce the irrelevant features. More than 192,000 buildings are analyzed using the NL-THA and eighty artificial ground motions (GMs) to generate the dataset. The buildings’ characteristics are generated randomly from the selection range. The results showed that the Autoencoder-ANN model represents the highest performance compared to the PCA-ANN and ANN models. The Autoencoder-ANN model could quickly and accurately predict the seismic responses of unseen ground motions using only the building’s characteristics and the GM parameters without using any FE software.

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Section: Introductionmentioning

confidence: 99%

Seismic response prediction using a hybrid unsupervised and supervised machine learning in case of 3D RC frame buildings

Abdellatif,

Chikh,

Ahmed

2024

Res. Eng. Struct. Mat.

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“…These models seek to more accurately predict the behavior of structures during seismic events, considering effects such as the induction of ground displacements and the dynamic interaction between the soil and structure [36,37]. Three-dimensional simulations and nonlinear analysis have provided valuable insights into how these interactions affect the safety and performance of structures during and after seismic events [28,29,[38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Influence of the Plan Structural Symmetry on the Non-Linear Seismic Response of Framed Reinforced Concrete Buildings

Vielma-Quintero,

Diaz-Segura,

Vielma

2024

Symmetry

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Seismic-resistant design incorporates measures to ensure that structures perform adequately under specific limit states, focusing on seismic forces derived from both the equivalent static and spectral modal methods. This study examined buildings on slopes in densely built urban areas, a common scenario in Latin American cities with high seismic risks. The adjustment of high-rise buildings to sloping terrains induces structural asymmetry, leading to plan and elevation irregularities that significantly impact their seismic response. This paper explores the asymmetry in medium-height reinforced concrete frame buildings on variable inclines (0°, 15°, 30°, and 45°) and its effect on their nonlinear response, assessed via displacements, rotations, and damage. Synthetic accelerograms matched with Chile’s high seismic hazard design spectrum, scaled for different performance states and seismic records from the Chilean subduction zone, were applied. The findings highlight structural asymmetry’s role in influencing nonlinear response parameters such as ductility, transient interstory drifts, and roof rotations, and uncover element demand distributions surpassing conventional analysis and in earthquake-resistant design expectations.

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“…There are different methodologies to evaluate the seismic vulnerability index (SVI). One of them is the Rapid Screening Method (RSM) which is considered the first level ( 5), (6), and (7). This method does not need specialized studies to estimate the state of the structure (8).…”

Section: Introductionmentioning

confidence: 99%

Corrosion Effects on the Seismic Vulnerability Index of Reinforced Concrete Scholar Buildings

Colorado¹,

Domínguez²,

Barradas-Hernández³

et al. 2023

ECS Trans.

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The Veracruz - Boca del Río metropolitan area in Mexico presents high temperatures and even high chloride ion concentrations, causing an aggressive environment with significant corrosion rates. Therefore, in reinforced concrete buildings in said area, their service life and integrity can compromise. Rapid Screening Methods (RSM) are used to determine their Seismic Vulnerability Index (SVI). The Benedetti-Petrini RSM can be used to determine the SVI of the corroded structures, adapting how the state of conservation is evaluated and weighted. To that end, the corrosion level and the crack width were correlated to estimate how their state of conservation can be deteriorated, reducing the service life. To test its usefulness, nine faculties, whose ages are close to 50 years, were analyzed, and the SVI of the original and modified methods were compared. Additionally, as expected, the buildings close to the coastline, and the faces exposed to wind direction, were the most damaged. The results indicate that the modified SVI provides rational decisions and improves the action management for each structure.

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An improved rapid visual screening method for seismic vulnerability assessment of reinforced concrete buildings in Indian Himalayan region

Cited by 12 publications

References 22 publications

Seismic response prediction using a hybrid unsupervised and supervised machine learning in case of 3D RC frame buildings

Seismic response prediction using a hybrid unsupervised and supervised machine learning in case of 3D RC frame buildings

Influence of the Plan Structural Symmetry on the Non-Linear Seismic Response of Framed Reinforced Concrete Buildings

Corrosion Effects on the Seismic Vulnerability Index of Reinforced Concrete Scholar Buildings

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