Seismic performance evaluation of masonry infill R.C. frame considering soil-structure interaction

Karki, Prajwol; Pyakurel, Shuvam; Utkarsh, Kumar

doi:10.1007/s41062-022-00979-6

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Soil-structure interaction is an interdisciplinary issue that has been addressed since the end of the 19th century [13]. Evaluation of the soil-structure interaction while monitoring the building's response is important, as the material properties of the underlying soil can change the ground motion at the soil-foundation interface [14]. Therefore, it is necessary to know the soil and its properties for all structures.…”

Section: Introductionmentioning

confidence: 99%

Use of Machine Learning Techniques in Soil Classification

et al. 2023

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In the design of reliable structures, the soil classification process is the first step, which involves costly and time-consuming work including laboratory tests. Machine learning (ML), which has wide use in many scientific fields, can be utilized for facilitating soil classification. This study aims to provide a concrete example of the use of ML for soil classification. The dataset of the study comprises 805 soil samples based on the soil drillings of the new Gayrettepe–Istanbul Airport metro line construction. The dataset has both missing data and class imbalance. In the data preprocessing stage, first, data imputation techniques were applied to deal with the missing data. Two different imputation techniques were tested, and finally, the data were imputed with the KNN imputer. Later, a balance was achieved with the synthetic minority oversampling technique (SMOTE). After the preprocessing, a series of ML algorithms were tested with 10-fold cross-validation. Unlike the studies conducted in previous research, new gradient-boosting methods such as XGBoost, LightGBM, and CatBoost were tested, high classification accuracy rates of up to +90% were observed, and a significant improvement in the accuracy of prediction (when compared with previous research) was achieved.

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Section: Introductionmentioning

confidence: 99%

Use of Machine Learning Techniques in Soil Classification

et al. 2023

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Enhancing Seismic Performance: A Comprehensive Study on Masonry and Reinforced Concrete Structures Considering Soil Properties and Environmental Impact Assessment

Labar,

Bektaş,

Kegyes-Brassai

2024

Advances in Civil Engineering

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Approximately 20,000 people are killed annually on average by building and infrastructure collapses and failures caused by seismic activities. In earlier times, seismic design codes and specifications set minimal requirements for life safety performance levels. Earthquakes can be thought of as recurring events in seismically active areas, with severity states ranging from serviceability to ultimate levels. Buildings designed in accordance with site-specific response spectra, which take into account soil properties based on ground motion amplification data, are better at withstanding such forces and serving their design purposes. This study aims to investigate the site response of reinforced and masonry buildings, considering the effect of soil properties based on the amplification of ground motion data, and to compare the life cycle assessment of the buildings under consideration based on the design and the site-specific response spectrum. In terms of soil properties and site-specific response spectra, STRATA is used to determine the site-specific response for the considered locations for a return period of 475 years for 100 realizations based on the randomization of site properties. For structural analysis, AxisVM software, which is a compatible finite element analysis, is used for building design and analysis, generating comparative results based on the design- and site-specific spectra. To determine and identify potential failures in the model, response spectra were applied to understand the difference in horizontal deflection in two different instances (for elastic design- and site-specific spectra). After building design and analysis is performed, a life cycle analysis in terms of environmental impact assesments using OpenLCA and IdematLightLCA is done. This is done to ascertain the additional expenses in terms of ecocosts and carbon footprints on some failed elements in the structure which are required to make the buildings more resilient when the site-specific response spectrum is applied and to compare the potential economic losses that may occur based on ecological costs. The study presents a comprehensive investigation into the seismic response of masonry and reinforced concrete buildings in Győr, Hungary, incorporating advanced geophysical techniques like multichannel surface wave (MASW) and structural analysis software, AxisVM. Additionally, tailored retrofitting strategies are explored to enhance structural resilience in seismic-prone regions. Significant ground amplifications in soil properties across different profiles are revealed, emphasizing the effectiveness of these strategies in reducing structural deflection and improving resilience. Highlights of the results are observed where the site-specific response spectra are higher than the EC8 design response spectrum. Furthermore, the research underscores the substantial environmental impact, considering both ecocosts and CO2 emissions associated with retrofitting measures, highlighting the importance of sustainable structural interventions in mitigating seismic risks.

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Seismic performance evaluation of masonry infill R.C. frame considering soil-structure interaction

Cited by 2 publications

References 19 publications

Use of Machine Learning Techniques in Soil Classification

Use of Machine Learning Techniques in Soil Classification

Enhancing Seismic Performance: A Comprehensive Study on Masonry and Reinforced Concrete Structures Considering Soil Properties and Environmental Impact Assessment

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