A new approach to determine the moment-curvature relationship of circular reinforced concrete columns

Çağlar, Naci; Demir, Aydın; Öztürk, Hakan; Akkaya, Abdulhalim

doi:10.12989/cac.2015.15.3.321

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…It was updated last in 2018. In the literature, there is an excessive number of studies investigating the seismic performance of the buildings designed according to TSCB-2018 (Caglar, 2015;Ozturk et al, 2017;Buzuki, 2019;Kurkcu, 2019;Zolmaz, 2019).…”

Section: Introductionmentioning

confidence: 99%

Progressive Collapse Evaluation of Low-Rise Reinforced Concrete Buildings Designed for Different Occupancy Classes

Demir¹

2022

JCEU

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The study aims to investigate the progressive collapse behavior of low-rise reinforced concrete buildings designed for different occupancy classes. For this objective, two low-rise reinforced concrete framed buildings were designed independently according to the Turkish Seismic Code for Buildings by considering the Residential Occupancy Class and Government Buildings Occupancy Class. A nonlinear dynamic analysis method was employed to evaluate the progressive collapse response of the buildings by using the alternate path direct design approach of UFC 4-023-03 and GSA-2016 guidelines. Three-dimensional finite element models were created for the analyses, and fiber hinges were used to represent the nonlinear behavior of the load-bearing members. Three column loss scenarios were implemented independently. The analysis results show that the residual displacement of the residential building is higher than that of the government building in all column removal scenarios. Moreover, the damage conditions of the residential building are commonly worse than the government building for all column loss scenarios. It is deduced from the study that the buildings experienced the most severe local damage, disproportionate to the initial failure, under the inner column loss scenario

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Section: Introductionmentioning

confidence: 99%

Progressive Collapse Evaluation of Low-Rise Reinforced Concrete Buildings Designed for Different Occupancy Classes

Demir¹

2022

JCEU

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“…For example, Jadid and Fairbairn 14 investigated the role of simple artificial neural network (ANN) architecture (with only one hidden layer) to assist experimental work for estimating yield moment and curvature (i.e., output variables) of a rectangular RC beam section. Caglar et al 15 applied ANN to yield and ultimate curvature estimates of circular RC columns, but the input variables in their ANN model are limited to external vertical loads, column diameter, and longitudinal and transverse reinforcement ratios, while other critical variables such as concrete and rebar strength as well as cover thickness are not considered. Therefore, the application scope of this model is limited.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven probabilistic curvature capacity modeling of circular RC columns facilitating seismic fragility analyses of highway bridges

Wang

Yuan

Feng

et al. 2022

Earthquake Engineering and Resilience

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The availability of reliable probabilistic capacity models of reinforced concrete (RC) columns is a cornerstone for high-confidence seismic fragility and risk analyses of highway bridges. Existing studies often perform physics-based pushover or moment-curvature analyses for the capacity modeling of RC columns, which may encounter nonconvergent problems under high levels of nonlinearities in structural material constitutive models and elements, and become computationally inefficient especially when the analysis model contains plenty of cases involving multisource uncertainties. To mitigate the nonconvergent issues as well as release the computational burden of RC column capacity estimates, this study explores the potency of artificial neural network for data-driven probabilistic curvature capacity modeling of circular RC columns, which can facilitate seismic fragility assessment of highway bridges. To this end, a large database is developed by fiber-section-based moment-curvature analyses covering major ranges of concrete and steel strengths, reinforcement ratios, vertical loads, and geometries of RC columns in engineering practices. To obtain an accurate data-driven model, a fivefold cross-validation training and test process is performed to optimize the neural network architecture. The optimized neural network leads to a reliable datadriven model for estimating multilevel curvature capacity indices with percentage errors less than 15%. Finally, a typical highway bridge is taken as a case study to demonstrate the applicability of the developed data-driven capacity model for the expediency of seismic fragility analysis. For ease of implementation, the database and associated codes are available at https://bit. ly/3A1dh1V.

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“…Betonarme elemanların davranışlarını anlayabilmek için kesitsel davranışın iyi bilinmesi gerekir. Kesitsel davranış momenteğrilik ilişkisi ile değerlendirilebilir [5]. Basit eğilme için moment-eğrilik ilişkisi iyi çalışılmış bir konudur ve klasik moment-eğrilik diyagramı literatürde yaygın olarak bulunmaktadır [6].…”

Section: Introductionunclassified

Sargı Donatısı ve Eksenel Yük Seviyesinin Betonarme Kolonların Eğrilik Süneklik ile Etkin Kesit Rijitliğe Etkisi

Foroughi¹,

Jamal²,

Yüksel³

2020

El-Cezeri Fen Ve Mühendislik Dergisi

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Betonarme elemanların eğrilik sünekliğinin doğru tahmini, sismik yükler altında binaların kapasitesinin güvenilir bir tahminini sağladığı için her zaman çekici bir çalışma konusu olmuştur. Bu çalışmada; malzeme modelinin, eksenel yükün ve sargı donatı oranının betonarme kolonların davranışı üzerindeki etkisi analitik olarak araştırılmıştır. Kare kesitli kolon modelleri tasarlanmıştır. Kolonların davranışı, malzemelerin doğrusal olmayan davranışı dikkate alınarak moment-eğrilik ilişkisinden değerlendirilmiştir. Betonarme kolonların farklı eksenel yük seviyeleri ve sargı donatı oranları için moment eğrilik ilişkileri Mander modeli dikkate alınarak elde edilmiştir. Parametrelerin kolon davranışı üzerindeki incelenen etkileri kesitin mukavemeti, süneklik ve etkin rijitlik açısından değerlendirilmiştir. Tasarlanan betonarme kolon kesitlerde, farklı parametrelerin moment eğrilik ilişkisi, sünekliği ve etkin rijitlik üzerindeki etkisi hesaplanarak karşılaştırılmıştır. Eksenel yük ve sargı donatı oranının değişiminin, betonarme kolonların moment-eğrilik davranışı üzerinde önemli bir etkiye sahip olduğu gözlenmiştir. Çatlamış kesitin etkin rijitliği sabit olmasa da sargı donatı oranı ve kesit üzerine etki eden eksenel kuvvet gibi parametrelere bağlı olarak değişmektedir.

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A new approach to determine the moment-curvature relationship of circular reinforced concrete columns

Cited by 7 publications

References 10 publications

Progressive Collapse Evaluation of Low-Rise Reinforced Concrete Buildings Designed for Different Occupancy Classes

Progressive Collapse Evaluation of Low-Rise Reinforced Concrete Buildings Designed for Different Occupancy Classes

Data‐driven probabilistic curvature capacity modeling of circular RC columns facilitating seismic fragility analyses of highway bridges

Sargı Donatısı ve Eksenel Yük Seviyesinin Betonarme Kolonların Eğrilik Süneklik ile Etkin Kesit Rijitliğe Etkisi

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