Behavior of GFRP-RC Interior Slab-Column Connections with Shear Studs and High-Moment Transfer

Gouda, A. Thimmana; El-Salakawy, Ehab

doi:10.1061/(asce)cc.1943-5614.0000663

Cited by 26 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To ensure the accuracy of predicted results, a database with adequate samples is required for model training. In this paper, 121 groups of experimental results [6][7][8][9][10]12,[31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49] of FRP reinforced concrete slabs under punching shear tests were collected, and made it into a database. In the data set, 80% of the data was used as a training set (10% of this was used as validation set), and remaining 20% were used as test set.…”

Section: Experimental Database Of Frp Reinforced Concrete Slabmentioning

confidence: 99%

Interpretable Machine Learning Models for Punching Shear Strength Estimation of FRP Reinforced Concrete Slabs

2022

View full text Add to dashboard Cite

Fiber reinforced polymer (FRP) serves as a prospective alternative to reinforcement in concrete slabs. However, similarly to traditional reinforced concrete slabs, FRP reinforced concrete slabs are susceptible to punching shear failure. Accounts of the insufficient consideration of impact factors, existing empirical models and design provisions for punching strength of FRP reinforced concrete slabs have some problems such as high bias and variance. This study established machine learning-based models to accurately predict the punching shear strength of FRP reinforced concrete slabs. A database of 121 groups of experimental results of FRP reinforced concrete slabs are collected from a literature review. Several machine learning algorithms, such as artificial neural network, support vector machine, decision tree, and adaptive boosting, are selected to build models and compare the performance between them. To demonstrate the predicted accuracy of machine learning, this paper also introduces 6 empirical models and design codes for comparative analysis. The comparative results demonstrate that adaptive boosting has the highest predicted precision, in which the root mean squared error, mean absolute error and coefficient of determination of which are 29.83, 23.00 and 0.99, respectively. GB 50010-2010 (2015) has the best predicted performance among these empirical models and design codes, and ACI 318-19 has the similar result. In addition, among these empirical models, the model proposed by El-Ghandour et al. (1999) has the highest predicted accuracy. According to the results obtained above, SHapley Additive exPlanation (SHAP) is adopted to illustrate the predicted process of AdaBoost. SHAP not only provides global and individual interpretations, but also carries out feature dependency analysis for each input variable. The interpretation results of the model reflect the importance and contribution of the factors that influence the punching shear strength in the machine learning model.

show abstract

Section: Experimental Database Of Frp Reinforced Concrete Slabmentioning

confidence: 99%

Interpretable Machine Learning Models for Punching Shear Strength Estimation of FRP Reinforced Concrete Slabs

2022

View full text Add to dashboard Cite

show abstract

“…An extensive literature survey of punching shear experimental programs for FRP-RC slabs performed between 1995-2019 was conducted. A total of 197 punching shear tests were collected from 41 studies (listed and cited in Appendix A Table A1 , Table A2 [23,[41][42][43][44][45][46][47][48][49], Table A3 [50], and Table A4 [23,41,46,47]). Out of the 197 tests, 155 specimens were subjected to concentric loading and 42 specimens were subjected to eccentric loading simulating the addition of an unbalanced moment.…”

Section: Punching Shear Databasementioning

confidence: 99%

Punching Shear of FRP-RC Slab–Column Connections: A Comprehensive Database

Almomani,

Alawadi,

Tarawneh

et al. 2024

J. Compos. Sci.

View full text Add to dashboard Cite

Several design standards have been developed in the last two decades to estimate the punching capacity of two-way reinforced concrete (RC) slabs reinforced with fiber-reinforced polymer (FRP) reinforcement. FRP-RC design standards include the recently published ACI 440.11-22, CSA/S806-12, and JSCE-2007. These models are either based on empirical data or semi-empirical methods and calibrated using different databases. Additionally, these standards do not have provisions for connections with shear reinforcement. Therefore, a reliable worldwide database for developing and assessing the applicability of such provisions with test results is vital. This study presents a worldwide and up-to-date database for punching shear of FRP-RC slabs. The database includes 197 tested connections, comprising interior and edge connections, with and without shear reinforcement, and a wide range of materials and cross-sectional properties. The database was used to evaluate the accuracy of the mentioned standards in predicting the punching shear capacity. For connections without shear reinforcement, it was determined that the three design standards yielded similar performance with different conservatism levels. ACI 440.11-22 yielded the most conservative results, with average Vexp/Vpred ratios of 2.04 compared to 1.28 and 1.3 for other models. For connection with shear reinforcement, specimens with Evf> 100 GPa resulted in Vexp/Vpred ratios less than 1.0 for ACI and CSA standards.

show abstract

“…Recent studies [4][5][6] investigated the punching shear behavior of interior FRP-RC slab-column connections with various forms of FRP shear reinforcement under monotonic load. Similarly, few researchers [3,[7][8][9] studied the efectiveness and contribution of GFRP stirrups as shear reinforcement in two-way slabs under lateral cyclic load.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous studies [1][2][3][4][5][6][7][8][9] investigated the punching shear behavior of GFRP-reinforcedslab-column connections at ambient temperatures, and there is perceived void in the article on post-earthquake fre punching shear behavior of GFRP-reinforcedslab-column connections. Secondary fre loading followed by an earthquake action poses a major threat to structural integrity of a member and needs in detail study as a structural member's singular response for only fre, and only earthquake is diferent as compared to combined post-earthquake fre scenario [10].…”

Section: Introductionmentioning

confidence: 99%

Post-Earthquake Fire Punching Shear Behavior of GFRP-Reinforced Slab-Column Connections

Mohammed

Shirtaga

2023

Advances in Civil Engineering

View full text Add to dashboard Cite

It is not uncommon for reinforced concrete slab-column assembly to experience a punching brittle shear failure type under seismic loading. This research work numerically investigates the punching shear behavior of interior GFRP-reinforcedslab-column connections under post-earthquake fire. Two experimental results reported in the article were used for validation analysis using ANSYS nonlinear finite element software program. Further, parametric studies on influential variables such as fire exposure duration, concrete grade, slab thickness, fire exposed surface, and column aspect ratio were performed to get insight into the post-earthquake fire behavior of GFRP-reinforcedslab-column connections. Finite element analysis results showed decreasing fire exposure duration, enhancing grades of concrete, increasing slab thickness, minimizing fire exposure surfaces, and increasing column aspect ratio, which improved the punching shear capacity of GFRP-reinforced slab columns. Moreover, increasing slab thickness provided good performance against post-earthquake fire loading as compared to enhancing concrete grades in GFRP-reinforcedslab-column assemblies. The research concludes that it is crucial to take post-earthquake fire into account when designing structures to ensure no collapse safety design requirements.

show abstract

Behavior of GFRP-RC Interior Slab-Column Connections with Shear Studs and High-Moment Transfer

Cited by 26 publications

References 3 publications

Interpretable Machine Learning Models for Punching Shear Strength Estimation of FRP Reinforced Concrete Slabs

Interpretable Machine Learning Models for Punching Shear Strength Estimation of FRP Reinforced Concrete Slabs

Punching Shear of FRP-RC Slab–Column Connections: A Comprehensive Database

Post-Earthquake Fire Punching Shear Behavior of GFRP-Reinforced Slab-Column Connections

Contact Info

Product

Resources

About