Punching Shear Strengths of RC Slab-Column Connections: Prediction and Reliability

Chetchotisak, Panatchai; Ruengpim, Pongpan; Chetchotsak, Danaipong; Yindeesuk, Sukit

doi:10.1007/s12205-017-0456-6

Cited by 24 publications

(11 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results suggested that ANN could outperform the available design code equations (e.g., the ACI 318-08 [16] equation) when comparing prediction accuracy. Chetchotisak et al [17] collected 342 experimental results to estimate the punching shear capacity of the slab-column connections using multiple linear regression (MLR) and ANN models. Results showed that the MLR approach exhibits higher accuracy and simplicity in the prediction of PSS.…”

Section: Figure 1 Punching Shear Failure In Slab-column Connectionsmentioning

confidence: 99%

Ensemble Learning Models for Prediction of Punching Shear Strength in RC Slab-Column Connections

Habibi,

Youssef,

Naseri

et al. 2024

Civ Eng J

View full text Add to dashboard Cite

In reinforced concrete (RC) structures, accurate prediction of the punching shear strength (PSS) of slab-column connections is imperative for ensuring safety. The existing equations in the literature show variability in defining parameters influencing PSS. They neglect potential variable interactions and rely on a limited dataset. This study aims to develop an accurate and reliable model to predict the PSS of slab-column connections. An extensive dataset, including 616 experimental results, was collected from earlier studies. Six robust ensemble machine learning techniques—random forest, gradient boosting, extreme gradient boosting, adaptive boosting, gradient boosting with categorical feature support, and light gradient boosting machines—are employed to predict the PSS. The findings indicate that gradient boosting stands out as the most accurate method compared to other prediction models and existing equations in the literature, achieving a coefficient of determination of 0.986. Moreover, this study utilizes techniques to explain machine learning predictions. A feature importance analysis is conducted, wherein it is observed that the reinforcement ratio and compressive strength of concrete demonstrate the highest influence on the PSS output. SHapley Additive exPlanation is conducted to represent the influence of variables on PSS. A graphical user interface for PSS prediction was developed for users’ convenience. Doi: 10.28991/CEJ-SP2024-010-01 Full Text: PDF

show abstract

Section: Figure 1 Punching Shear Failure In Slab-column Connectionsmentioning

confidence: 99%

Ensemble Learning Models for Prediction of Punching Shear Strength in RC Slab-Column Connections

Habibi,

Youssef,

Naseri

et al. 2024

Civ Eng J

View full text Add to dashboard Cite

show abstract

“…Chetchotisak et al [27] presented a distinct approach for calculating the punching shear strength (V c ) in RC slab-column connections without shear reinforcement. They gathered data from 342 slab-column connections and used multiple linear regression to develop a model in logarithmic space.…”

Section: Design Code and Empirical Formulasmentioning

confidence: 99%

The Machine-Learning-Based Prediction of the Punching Shear Capacity of Reinforced Concrete Flat Slabs: An Advanced M5P Model Tree Approach

Abdallah,

Thoeny,

Henedy

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Reinforced concrete (RC) flat slabs are widely employed in modern construction, and accurately predicting their load-carrying capacity is crucial for ensuring safety and reliability. Existing design methods and empirical equations still exhibit discrepancies in determining the ultimate load capacity of flat slabs. This study aims to develop a robust machine learning model, specifically the M5P model tree, for predicting the punching shear capacity of a RC flat slab without shear reinforcement. A comprehensive dataset of 482 experimentally tested flat slabs without shear reinforcement was gathered through an extensive literature review and utilized for the development of the M5P model. The model takes into account influential parameters, such as slab thickness, longitudinal reinforcement ratios, and concrete strength. The performance of the proposed M5P model was compared with existing design codes and other empirical models. The comparison highlights that the developed M5P model tree provides a more accurate and reliable prediction of the punching shear capacity of RC flat slabs. This study contributes to the advancement of structural engineering knowledge and has the potential to improve the design and safety assessment of concrete flat slab structures.

show abstract

“…In recent decades, extensive numerical and experimental investigations have focused on the performance of slab-column connections and their strengthening using carbon-fiber-reinforced polymer (CFRP) (Abdulrahman and Aziz, 2021;Al-Rousan et al, 2021;El-Mandouh et al, 2022;Akhundzada et al, 2019). Researchers have explored retrofitting externally adhered CFRP plates in RC structures (Amoozadeh et al, 2024;Bindurani et al, 2023;Chetchotisak et al, 2018;Al-Mawed & Hamad 2023), especially in RC slabs with or without openings under bending loads.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of CFRP Plates For Strengthening RC Flat Slab-Column Connections: Parametric Study and GWO-Based Optimization

Kohneposhi,

Azizian,

Haddad

et al. 2024

Preprint

View full text Add to dashboard Cite

The present study assesses the efficacy of carbon-fiber-reinforced polymer (CFRP) plates in enhancing the strengthening of reinforced concrete (RC) flat slab-column connections under both punching shear and seismic loading conditions. A comprehensive parametric analysis is conducted using the Abaqus finite element software, encompassing 33 distinct models with variations in key parameters including loading conditions, number of fiber layers, orientation, thicknesses, and dimensions of the CFRP plate. The numerical model is validated through experimental testing of a laboratory-scale prototype, demonstrating a mean error of 5.2% relative to experimental data, indicating satisfactory accuracy. Furthermore, an optimal design approach for CFRP plates is developed employing the gray wolf optimization (GWO) algorithm. The primary objective of this optimization framework is to mitigate damage in the slab-column connection under loading. Computational simulations are performed simultaneously in Abaqus and MATLAB to determine the optimal geometric parameters of CFRP for retrofitting the connection. The results demonstrate that CFRP plates with optimal parameters can reduce more than 90% of the damage incurred at the slab-column connection. The results, together with the CFRP optimization model developed using GWO, can be practically utilized for the optimal design of CFRP plates to reinforce structures.

show abstract

Punching Shear Strengths of RC Slab-Column Connections: Prediction and Reliability

Cited by 24 publications

References 16 publications

Ensemble Learning Models for Prediction of Punching Shear Strength in RC Slab-Column Connections

Ensemble Learning Models for Prediction of Punching Shear Strength in RC Slab-Column Connections

The Machine-Learning-Based Prediction of the Punching Shear Capacity of Reinforced Concrete Flat Slabs: An Advanced M5P Model Tree Approach

Optimal Design of CFRP Plates For Strengthening RC Flat Slab-Column Connections: Parametric Study and GWO-Based Optimization

Contact Info

Product

Resources

About