The influence of concrete compressive strength on the punching shear capacity of reinforced concrete flat slabs under different opening configurations and loading conditions

Al-Rousan, Rajai Z.; Alnemrawi, Bara’a R.

doi:10.1016/j.istruc.2022.07.091

Cited by 18 publications

(8 citation statements)

References 23 publications

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“…For the flat slabs, punching failure can be predicted more precisely by using compressive strength ( f c ) [57], as illustrated in Figure 23. It has been reported by numerous research studies [6,59,60] that the compressive strength of concrete has a profound influence on the punching shear of flat slab. The results of the PSOFNN and ANN are close to the experimental results; therefore, the scatter of the points is close to the unity line, while the scatter of VEXP/VACI and VEXP/VEC2 against the unity line show that ACI underestimate and EC2 overestimate the values that are in agreement with the previous findings [55].…”

Section: Compressive Strength ( F C )mentioning

confidence: 99%

“…Due to this failure, the loads are redistributed to the adjacent structural elements leading to a progressive failure. Determination of punching shear failure of the flat slab is a complex task due to numerous factors and may be caused due to large column loads, insufficient concrete strength [6], inadequate slab thickness [7], insufficient shear reinforcement [8], small column heads, and poor construction quality. Numerous experimental studies have been conducted to assess the performance of flat slabs.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab–Column Connections

Wahab,

Mahmoudabadi,

Waqas

et al. 2024

Advances in Civil Engineering

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This research focuses on a comprehensive comparative analysis of shear strength prediction in slab–column connections, integrating machine learning, design codes, and finite element analysis (FEA). The existing empirical models lack the influencing parameters that decrease their prediction accuracy. In this paper, current design codes of American Concrete Institute 318-19 (ACI 318-19) and Eurocode 2 (EC2), as well as innovative approaches like the compressive force path method and machine learning models are employed to predict the punching shear strength using a comprehensive database of 610 samples. The database consists of seven key parameters including slab depth (ds), column dimension (cs), shear span ratio (av/d), yield strength of longitudinal steel (fy), longitudinal reinforcement ratio (ρl), ultimate load-carrying capacity (Vu), and concrete compressive strength (fc). Compared with the design codes and other machine learning models, the particle swarm optimization-based feedforward neural network (PSOFNN) performed the best predictions. PSOFNN predicted the punching shear of flat slab with maximum accuracy with R2 value of 99.37% and least MSE and MAE values of 0.0275% and 1.214%, respectively. The findings of the study are validated through FEA of slabs to confirm experimental results and machine learning predictions that showed excellent agreement with PSOFNN predictions. The research also provides insight into the application of metaheuristic models along with ANN, revealing that not all metaheuristic models can outperform ANN as usually perceived. The study also highlights superior predictive capabilities of EC2 over ACI 318-19 for punching shear values.

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Section: Compressive Strength ( F C )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab–Column Connections

Wahab,

Mahmoudabadi,

Waqas

et al. 2024

Advances in Civil Engineering

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“…However, as building designs evolve towards larger spans, traditional solid slab systems necessitate thicker slabs. This translates to heavier structures requiring increased beam and column dimensions, leading to a number of drawbacks [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…Although void slabs offer benefits such as reduced self-weight, environmental friendliness, and potential solutions to societal issues, they are susceptible to greater vulnerability to shear forces compared to solid slabs [5]. The reduction in shear force resistance in void slabs is attributed to the removal of abdominal concrete, which does not compromise bending resistance but introduces cross-sectional defects that diminish shear force resistance [2,6,8,9,11,14]. Moreover, shear failure, induced by shear force, poses challenges in failure prediction and user safety due to its inherently brittle nature, contrasting with the gradual failure characteristic of flexural failure [32].…”

Section: Introductionmentioning

confidence: 99%

Shear Reinforcement Effectiveness of One-Way Void Slab with the Hollow Core Ratio and Shear Reinforcement

Cho,

Na,

2024

Applied Sciences

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Void slabs offer a promising solution for sustainable construction due to their reduced weight and potential for recycled materials. However, their inherent hollowness can compromise shear capacity compared to solid slabs. This study investigates the effectiveness of shear reinforcement in mitigating this vulnerability. Experimental testing with a four-point support loading confirmed shear failure in all specimens and revealed a significant reserve of shear strength exceeding predictions from ACI 318-14 by at least 1.436. This suggests the potential for more efficient designs that utilize less shear reinforcement while maintaining structural integrity. An inverse relationship between porosity and shear strength was observed, highlighting the importance of considering void content during design. Among established design codes (ACI 318-14, UBC 2, and CEB-FIP 1990), CEB-FIP 1990 provided the most accurate prediction of shear capacity for these reinforced hollow slabs. These findings offer valuable insights for optimizing the shear design of voided slabs. The observed strength reserve suggests the potential for reduced shear reinforcement while maintaining safety. Additionally, the influence of porosity and the code comparison provide crucial considerations for future design practices. This research paves the way for developing efficient and safe voided slab applications, promoting sustainability in the construction industry.

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“…Furthermore, this study aims to develop and compare finite-element method (FEM) simulations with ML models for predicting PSS. FEM, a widely used computational technique in structural engineering, has long been employed for analysing complex physical phenomena [16][17][18][19]. By contrasting these computational approaches, this research intends to offer valuable insights into the advantages and disadvantages of each method and identify the most suitable technique for practical applications.…”

Section: Introductionmentioning

confidence: 99%

Low-Code Application and Practical Implications of Common Machine Learning Models for Predicting Punching Shear Strength of Concrete Reinforced Slabs

Nguyen,

Do,

Nguyen

et al. 2023

Advances in Civil Engineering

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This paper investigates the effectiveness of machine learning (ML) models available in MATLAB Regression Learner app and MATLAB App Designer, both low-code applications, for accurately predicting punching shear strength (PSS) in reinforced concrete (RC) slabs. A database of 379 RC slab samples without transverse reinforcement was compiled from renowned publications. RandomSearch and Bayesian optimisation were employed for tuning hyperparameters. The performance of these models was compared with six empirical models, which included three current design codes, three equations from other researchers, and 227 finite-element simulations conducted by the authors. The ML models and finite-element method (FEM) demonstrated superior performance compared with the literature and practical codes. Furthermore, the results emphasised the exceptional performance of the Gaussian process regression (GPR) with optimised hyperparameters, exhibiting the best performance in validation, training, and testing datasets with R2 values of 0.95, 0.99, and 0.98, respectively. A user-friendly standalone application was developed, providing real-time predictions of the PSS using the two best-developed ML models, GPR and support vector machine (SVM), as well as six empirical models from the literature. This tool offers users flexibility in choosing the most appropriate model for their specific needs, delivering reliable, and accurate results for estimating the PSS of RC slabs.

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The influence of concrete compressive strength on the punching shear capacity of reinforced concrete flat slabs under different opening configurations and loading conditions

Cited by 18 publications

References 23 publications

Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab–Column Connections

Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab–Column Connections

Shear Reinforcement Effectiveness of One-Way Void Slab with the Hollow Core Ratio and Shear Reinforcement

Low-Code Application and Practical Implications of Common Machine Learning Models for Predicting Punching Shear Strength of Concrete Reinforced Slabs

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