This paper examines the structural response of reinforced concrete flat slabs,provided with flillyembedded shear-heads,through detailed three-dimensional nonlinear numerical simulations and parametric assessments usin...This paper examines the structural response of reinforced concrete flat slabs,provided with flillyembedded shear-heads,through detailed three-dimensional nonlinear numerical simulations and parametric assessments using concrete damage plasticity models.Validations of the adopted nonlinear finite element procedures are carried out against experimental results from three test series.After gaining confidence in the ability of the numerical models to predict closely the full inelastic response and failure modes,numerical investigations are carried out in order to examine the influence of key material and geometric parameters.The results of these numerical assessments enable the identification of three modes of failure as a function of the interaction between the shear-head and surrounding concrete.Based on the findings,coupled with results from previous studies,analytical models are proposed for predicting the rotational response as well as the ultimate strength of such slab systems.Practical recommendations are also provided for the design of shear-heads in RC slabs,including the embedment length and section size.The analytical expressions proposed in this paper,based on a wide-ranging parametric assessment,are shown to offer a more reliable design approach in comparison with existing methods for all types of shear-heads,and are suitable for direct practical application.展开更多
Reinforced concrete(RC)flat slabs,a popular choice in construction due to their flexibility,are susceptible to sudden and brittle punching shear failure.Existing design methods often exhibit significant bias and varia...Reinforced concrete(RC)flat slabs,a popular choice in construction due to their flexibility,are susceptible to sudden and brittle punching shear failure.Existing design methods often exhibit significant bias and variability.Accurate estimation of punching shear strength in RC flat slabs is crucial for effective concrete structure design and management.This study introduces a novel computation method,the jellyfish-least square support vector machine(JS-LSSVR)hybrid model,to predict punching shear strength.By combining machine learning(LSSVR)with jellyfish swarm(JS)intelligence,this hybrid model ensures precise and reliable predictions.The model’s development utilizes a real-world experimental data set.Comparison with seven established optimizers,including artificial bee colony(ABC),differential evolution(DE),genetic algorithm(GA),and others,as well as existing machine learning(ML)-based models and design codes,validates the superiority of the JS-LSSVR hybrid model.This innovative approach significantly enhances prediction accuracy,providing valuable support for civil engineers in estimating RC flat slab punching shear strength.展开更多
文摘This paper examines the structural response of reinforced concrete flat slabs,provided with flillyembedded shear-heads,through detailed three-dimensional nonlinear numerical simulations and parametric assessments using concrete damage plasticity models.Validations of the adopted nonlinear finite element procedures are carried out against experimental results from three test series.After gaining confidence in the ability of the numerical models to predict closely the full inelastic response and failure modes,numerical investigations are carried out in order to examine the influence of key material and geometric parameters.The results of these numerical assessments enable the identification of three modes of failure as a function of the interaction between the shear-head and surrounding concrete.Based on the findings,coupled with results from previous studies,analytical models are proposed for predicting the rotational response as well as the ultimate strength of such slab systems.Practical recommendations are also provided for the design of shear-heads in RC slabs,including the embedment length and section size.The analytical expressions proposed in this paper,based on a wide-ranging parametric assessment,are shown to offer a more reliable design approach in comparison with existing methods for all types of shear-heads,and are suitable for direct practical application.
基金Acknowledgements This research was supported by the Research Program funded by Seoul National University of Science and Technology(SeoulTech).
文摘Reinforced concrete(RC)flat slabs,a popular choice in construction due to their flexibility,are susceptible to sudden and brittle punching shear failure.Existing design methods often exhibit significant bias and variability.Accurate estimation of punching shear strength in RC flat slabs is crucial for effective concrete structure design and management.This study introduces a novel computation method,the jellyfish-least square support vector machine(JS-LSSVR)hybrid model,to predict punching shear strength.By combining machine learning(LSSVR)with jellyfish swarm(JS)intelligence,this hybrid model ensures precise and reliable predictions.The model’s development utilizes a real-world experimental data set.Comparison with seven established optimizers,including artificial bee colony(ABC),differential evolution(DE),genetic algorithm(GA),and others,as well as existing machine learning(ML)-based models and design codes,validates the superiority of the JS-LSSVR hybrid model.This innovative approach significantly enhances prediction accuracy,providing valuable support for civil engineers in estimating RC flat slab punching shear strength.
