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
Reinforced concrete(RC)slabs are characterized by reduced construction time,versatility,and easier space partitioning.Their structural behavior is not straightforward and,specifically,punching shear strength is a curr...Reinforced concrete(RC)slabs are characterized by reduced construction time,versatility,and easier space partitioning.Their structural behavior is not straightforward and,specifically,punching shear strength is a current research topic.In this study an experimental database of 113 RC slabs without shear reinforcement under punching loads was compiled using data available in the literature.A sensitivity analysis of the parameters involved in the punching shear strength assessment was conducted,which highlighted the importance of the flexural reinforcement that are not typically considered for punching shear strength.After a discussion of the current international standards,a new proposed model for punching shear strength and rotation of RC slabs without shear reinforcement was discussed.It was based on a simplified load-rotation curve and new failure criteria that takes into account the flexural reinforcement effects.This experimental database was used to validate the approaches of the current international standards as well as the new proposed model.The latter proved to be a potentially useful design tool.展开更多
Accurate prediction of shear strength of structural engineering components can yield a magnificent information modeling and predesign process.This paper aims to determine the shear strength of steel fiber reinforced c...Accurate prediction of shear strength of structural engineering components can yield a magnificent information modeling and predesign process.This paper aims to determine the shear strength of steel fiber reinforced concrete beams using the application of data-intelligence models namely hybrid artificial neural network integrated with particle swarm optimization.For the considered data-intelligence models,the input matrix attribute is one of the central element in attaining accurate predictive model.Hence,various input attributes are constructed to model the shear strength"as a targeted variable".The modeling is initiated using historical published researches steel fiber reinforced concrete beams information.Seven variables are used as input attribute combination including reinforcement ratio(ρ%),concrete compressive strength(f′c),fiber factor(F1),volume percentage of fiber(Vf),fiber length to diameter ratio(lf/ld)effective depth(d),and shear span-to-strength ratio(a/d),while the shear strength(SS)is the output of the matrix.The best network structure obtained using the network having ten nodes and one hidden layer.The final results obtained indicated that the hybrid predictive model of ANN-PSO can be used efficiently in the prediction of the shear strength of fiber reinforced concrete beams.In more representable details,the hybrid model attained the values of root mean square error and correlation coefficient 0.567 and 0.82,respectively.展开更多
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.
基金The financial support of the A utonomous Region of Sardinia under Grant PO-FSE 2014-2020,CCI:2014-IT05SFOP021,through the project"Retrofitting,Rehabilitation and Requalification of the Historical Cultural Architectural Heritage(R3-PAS)",is acknowledged by Flavio Stochino.
文摘Reinforced concrete(RC)slabs are characterized by reduced construction time,versatility,and easier space partitioning.Their structural behavior is not straightforward and,specifically,punching shear strength is a current research topic.In this study an experimental database of 113 RC slabs without shear reinforcement under punching loads was compiled using data available in the literature.A sensitivity analysis of the parameters involved in the punching shear strength assessment was conducted,which highlighted the importance of the flexural reinforcement that are not typically considered for punching shear strength.After a discussion of the current international standards,a new proposed model for punching shear strength and rotation of RC slabs without shear reinforcement was discussed.It was based on a simplified load-rotation curve and new failure criteria that takes into account the flexural reinforcement effects.This experimental database was used to validate the approaches of the current international standards as well as the new proposed model.The latter proved to be a potentially useful design tool.
文摘Accurate prediction of shear strength of structural engineering components can yield a magnificent information modeling and predesign process.This paper aims to determine the shear strength of steel fiber reinforced concrete beams using the application of data-intelligence models namely hybrid artificial neural network integrated with particle swarm optimization.For the considered data-intelligence models,the input matrix attribute is one of the central element in attaining accurate predictive model.Hence,various input attributes are constructed to model the shear strength"as a targeted variable".The modeling is initiated using historical published researches steel fiber reinforced concrete beams information.Seven variables are used as input attribute combination including reinforcement ratio(ρ%),concrete compressive strength(f′c),fiber factor(F1),volume percentage of fiber(Vf),fiber length to diameter ratio(lf/ld)effective depth(d),and shear span-to-strength ratio(a/d),while the shear strength(SS)is the output of the matrix.The best network structure obtained using the network having ten nodes and one hidden layer.The final results obtained indicated that the hybrid predictive model of ANN-PSO can be used efficiently in the prediction of the shear strength of fiber reinforced concrete beams.In more representable details,the hybrid model attained the values of root mean square error and correlation coefficient 0.567 and 0.82,respectively.
