Abstract
The present research work aims to compare the results for predicting the ultimate response of Reinforced Concrete (RC) members using Current Design Codes (CDCs), an alternative method based on the Compressive Force Path (CFP) method, and Artificial Neural Network (ANN). For this purpose, the database of 145 samples of RC Flat Slab with the simple supported condition under concentrated load is developed from the latest published work. All the cases studied were Square Concrete Slabs (SCS). The critical parameters used as input for the study were column dimension, cs, depth of the slab, ds, shear span ratio, a v s / d , longitudinal percentage steel ratio, ρls, yield strength of longitudinal steel, fyls, the compressive strength of concrete, fcs, and ultimate load-carrying capacity, Vus. Seven ANN models were trained using different combinations of input parameters and different points of hidden neurons with different activation functions. The results exhibited that SCS-4 was the most optimized ANN model, having the maximum value of R (89%) with the least values of MSE (0.62%) and MAE (6.2%). It did not only reduce the error but also predicted accurate results with the least quantity of input parameters. The predictions obtained from the studied models (i.e., CDCs, CFP, and ANN) exhibited that results obtained using the ANNs model correlated well with the experimental data. Furthermore, the FEM results for the selected cases show the closer result to the ANN predictions.
Highlights
Introduction e practice of ReinforcedConcrete (RC) flat slabs or flat plate or waffle slabs in which the Reinforced Concrete (RC) slab is placed directly over the RC members is very common in the construction industry
Capability of Artificial Neural Network (ANN) modeling is described for RC flat slab with the -supported condition under punching load, while the parameters considered are column dimension, cs, depth of the slab, ds, shear span ratio, avs/ds, longitudinal percentage steel ratio, ρls, yield strength of longitudinal steel, fyls, compressive strength of concrete, fcs, and ultimate load-carrying capacity, Vus
Following are the main conclusions of the present work: (1) As a result of the comparative study, it has been revealed that nonconventional ANN models predicted the experimental values well compared to conventional models i.e., Current Design Codes (CDCs) and Compressive Force Path (CFP)
Summary
Afaq Ahmad ,1 Muhammad Usman Arshid ,1 Toqeer Mahmood ,2 Naveed Ahmad ,1 Abdul Waheed ,3 and Syed Shujaa Safdar 4. E present research work aims to compare the results for predicting the ultimate response of Reinforced Concrete (RC) members using Current Design Codes (CDCs), an alternative method based on the Compressive Force Path (CFP) method, and Artificial Neural Network (ANN). For this purpose, the database of 145 samples of RC Flat Slab with the simple supported condition under concentrated load is developed from the latest published work.
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