  This research aims at comparative analysis of shear strength prediction at
slab-column connection, unifying machine learning, design codes and Finite
Element Analysis. Current design codes (CDCs) of ACI 318-19 (ACI), Eurocode 2
(EC2), Compressive Force Path (CFP) method, Feed Forward Neural Network (FNN)
based Artificial Neural Network (ANN), PSO-based FNN (PSOFNN), and BAT
algorithm-based BATFNN are used. The study is complemented with FEA of slab for
validating the experimental results and machine learning predictions.In the
case of hybrid models of PSOFNN and BATFNN, mean square error is used as an
objective function to obtain the optimized values of the weights, that are used
by Feed Forward Neural Network to perform predictions on the slab data. Seven
different models of PSOFNN, BATFNN, and FNN are trained on this data and the
results exhibited that PSOFNN is the best model overall. PSOFNN has the best
results for SCS=1 with highest value of R as 99.37% and lowest of MSE, and MAE
values of 0.0275%, and 1.214% respectively which are better than the best FNN
model for SCS=4 having the values of R, MSE, and MAE as 97.464%, 0.0492%, and
1.43%, respectively.
