Developing Estimation Algorithms for the Punching Shear Strength of SlabColumn Connections

Abstract

This exploration introduces a data-oriented scheme for projecting the punching shear resistance (Vn) linked to failure modes (FMs) in slab-column connections with shear reinforcement. Because reinforced concrete (RC) slab-column connections exhibit a simple construction approach, slabs rest directly on columns without beams. In slab-column connections with shear reinforcement, Vn linked to FMs has only very seldom been stated in research up to this point using machine learning approaches. Two accepted and reliable models were considered for estimation: random forests (RF) and adaptive neuro-fuzzy inference system (ANFIS). Nine input parameters about the punching shear mechanism are found using 327 test results from a computational database. The dataset's learning set (70%) and evaluation set (15%) were used to construct, validate, and test the suggested scheme. Its accuracy is greatly affected by the RF and ANFIS hyperparameters, which need to be selected using metaheuristic enhancement tactics. For this, the Prairie Dog Algorithm (PDA) is used. Based on the logic and assessment of analytical justifications, it can be concluded that both models are exact and dependable, with ANF-PDO being marginally superior compared to RF-PDO.