Numerical Evaluation of the Upright Columns with Partial Reinforcement along with the Utilisation of Neural Networks with Combining Feature-Selection Method to Predict the Load and Displacement

Abstract

This study evaluated the axial capacity of cold-formed racking upright sections strengthened with an innovative reinforcement method by finite element modelling and artificial intelligence techniques. At the first stage, several specimens with different lengths, thicknesses and reinforcement spacings were modelled in ABAQUS. The finite element method (FEM) was employed to increase the available datasets and evaluate the proposed reinforcement method in different geometrical types of sections. The most influential factors on the axial strength were investigated using a feature-selection (FS) method within a multi-layer perceptron (MLP) algorithm. The MLP algorithm was developed by particle swarm optimization (PSO) and FEM results as input. In terms of accuracy evaluation, some of the rolling criteria including results showed that geometrical parameters have almost the same contribution in compression capacity and displacement of the specimens. According to the performance evaluation indexes, the best model was detected and specified in the paper and optimised by tuning other parameters of the algorithm. As a result, the normalised ultimate load and displacement were predicted successfully.

Evaluation of the Performance of a Composite Profile at Elevated Temperatures Using Finite Element and Hybrid Artificial Intelligence Techniques

“…In the PSO algorithm, particles are the building blocks of the population, and they work together to obtain the optimum approach to the target [51,62,67]. For this reason, it is called swarm intelligence.…”

“…For example, the ABAQUS program has been successfully used for modelling the steel-concrete composite structures [50], and the results were reliable enough to be used in further studies. In another study, ABAQUS was utilized to simulate monotonic testing to verify experimental results and simultaneously provide new data from other samples [51].…”

“…The MLP neural network is suitable for prediction, especially in problems with stochastic irregularities. An MLP-PSO combination was used in another study to predict the compressive strength of upright columns, and the combination of feature selection technique with NN method led to promising results [51].…”

Sustainable Design of Self-Consolidating Green Concrete with Partial Replacements for Cement through Neural-Network and Fuzzy Technique

“…ANFIS has been used in many applications including the real-time identification and estimation of multivariate systems [40]. ANFIS, in contrast to most analytical procedures, does not rely on system parameters to be known [41]. Its more straightforward approach allows it to be used in multivariate systems where input data is limited.…”

“…Its more straightforward approach allows it to be used in multivariate systems where input data is limited. The partial replacement of cement in SCC with SCMs leads to a change in the concrete properties; for example, a change in the compressive strength can result as a function of time [41,42]. Quantifying the degree of this change is a challenge.…”