Random forest-based algorithms for accurate evaluation of ultimate bending capacity of steel tubes

Abstract

Despite the existence of methods for estimating the behavior of steel circular tubes subjected to pure bending, analytical models are still restricted due to the problem's complexity and significant nonlinearity. Using the random forest (RF) as the basic model, novel intelligent models are constructed to estimate the ultimate pure bending capacity of circular steel tubes in this study. The RF model's parameters are optimized using three nature inspired optimization algorithms, namely, the particle swarm optimization (PSO), ant colony optimization (ACO) and whale optimization algorithm (WOA). In the experimental part, a database of 104 tests that comprise 49 and 55 pure bending tests conducted on fabricated and cold-formed steel circular tubes, respectively, are evaluated and utilized to investigate the applicability of the hybrid RF-models. A single RF model is also built for comparative reasons in order to estimate the ultimate pending capacity. Various statistical and graphical measures are used to evaluate the performance of the developed models. The results show that the proposed RF-based nature-inspired algorithms can outperform the original RF predictive model. When the hybrid-RF models were assessed, it was discovered that the RF-WOA performed best. In addition, the influence of each parameter on the prediction findings based on the best RF-model is investigated via sensitivity analysis. Taking into account the overall findings, the hybrid RF-models may be used as powerful tools to predict the ultimate bending capacity of circular steel tubes and may be viable to aid technicians in making proper judgments.