Reverse designs of doubly reinforced concrete beams using Gaussian process regression models enhanced by sequence training/designing technique based on feature selection algorithms

Abstract

The present paper introduces a practical and convenient artificial intelligence-based design approach for doubly reinforced concrete (RC) beams. Completed designs are automatically obtained from regression models and back-substitution (BS) procedures, which satisfy the preassigned flexural strength, curvature ductility, and calculate serviceability parameters. In addition, regression algorithms are developed by training multiple Gaussian Process Regression models on structural data. Furthermore, feature selections and Chained training scheme with Revised Sequence (CRS) techniques are implemented to enhance the training accuracy, providing acceptable accuracies (less than 0.7% errors) in 91 interpolation designs. First, CRS procedures are employed, improving the regression accuracy by sequentially predicting outputs, using predictions of predecessor steps as inputs for the successor ones. In doing so, the preciseness of models is improved as training continues. Appropriate inputs and reasonable output sequences for CRS are determined using a feature selection-based procedure for obtaining optimal training. This procedure implemented three feature selection methods (F-test, Neighborhood Component Analysis (NCA), and RReliefF) in a greedy algorithm, evaluating relations among design parameters. In summary, a direct design approach of a doubly reinforced concrete beam is presented, which enables engineers to control moment capacities and curvature ductility easily, replacing ineffective iteration-based conventional design procedures.