Feature selection based reverse design of doubly reinforced concrete beams

Abstract

ABSTRACT Chained Training Scheme (CTS) and Chained training scheme with Revised Sequence (CRS) were proposed to train artificial neural networks (ANNs) to design doubly reinforced concrete (RC) beams. CRS and CTS performed training on large datasets based on feature selection scores determined by Neighborhood Component Analysis (NCA). Conventional training methods cannot utilize output parameters as input indexes for training. CRS-based networks can be trained on inputs and outputs at the same time, regardless of whether they belong to an input or output side. This method allows design parameters appearing on the output side to be simultaneously used as input feature indexes of the other outputs in a chained fashion, improving training accuracies. A targeted nominal moment capacity was reversely pre-assigned to the input side to design beam parameters such as beam width, both tensile and compressive rebar ratios, and cost of beam materials on the output side. This type of reverse design improving the conventional design standard is difficult to be achieved using conventional design methods. CRS- and CTS-trained ANN models substantially outperformed the parallel training method (PTM)- and training on entire dataset (TED)-trained ANN models, with the ability to accurately design doubly RC beams with multiple input and output parameters.