Residential reinforced concrete building design relies on close collaboration between architectural and engineering offices to improve the distribution of living spaces while meeting structural regulatory requirements. Several studies have taken advantage of the vast data generated by both offices to create machine-learning models that streamline design processes and decision-making. Recent research proposed an artificial neural network (ANN) model for predicting the length and thickness of the rectangular segments that constitute the plan's walls based on the architectural data; however, it could not predict walls absent from the original design. This constraint was addressed by a convolutional neural network (CNN) model, demanding a larger dataset (by 137 times) and several rule-based filters for assembling the predicted plan, incurring high computational costs, and generating blurry predictions. Therefore, this study presents a new methodology to propose walls and columns not considered in the architectural design through an ANN model, which employs less data than CNN but with comparable results. First, this study creates a dataset of 165 Chilean buildings using a mapping function capable of generating neighborhoods within the floors and extracting their walls' geometric and topological features. Then, we trained an ANN model to predict unconsidered wall segments in architectural design, using a feature vector that addresses conditions such as thickness, wall connectivity, distance between elements, seismic zone, foundation soil type, and other engineering parameters, achieving outstanding results in terms of the coefficient of determination (R2) of 0.95 for length, 0.93 for thickness, 0.94 for angle, and 0.97 for position (x, y). Finally, with an architectural plan, this model can propose different structural solutions, reducing the data used for training and validation to 8% concerning the CNN model, with comparable performance.