An Intelligent Framework for Shear Capacity Prediction in Productive Building Structures Based on Image Reconstruction and Deep Learning Data Completion

This study proposes a novel deep learning framework for predicting the shear capacity of slender reinforced concrete (RC) beams without shear reinforcement. The proposed approach employs convolutional neural networks and autoencoders to transform structural data into image representations, reconstruct missing data, and predict shear capacity with high accuracy. Using a dataset of 964 experimental results covering a wide range of beam characteristics, the framework achieves remarkable predictive performance. The image-based methodology enables the model to capture spatial dependencies, while the autoencoder reconstructs incomplete data with a fidelity exceeding 95%. The framework is validated against conventional methods under different data masking levels (10%, 20%, 30%). For 10% masking, the proposed method achieves R² = 0.94, MAE = 0.05, and NSE = 0.93, significantly outperforming ACI 318 and Eurocode 2. Even with 30% masking, the framework maintains robust performance, with R² = 0.85 and NSE = 0.81. These results highlight the scalability and reliability of the model in handling incomplete datasets, as well as its potential to advance structural engineering practice by integrating machine learning techniques with traditional design methodologies.