Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Buildings

DOI: 10.3390/buildings16020336

Authors: Xin Tian, Ming Lan, Yongchao Dang, Nan Li

Institutions: Inner Mongolia University of Technology, Xi'an University of Science and Technology

Language: English

Topic: Structural Behavior of Reinforced Concrete

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137