Deep Neural Network Approach for Automated Architectural Bolt Usage Prediction in Building Information Model Control

Abstract

The rapid advancements in deep neural network (DNN) technology have sparked a surge of academic research aimed at harnessing the immense potential of DNNs across various industries. In line with this trend, this paper introduces a data-driven DNN model designed to automate Building Information Model (BIM) control by accurately predicting architectural bolt usage. The development of this model involved meticulous data preprocessing techniques and the elaboration of a sophisticated DNN architecture. The model was trained using a substantial dataset consisting of 13,000 samples. The validation results achieved high performance, with an average accuracy surpassing 90% for both the x-axis and y-axis data. These achieved accuracy levels are notably high, signifying the model’s suitability for real-world BIM controllers.