Deep Neural Network Approach for Automated Architectural Bolt Usage Prediction in Building Information Model Control
- Title
- Deep Neural Network Approach for Automated Architectural Bolt Usage Prediction in Building Information Model Control
- Authors
- Jonghyeok, Park; HAN, SOOHEE; Kyung-Jun, Kim
- Date Issued
- 2023-10-19
- Publisher
- ICROS
- 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.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/122404
- Article Type
- Conference
- Citation
- 2023 The 23rd International Conference on Control, Automation and Systems (ICCAS 2023), 2023-10-19
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.