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Automated Translation of Rebar Information from GPR Data into As-Built BIM: A Deep Learning-based Approach
Building Information Modeling (BIM) is increasingly used in the construction industry, but existing studies often ignore embedded rebars.
An Innovative Approach to Determine Rebar Depth and Size by Comparing GPR Data with a Theoretical Database
However, due to the difficulty in extracting signals from GPR data and the intrinsic coupling between the rebar depth and size showing in the data, simultaneously determining rebar depth and size is challenging.
An Improved Convolutional Neural Network System for Automatically Detecting Rebar in GPR Data
Thus, this paper introduces AlexNet as an alternative solution for automatically detecting rebar within GPR data.
Automated Activity Recognition of Construction Equipment Using a Data Fusion Approach
To address the issue, we utilized a data fusion approach: This approach is based on collecting audio and kinematic data, and includes the following steps: 1) recording audio and kinematic data generated by machines, 2) preprocessing data, 3) extracting time- and frequency-domain-features, 4) feature-fusion, and 5) categorizing activities using a machine-learning algorithm.
