Automation in Construction, Journal Year: 2024, Volume and Issue: 166, P. 105688 - 105688
Published: Aug. 9, 2024
Language: Английский
Automation in Construction, Journal Year: 2024, Volume and Issue: 166, P. 105688 - 105688
Published: Aug. 9, 2024
Language: Английский
Automation in Construction, Journal Year: 2025, Volume and Issue: 174, P. 106142 - 106142
Published: March 31, 2025
Language: Английский
Citations
0Asian Journal of Civil Engineering, Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 5, 2024
Language: Английский
Citations
1Applied Sciences, Journal Year: 2024, Volume and Issue: 14(7), P. 2859 - 2859
Published: March 28, 2024
In this study, we delve into a novel approach by employing sensor-based pattern recognition model to address the automation of construction equipment activity analysis. The integrates time flight (ToF) sensors with deep convolutional neural networks (DCNNs) accurately classify operational activities equipment, focusing on piston movements. research utilized one-twelfth-scale excavator model, processing displacement ratios its pistons unified dataset for Methodologically, study outlines setup sensor modules and their integration controller, emphasizing precision in capturing dynamics. DCNN characterized four-layered blocks, was meticulously tuned within MATLAB environment, demonstrating model’s learning capabilities through hyperparameter optimization. An analysis 2070 samples representing six distinct yielded an impressive average 95.51% recall 95.31%, overall accuracy 95.19%. When compared against other vision-based accelerometer-based methods, proposed showcases enhanced performance reliability under controlled experimental conditions. This substantiates potential practical application real-world scenarios, marking significant advancement field monitoring.
Language: Английский
Citations
0Automation in Construction, Journal Year: 2024, Volume and Issue: 166, P. 105688 - 105688
Published: Aug. 9, 2024
Language: Английский
Citations
0