Construction Activity Classification Based on Vibration Monitoring Data: A Supervised Deep-Learning Approach with Time Series RandAugment-Reference-Cited by-同舟云学术

Construction Activity Classification Based on Vibration Monitoring Data: A Supervised Deep-Learning Approach with Time Series RandAugment

Published:2022-09 Issue:9 Volume:148 Page:
ISSN:0733-9364
Container-title:Journal of Construction Engineering and Management
language:en
Short-container-title:J. Constr. Eng. Manage.

Author:

Meng Qiuhan¹,Zhu Songye²^ORCID

Affiliation:

1. Ph.D. Candidate, Dept. of Civil and Environmental Engineering, The Hong Kong Polytechnic Univ., Kowloon, Hong Kong 999077, China.

2. Professor, Dept. of Civil and Environmental Engineering, The Hong Kong Polytechnic Univ., Kowloon, Hong Kong 999077, China (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Strategy and Management,Industrial relations,Building and Construction,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CO.1943-7862.0002359

Reference30 articles.

1. Application of Low-Cost Accelerometers for Measuring the Operational Efficiency of a Construction Equipment Fleet

2. Construction equipment activity recognition for simulation input modeling using mobile sensors and machine learning classifiers

3. Amick H. and M. Gendreau. 2000. “Construction vibrations and their impact on vibration-sensitive facilities.” In Proc. Construction Congress 6: Building Together for a Better Tomorrow in an Increasingly Complex World 758–767. Reston VA: ASCE.

4. Avci, O., O. Abdeljaber, S. Kiranyaz, and D. Inman. 2017. “Structural damage detection in real time: Implementation of 1D convolutional neural networks for SHM applications.” In Structural health monitoring & damage detection, 49–54. Berlin: Springer.

5. Cubuk, E. D., B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le. 2019. “AutoAugment: Learning augmentation strategies from data.” In Proc., IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshops (CVPR 2019), 113–123. New York: IEEE.

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