Construction Site Monitoring Data Processing Based on Detecting Anomalies and Improved Variational Mode Decomposition

Yixiao Shao; Tengfei An; Yafei Qi; Wenli Liu

doi:10.2991/978-94-6463-312-2_27

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Construction Site Monitoring Data Processing Based on Detecting Anomalies and Improved Variational Mode Decomposition

Authors

Yixiao Shao¹, Tengfei An¹, Yafei Qi¹, Wenli Liu¹^{, *}

¹School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China

^*Corresponding author. Email: liu_wenli@hust.edu.cn

Corresponding Author

Wenli Liu

Available Online 7 December 2023.

DOI: 10.2991/978-94-6463-312-2_27 How to use a DOI?
Keywords: Deep pit foundations; Variational mode decomposition; De-noise; Anomaly
Abstract: Anomalies and noise are prevalent in the time series data extracted from sensors at construction sites, which can hinder the assessment of safety levels and risks. This study aims to detect anomalies and denoise real-time monitoring data from sensors, thereby facilitating early risk warning and enhancing accuracy of real-time status. To achieve this objective, we propose a framework that integrates Extended Isolation Forest, Whale Optimization Algorithm, and Variational Mode Decomposition models. The effectiveness of the framework is validated using a dataset obtained from sensors deployed during the construction of a deep pit foundation. The proposed approach successfully denoises the dataset without anomalies with a root mean square error of 0.0389 and signal-to-noise ratio of 24.09. Consequently, our approach effectively preprocesses data to enable improved decision-making and enhance security risk management capabilities.
Copyright: © 2023 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 2023 5th International Conference on Structural Seismic and Civil Engineering Research (ICSSCER 2023)
Series: Atlantis Highlights in Engineering
Publication Date: 7 December 2023
ISBN: 10.2991/978-94-6463-312-2_27
ISSN: 2589-4943
DOI: 10.2991/978-94-6463-312-2_27 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Yixiao Shao
AU  - Tengfei An
AU  - Yafei Qi
AU  - Wenli Liu
PY  - 2023
DA  - 2023/12/07
TI  - Construction Site Monitoring Data Processing Based on Detecting Anomalies and Improved Variational Mode Decomposition
BT  - Proceedings of the 2023 5th International Conference on Structural Seismic and Civil Engineering Research (ICSSCER 2023)
PB  - Atlantis Press
SP  - 258
EP  - 269
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-312-2_27
DO  - 10.2991/978-94-6463-312-2_27
ID  - Shao2023
ER  -

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