Predicting pit deformation anomalies based on HTM algorithm analysis

Yue Qiao; Wenjing Xu

doi:10.1117/12.3029364

16 May 2024 Predicting pit deformation anomalies based on HTM algorithm analysis

Yue Qiao, Wenjing Xu

Proceedings Volume 13166, International Conference on Remote Sensing Technology and Survey Mapping (RSTSM 2024); 131660R (2024) https://doi.org/10.1117/12.3029364
Event: International Conference on Remote Sensing Technology and Survey Mapping (RSTSM 2024), 2024, Changchun, China

Abstract

Hierarchical Temporal Memory (HTM), an unsupervised online algorithm, has been validated in multiple fields of machine learning, especially prediction and anomaly detection, which can well reduce the false alarm rate by analyzing from multiple dimensions. For instance, traffic flow prediction, ocean observation data anomaly detection, etc. However, the analysis and prediction based on the real-time monitoring data of technical engineering have not been addressed. In this study, a real-time prediction method of foundation pit deformation based on HTM is proposed to monitor the sensor data of different depths in real time, predict the horizontal displacement and vertical settlement, set the abnormal range according to the requirements of the monitoring accuracy level, and calculate the measured and predicted values. Combining the measured and predicted values by cloud computation, a signal value is returned to determine whether it is anomalous or not. The error reporting rate is set according to the number of anomalies. The results show that the HTM can be used to analyze and predict the pit deformation monitoring data effectively and reliably.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yue Qiao and Wenjing Xu "Predicting pit deformation anomalies based on HTM algorithm analysis", Proc. SPIE 13166, International Conference on Remote Sensing Technology and Survey Mapping (RSTSM 2024), 131660R (16 May 2024); https://doi.org/10.1117/12.3029364

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available