Data Fusion Analysis Method for Assessment on Safety Monitoring Results of Deep Excavations
Publication: Journal of Aerospace Engineering
Volume 30, Issue 2
Abstract
A safety monitoring system is usually applied in deep excavations in order to control the construction risk and to ensure the serviceability of adjacent facilities. Considering the mass data collected by different sensors, a reasonable assessment method on the monitoring results is necessary to evaluate the safety state of both the deep excavation itself and the surrounding environment. By introducing the conception of data fusion, a comprehensive assessment method is presented to find the anomaly in the safety monitoring results in this paper. Data fusion analyses on both a single monitoring item and the correlation of multiple monitoring items are proposed and studied. The one-class support vector machines (SVMs) are used to improve the data fusion analysis between a single monitoring item and different excavation parameters, and then developed to three-dimensional (3D) fusion analysis on a single item and multiple parameters of an excavation. The mechanical and geometric patterns between different monitoring items are studied to propose a data fusion analysis on multiple monitoring items and then to build the assessment criteria. Based on these two kinds of data fusion analysis, the mass monitoring data can be analyzed completely to assess the safety state of deep excavations. An application in two cases of deep excavation in Shanghai, China, shows that the proposed method is effective in data anomaly assessment.
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Acknowledgments
The financial support from the National Natural Science Foundation of China (NSFC) (Grant Nos. 41172251 and 41330633) and the Science and Technology Commission of Shanghai Municipality (Funding No. 14231200702) are gratefully acknowledged.
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Published In
Journal of Aerospace Engineering
Volume 30 • Issue 2 • March 2017
Copyright
© 2015 American Society of Civil Engineers.
History
Received: May 10, 2015
Accepted: Oct 9, 2015
Published online: Dec 31, 2015
Discussion open until: May 31, 2016
Published in print: Mar 1, 2017
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