Data Fusion Analysis Method for Assessment on Safety Monitoring Results of Deep Excavations

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.