Monitoring Proximity Tunneling Effects Using Blind Source Separation Technique

Abstract

The recent advances in sensing methods and data acquisition technologies have facilitated the collection of instrumentation data for continuous structural health monitoring (SHM). However, interpretation of raw sensor data, affected by various known and unknown environmental factors in field conditions is a challenging task. Structural systems are usually undetermined due to limited sensor data that are not sufficient for finding explicit relations between system inputs and outputs. This study aims to introduce a data-driven methodology using response-only data for underdetermined structural systems. The Principle Component Analysis (PCA) as a Blind Source Separation (BSS) method has been used to decompose the mixed raw response data into a linear combination of statistically uncorrelated mode shapes of input data. Being a data-driven method, the proposed framework is not limited in application to a specific sensor type. To evaluate the efficiency of the method in practice, the close proximity excavation effects of a new tunnel on an existing tunnel has been considered. The analysis results show that the method is not only able to decompose measurements into excavation-induced and the environment-induced deformations but also the calculated eigen-parameters can be used as excellent indicators of structural behaviors during excavation by visualizing the tunnel lining deformations.