A time series based damage detection method for obtaining separate mass and stiffness damage features of shear-type structures

Abstract

One of the main challenges in damage detection of in-service civil structures is the effect of operational and environmental changes. This paper presents a novel output-only vibration based method for detection of stiffness and mass changes in shear-type structures using a proposed time series analysis along with sensor clustering technique. AutoRegressive Moving Average models with eXogenous inputs (ARMAX) are incorporated with the equations of motion written for different sensor clusters. Together with two assumptions, changes in the ARMAX model coefficients are employed to build Stiffness Damage Features (SDF) and Mass Damage Features (MDF). Using SDFs and MDFs, existence, location, severity of changes, i.e., stiffness reduction due to damage, and changes in mass due to operational conditions, can be identified separately and accurately. To demonstrate the effectiveness of the proposed method in its current form, first, the shear-type IASC-ASCE numerical benchmark problem is employed. Subsequently, a laboratory-scale four-storey steel structure is developed and tested to study the proposed approach with experimental data. The results show that the approach can successfully identify the location and severity of damage and mass changes separately and very accurately using output-only vibration data.