Detection of seismic damage in buildings using structural responses

As a result of an earthquake and its aftershocks built infrastructure may sustain damage. One of the major challenges for quick and efficient recovery in the aftermath of a hazardous event is rapid estimation of the damage. If the state of buildings, bridges, dams and other structures could be quickly and reliably assessed healthy, undamaged structures could be immediately re-opened for continuous, uninterrupted service, while damaged structures would be closed and prioritised for later detailed evaluation, repair, demolition or replacement. Doing so will minimize casualties and economic loses and will aid quick recovery of an affected area. Accurate estimation of seismic damage is, however, a time and resource consuming task. Traditionally, it can be achieved by visual inspection of infrastructure following an earthquake. However, given the usually large stocks of structures to inspect and limited number of qualified personnel damage assessment is a slow process. The fact that damage can often be inconspicuous adds to the difficulty.

An alternative to visual inspection can be using measurements of structural responses during strong motion events taken by sensors located in the structure. This approach becomes feasible with the development of continuous seismic monitoring arrays. In New Zealand, the EQC and FRST funded GeoNet monitoring project that is currently expanding its coverage to buildings and bridges, can be used for structural damage detection. However, raw data from seismic sensors are of limited value. The challenge is to analyse the measured structural responses so that structural damage can be detected and quantified. This research studies several techniques that enable such purposeful data analyses.

Damage detection by analysis of structural responses is based on the premise that it is possible to choose certain response signal features that are different for responses of healthy and damaged structures. Once the features are selected another analytical tool is required to actually tell the difference between the features corresponding to different structural states. In this research, we modelled structural accelerations using autoregressive time series models in order to find suitable damage sensitive features, and used pattern recognition techniques for feature classification. The approach was thoroughly investigated through several experimental studies and results of damage detection and quantification are promising.