Application of Time Series Methods on Long-Term Structural Monitoring Data for Fatigue Analysis

Structural health monitoring (SHM) can be employed to reduce uncertainties in different aspects of structural analysis such as: load modeling, crack development, corrosion rates, etc. Fatigue is one of the main degradation processes of structures that causes failure before the end of their design life. Fatigue loading is among those variables that have a great influence on uncertainty in fatigue damage assessment. Conventional load models such as Rain-flow counting and Markov chains work under stationarity assumption, and they are unable to deal with the seasonality effect in fatigue loading. Time series methods, such as ARIMA (Auto-Regressive Integrated Moving Average), are able to deal with this effect in the data; hence, they can be helpful for fatigue load modelling. The goal of this study is to implement seasonal ARIMA to prepare a load model for long-term fatigue loading that can capture more details of the loading scenario regarding the seasonal effects in traffic loading.