The second part of "My thesis in 3 Posters" is related to my studies during my secondment in Lausanne, Switzerland.
During this period, I joined the Structural Maintenance and Safety Laboratory (MCS) of Civil Engineering depertment at EPFL. My work in this lab was devoted to take advantage of valuable long-term monitoring data that is recorded by this lab for about two years. I have employed Time Series methods such as ARIMA to prepare a new load model for fatigue analysis while there is seasonality effect. This new model can deal with missing data, it can capture seasonality effect, and it can be used to generate fatigue loading for further analysis.
A short abstract in addition to my poster is provided below.
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.
▪ "Application of Time Series Methods on Long-Term Structural Monitoring Data for Fatigue Analysis", SMAR2019, 5th International Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures, 27 – 29 August 2019 in Potsdam, Germany.
**Please open the following link to be able to see the poster