Monday 15 October 2018

My work in a very small nutshell

In last decades maintenance optimisation of civil structures has gained increasing attention since the number of ageing structures is becoming large while available budget dedicated to maintenance is limited. Fatigue is one of the main degradation processes on steel structures that causes structural failure before the designed lifetime. Therefore, the main goal of our study is to provide an optimal maintenance planning to extend the lifetime of structures against fatigue. Structural health monitoring can help to have a better understanding of the structural condition for a better maintenance funding allocation. To reach our goal we might face different challenges in A: fatigue reliability assessment, B: application of monitoring information, and C: optimisation of maintenance strategies. Addressing these challenges can define different steps of this study. 

Within the first step, fracture mechanism and S-N curves are two regular approaches that are used to evaluate fatigue damage and provide a proper limit state for reliability analysis. Fatigue reliability can be performed within a time-independent or time-dependent framework depending on the choice of fatigue model. The challenge here is related to how to evaluate low probabilities of failure when computationally expensive performance functions are involved. Hence, we have developed an efficient methodology to perform time-dependent reliability analysis. Kriging meta-modelling is used to replace the computationally expensive performance functions while using Monte Carlo simulation. 

The second step is related to how to get benefit from monitoring information. Structural health monitoring provides us with valuable information about the current situation of civil structures. This information can be about crack development, actual loading conditions, etc. The challenge here is related to the way we employ the information coming from monitoring data. With this respect, a study has been developed on long-term monitoring data available at EPFL. Time series methods are employed to prepare a loading model for long-term monitoring data. The aim is to capture seasonality effect in traffic loading and to provide a model that gives more detail about structural fatigue loading. This load model can be used within S-N approach or fracture mechanism with some adjustments.

Finally, methods and approaches in previous steps will be combined to have a proper and updated indicator to apply maintenance actions. The desired indicator would be fatigue reliability index. The challenge in the final step can be related to the cost models for maintenance and monitoring. Therefore, an appropriate cost model will be chosen for the optimisation framework, and overall methodology will be applied on a shared object or another case study.  

Thank you very much for reading this post. 










Wednesday 3 October 2018

Secondment at EPFL: such a great experience


Secondments are one of the most interesting parts of Mari Curie projects. It gives you the opportunity to work with different researchers in different companies, universities, and research centres. 
I performed my first secondment within the INFRASTAR program at EPFL. It was such a great chance for me to stay as a PhD visitor in one of the well-known engineering schools in the world and to live in Switzerland with its magnificent nature and landscapes. What I have learnt during my secondment is not only related to my work, I also learnt a lot about the Swiss culture, people, and the great international atmosphere of Lausanne. 

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. It is worthy to mention that three of the shared objects within INFRASTAR are provided by MCS. Chillion viaduc is one of our shared objects and I was working on the monitoring data of this bridge. 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 (more details about this study will be provided later).  

Apart from work, this period was a great opportunity for me to enjoy the nature of Switzerland. What is very interesting in Switzerland is that one can make a plan to visit any place in the country during a weekend, however, there are plenty of beautiful places out there. Another thing that sounds even more interesting in this small country is that they have four official languages and when you visit different regions you will see the diversity of the languages and cultures. 

In conclusion, I would say that my secondment at EPFL was a great incident for me. I could get benefit of the invaluable experiences of researchers at MCS lab. Also, I could add the experience of living in Switzerland in my memory besides all other beautiful experiences that I had in another countries. 

Thank you for reading this article, 

Chillion Viaduc

A trip with MCS members to visit a newly constructed pedestrianise bridge with UHPFRC that is a new material developed at MCS.  




My thesis in 3 Posters- Part II: Application of time series methods on long-term structural monitoring data for fatigue analysis

The second part of "My thesis in 3 Posters" is related to my studies during my secondment in Lausanne, Switzerland.   During...