 Hello, it's already 2 o'clock, so if you could please take your seats. We have an interesting session on Working Group 2, which is dealing with SHM strategies and the connection with structural performance. We have six presentations this afternoon and then we have a few more tomorrow morning. To make it a little bit more lively, what we will do is we will have the first three presentations and we will have a discussion on these and then we will short break just to stretch our legs and we are ready for the next three presentations and a discussion. So without taking more time from the speakers, I would like to invite Dr. Sikora to present his fact sheet on assessment of cooling towers and industrial chimneys based on monitoring. Well good afternoon everyone, thanks for the introduction, Marios. Well in this contribution we would like just, can you hear me, I think so. In this contribution we would like just to show you what kind of data we have available and maybe also we would like to indicate some challenges which could be points of further activities maybe under umbrella of this cost action. The contribution is caused by my colleague Jana Markova and also by Milan Holitski. Well the motivation for collecting of such data and analyzing them is that cooling towers and industrial chimneys in our country are now reaching their service life. Many of them has been built in 1960, 70s, 80s and the concerns of power producers are whether they can use and under which condition they can use such structures for further activities. Well of course maintenance plans should be based on estimates of remaining lifetime so this is the ultimate question. Well the producers installed monitoring systems or are using monitoring systems that provide a great amount of information about performance of key energetic devices meaning not only cooling towers and chimneys but also turbine generators and other key devices in power units and power plants. Well so this contribution is focused or tried attempts to provide an overview of operational so let's say basic statistical tools for data analysis, procedures of residual lifetime estimation and trying to provide overview challenges for optimizing monitoring systems. So just to give you a feeling of the current practice let me summarize parameters which are now observed for concrete masonry and steel structures. So talking about concrete, typically cracking is visually inspected and records are made, spalling of concrete, carbonation depths are measured and statistical data on concrete covers are also available. For masonry structures well deterioration of surface on and deterioration of units and mortar is also recorded for steel structures and steel reinforcement in concrete data on steel corrosion are available and for geotechnical aspects irreversible deformations and settlements of foundations are recorded. Well structures well large structures like cooling towers and industrial chimneys are typically divided into zones for which homogeneous deterioration conditions can be assumed. So typical division is let's say outside and inside surfaces of shell, columns, supports of cooling system and inspection galleries for cooling tower, similar division for chimneys and our preliminary observation is that current monitoring seems to be sufficient for detecting serious damage so no severe failures or even collapses were not observed but maybe such system or the present system provides redundant information and in this respect it should be optimized. Just to give illustration of data we have so well we have digitalized databases of monitoring data for the period of roughly speaking last 10 years and well very typically the period of 2 years between subsequent measurements is applicable. What is advantage of this data is that reference areas were introduced so this means that for spatial structures like shell of cooling tower they are given areas for which carbonation depth is for instance carbonation depth is periodically monitored. And in the graph just to see the data we have on horizontal axis we have time since construction so this is the age of chimney at the time of measurement in this graph and on vertical axis we have carbonation depth in millimeters and you may see that bi-colors are distinguished three chimneys which are in one power plant of different age so green triangles are for chimneys denoted by number three and blue chimney number two and red circles are chimney number one. Well just for illustrative purposes you can see here the outcome of FIB model for carbonation depth taken from one of the blettings of FIB just I would like to emphasize that this is based on prior information without any updating for now and you see mean trend of carbonation depth and dashed curves indicates plus minus one sigma from the mean. Well immediately from this graph we can see that there are many questions which are open and needs further analysis so we have subsequent years in which we observe we decreasing trend of carbonation depth so the explanation should be whether it should be attributed to measurement uncertainty or maybe is just a random outcome of selection of measurement areas and we also observe in the data some systematic errors which should be explained and maybe such data should be removed from further analysis. Of course the question is if we have a chimney of age of 40 years do we need do we need to measure carbonation depth in two years or not. Just another comparison from the data on horizontal axis now we have a height above ground level so the chimneys are about above 200 meters and the mean carbonation depth is on vertical axis and we see comparison between east and west surfaces for which because of wind driven carbonation the carbonation depth should be different according to theoretical model so we see observation and we see some indication of differences from the data. Well of course for practical purposes the statistical treatment of data should be should be relatively simple so the typical procedure which we are trying to introduce for the operator is should include the following steps test of outliers so if we have some observation which cause of which is non statistical so it has some technical reasons we should eliminate these data from the analysis correlation analysis which might indicate that some parameters are so correlated that it's sufficient to observe only one parameter representative parameter regression modeling so fitting of appropriate regression models on the basis of experience so for instance from FIB model we have we have indications of trends in carbonation depth so let's say it's a it's some nonlinear model and similar models are then fitted to empirical data confidence interval so once we have a fitted regression model we can identify confidence intervals of the curve. Well this slide is just to attract your attention also to to assessment of our evaluation of the data because we are dealing with with structures with large surfaces and that's why also a criteria for maybe classifying or for decision-making about structures need to take into account spatial variability of phenomena so this table is just indicating how the relationship between a carbonation depth and concrete cover is assessed for for spatial structure so we need some threshold value for let's say the difference between carbonation depth and concrete cover but we also need to identify extent of area for which this value is acceptable and now I would like to summarize some challenges which may be treated within this action so one one challenge is let's say purely technical so is selection of observed deterioration processes and appropriate and relevant threshold values is this selection appropriate or correct how well if you know that there are some correlations between cracking carbonation and corrosion progress do we need to observe all these all these processes and can we optimize threshold values in sense that this would lead to optimal maintenance plans appropriate method for monitoring is another is another point of concern so we need to balance between related costs and uncertainty in outcomes of the procedure well if you look at one specific time and we have a large surface so natural question is what should be the amount of observation at one time for components of let's say different areas so how to make distinction between shell of a cooling tower and columns supporting supporting the shell well another question could be what should be optimal time interval between measurements for different degradation processes so for carbonation ingress or for corrosion development do we need the same interval and what is the interval and well the maybe the last question in this open question in this presentation how can we use how can we utilize monitoring of similar structure so we have three well very similar chimneys in one power plant do we need to observe all the three chimneys or it's sufficient to to observe only one and maybe check our predictions less frequently for the other well tools which which we think should be applied for to answer these questions are some kind of modeling of spatial variability in deterioration processes we have experienced and applied these approaches in previous publications for simplified model of FIB given in FIB billetin 59 which is based on on while simple modeling of a zone so for a zone we assume that we can assume homogeneous deterioration conditions and then this zone is divided into elements and for each element we assume that that realization of considered random field so for instance carbonation depth is independent from the other element and we may define some hyper parameters which are which has common values for all the elements in the zone which are typical weathering conditions well another tool is in this action this is called ultimately value of information well in this graph this is just a very simple idea of balancing of costs of monitoring and benefits associated to obtained information so if we have on the horizontal axis if we have some decision parameter describing intensity of monitoring and we have more or less linear costs of monitoring and let's say some failure consequences which can be given in terms of repair or even failure consequences so then we optimize these costs and find some optimum intensity of monitoring and well for these studies what is relatively good information is that we have information on monitoring and repair costs and we also can describe failure consequences which are inevitable part of this this game and this optimization so well this that I would like to thank you for your attention okay