An asset management program should be developed accordingly to the client’s goals and objectives. It consists of determining the selected area of study, type of system and the quality of data used for evaluation. Before a condition assessment can be determined, an inventory of assets needs to be established – maps, etc. are helpful.  So now you have a map of your water and sewer system and you want to develop a useful system for asset management.  Depending on the accuracy wanted, the data can be gathered in many ways ranging from onsite field investigation which could take a lot of time, to using existing maps, using maps while verifying the structures using aerial photography and video, or field investigations. But most local governments still lack data.  You cannot dig up pipe, or do a lot of destructive testing on buried infrastructure.  So what to do?

The reality is that you have a lot more data than one thinks.  For one thing, most utilities have a pretty good idea about the pipe materials.  Worker memory can be very useful, even if not completely accurate.  In most cases the depth of pipe is fairly similar – the deviations may be known. Soil conditions may be useful – there is an indication that that aggressive soil causes more corrosion in ductile iron pipe, and most soil information is readily available.  Likewise tree roots will wrap around water and sewer pipes, so their presence is detrimental.  Trees are easily noted from aerials.  Likewise road with truck traffic create more vibrations on roads, causing rocks to move toward the pipe and joints to flex.  So with a little research there are at least 5 variables known.  If the break history or sewer pipe condition is known, the impact of these factors can be developed via a linear regression program.  That can then be used as a predictive tool to help identify assets that are mostly likely to become a problem.   We are working on such an example now, but suspect that it will be slightly different for each utility.  Also, in smaller communities, many variables (ductile iron pipe, pvc pipe, soil condition…) may be so similar that differentiating would be unproductive.  That also remains to be seen, which brings up another possible variable- the field perception – what do the field crews recall about breaks?  Are there work orders?  If so do they contain the data needed to piece together missing variables that would be useful to add to the puzzle?

After all we want to avoid this before it happens….