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Predictive Models for Asset Lives

29th September 2016


Travel through Europe and you will find many iconic buildings 800 to 1200 years old.  Some took 100s of years to complete. These building stand as monuments to the builders.  Again each has many centuries ahead of them with the only threats to their lives being the way we humans have altered the world.  So what can we learn about asset lives?  There is another aspect to these buildings, it is the occupants over the centuries that have loved and cared for those buildings.  Not far away are the ruins of buildings from the same era that have been left to decay.  Why have these iconic buildings remained?  Essentially it is because they are still providing the service, or purpose they were built for.  The occupants have replaced components as required to ensure the building remains serviceable.

In our modern world we are becoming increasingly concerned about sustainable development and Asset Managers seek to get the most out of the assets created.  The concept of “making buildings last forever” is becoming the driving force of asset management. In order to do that we need to understand what investment is necessary to keep our asset components in good condition so that the building can continue to function as intended.  We need to understand how assets decay.  There are a multitude of influences on asset decay, very few of them are understood. There are macro influences such as climate, utilisation and integrity of construction.  And influences such as care or maintenance.  Each component is unique.  We also need to understand the other aspects of each component that leads to deterioration in a different sense.  Is it modern? Are there better technologies?  Are there better materials?  The component may be sound and doing its job well but no longer satisfies current styles and thinking.  The rapid growth in electric technologies is an example.  Incandescent light bulbs are a thing of the past.  Thus component replacement is driven by far more than just condition.  These other influences on asset life need considered thought.  It may not be sufficient to wait for the asset to “fail” before replacement.  Sustainability and efficiency of investment may suggest that the prudent strategy would be to replace early to gain the benefits of new technologies as soon as possible.  In these contexts, asset life takes on new meaning.

As an industry we have the benefit of many years of observing asset behaviour and in our uncoordinated way have developed models to forecast when investment will be necessary to restore components to ensure the building will last forever.  Each model tends to focus on a specific asset group such as roads, pipes or buildings.  I have had the privilege of working with large data sets and seen the development of deterioration models.  Experience has shown that the simpler the model the better.  There are just too many drivers for asset replacement to try and make the model all embracing. 

The most common model for building components is the condition model where the condition of the component is the primary driver is determining the remaining life of the asset.  The condition model should not require an understanding of the age of the asset as condition is more a function of how the asset is cared for than how old it is.  For building plant and equipment a model based on the age of the asset is may be more appropriate as a visual assessment of condition rarely has any relationship to the life of these components.  Sophisticated users will collect operational information on items of plant and equipment a model based on utilisation, load and run hours would be selected. 

 I am concerned that some models embedded in software packages do not really assist the asset manager.  Essentially they require the asset manager to determine and enter the manner of asset deterioration.  The model then faithfully reports the outcome.  We have knowledge in our systems of billions of dollars of assets.  This knowledge should be the basis of deterioration.  The models should be based on measured facts.

I have found that business drivers may have greater influence on asset replacement and models should allow recognition of risk parameters that require earlier consideration of replacement, or intervention where the in service status of the assets is required to be kept at a high standard.  The models should also allow the user to recognise that maintenance strategies can extend the life of the asset.  The models are guidance.  Ultimately the models inform the asset manager of upcoming requirements for investment.  It is then up to the Asset Manager to optimise, prioritise and develop the works programme. 

What have we learnt from past centuries of building development?  Understanding, knowledge and planning supported by analysis can make it possible for a building to last forever.




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