Infrastructure Life Expectancy: Frequency Distribution?

In summary, the conversation discusses the development of a costing model for water storage tanks with a limited amount of empirical data on their life expectancy. The collected estimates from manufacturers range from 25-50 years, and the individual is seeking to build a Monte Carlo simulation algorithm using this data. Suggestions are made to post the question in a math forum and to plot a standard deviation curve, although it is noted that the data may not be normally distributed and assumptions cannot be made due to the small sample size.
  • #1
TheRobsterUK
7
0
I'm currently working on a costing model for water storage tanks. The type of tanks I'm looking at have a certain life expectancy but due to a limited number of installations there isn't much empirical (observed) data about how long they can actually be expected to last before needing replacement.

I have collected estimates from a number of manufacturers who have each given an expected range of life expectances. Generally these are between about 25-50 years, as shown below:

http://www.sudsolutions.com/misc/tanks.JPG

Now what I'd like to do is build some sort of Monte Carlo simulation algorithm using the data in the table above. But I am not sure how to translate that data into a frequency distribution. Does anyone know a formula that I can plug the above numbers into in order to be able to get some kind of frequency distribution? Or am I going about this the wrong way?

Any ideas appreciated. :)

Cheers
-Rob
 
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  • #2
You might want to post this question in the math forum. It may get more responses. I guess the place I would start is just to calculate the mean and SD of each and plot a standard deviation curve for each range. I did a quick histogram and the distributions don't look normally distributed though. This is where someone well versed in stats can really help. With such a small sample to pull from, what assumptions can be made that are valid?
 
  • #3
Thanks, I've reposted in the General Math forum as advised.

https://www.physicsforums.com/showthread.php?p=1313697#post1313697

With regards to assumptions, I can't really make any...this is the only data that's available so I don't have much choice other than to work with a small sample and assume that the data is reasonably accurate.
 

1. What is infrastructure life expectancy?

Infrastructure life expectancy refers to the estimated duration of time that a specific type of infrastructure, such as roads, bridges, and buildings, can function effectively before needing to be replaced or significantly repaired.

2. Why is it important to understand infrastructure life expectancy?

Understanding infrastructure life expectancy is crucial for planning and budgeting purposes. It allows governments and organizations to anticipate and prepare for necessary repairs or replacements, and to allocate resources accordingly.

3. How is the frequency distribution of infrastructure life expectancy calculated?

The frequency distribution of infrastructure life expectancy is determined by collecting data on the age and condition of various types of infrastructure and then organizing it into different categories or groups. This data is then used to create a graph or table that displays the number of infrastructure units falling within each category.

4. What factors can affect infrastructure life expectancy?

There are several factors that can impact infrastructure life expectancy, including the quality of materials used during construction, the level of maintenance and repairs performed over time, and external factors such as weather and natural disasters.

5. How can we improve infrastructure life expectancy?

To improve infrastructure life expectancy, it is important to invest in high-quality materials and construction methods, regularly perform maintenance and repairs, and implement measures to protect against external factors. Additionally, having a comprehensive understanding of the life expectancy of different types of infrastructure can help inform better decision making and resource allocation.

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