The Cone of Uncertainty

I recently sat in a call for a project where two separate cost consultants where being given the opportunity to engage the BIM process and take advantage of any model-based data they could derive. The client was allowing them some time to engage the process and use the project as a pilot to identify their own gaps, whatever those may be.

After some talk, a leading voice emerged making the claim that the traditional method would be preferable as it would be the fastest. The comparison focused on expediency to produce the same product that was required by industry standard and their scopes. Based on these parameters, this person was probably correct. The chips were stacked against the new process as the lack of experience in the tools alone would doom the surveyors to a slower process than usual.

These parameters, however, failed to make a true comparison since the two products would be fundamentally different. The traditional method would lead them to a reasonably accurate pricing estimate, but with some considerable allowance due to uncertainty. The new process would require this allowance as well, but the introduction of the transparency afforded to us by a design model being made available should move the needle.

In software development project management, the Cone of Uncertainty represents the ‘…evolution of the amount of uncertainty during a project.‘ (source) The premise is that at the onset of a project, little is actually known about what the product will look like in the end and how it is going to be made. As we move through the process of design and development, decisions are made, paths are selected until eventually unknowns ‘…decrease, reaching 0% when all residual risk has been terminated or transferred. This happens by the end of the project i.e. by transferring the responsibilities to a separate maintenance group.’

Source: via Federico on Pinterest

The same article states that “Most environments change so slowly that they can be considered static for the duration of a typical project, and traditional project management methods therefore focus on achieving a full understanding of the environment through careful analysis and planning.” This strikes a clear parallel with the traditional design stages of the design delivery process. A lot of work is done through design development and construction documents to more clearly define a building’s scope of work. ‘Careful analysis and planning’ is done, sometimes for years, to come up with a set of specifications and drawings that will act as the basis for design in the bidding and construction phase. 

It continues “Well before any significant investments are made, the uncertainty is reduced to a level where the risk can be carried comfortably.” It stands to reason then that a rich source of insight into the designer’s intent – as is the model – would go a long way to move the dial farther to the right in the above graph. Even in a model of poor craft, having a clear understanding of the ‘gaps’ in the design is of value.

Model and data driven approaches to studying how a model was built and the intent behind it then should derive a richer context for the quantity surveyor, essentially ‘de-risking’ the estimating process by increasing transparency and democratizing access to information.

The person described above identified risk in the many unknowns that came with the new process and new information, and would act to mitigate that risk by recommending we don’t engage it. Instead, he should see the opportunity to provide a more complete estimate at an earlier time in the project schedule. Essentially, a more complete estimate, earlier in time and with less unknowns. This, regardless of whether a model existed, should be at the core of their research and development efforts internally. In this case, they are being given the opportunity to work with a client jointly on that R&D. I hope they take that chance.

Visualizing Building Information – Clash Detection

One of the more useful applications of building information we’ve been experimenting with has been visualization. We first started experimenting with visualizing building data back in 2010. You can see a presentation I gave at KA Connect 2010 on the research we were doing on this front with SOM here. The research we did on that project has evolved into a full fledged web based tool that is helping them manage their thousands of gigs of BIM data production.

Since I’ve become obsessed with other applications of visualization. Let’s look at what it can do to clash detection or conflict analysis. This is an area I spend a lot of time on since it is at the core of our Project Consulting services. Model progress comparisons, conflict analysis, audits, craft, etc… are all critical parts of the continuous BIM data management responsibilities that go along with BIM project delivery.

Of course, the objective behind visualization is to help emerge some other interpretation of the data that can provide further insight, whether it be through a new pattern that is recognized, grouping, or something else. It must teach us something new by viewing the data from a different perspective.

Here are a couple of examples:

1. Plotting the x and y coordinates of clash instances

clash detection visualization by x and y coordinates

clash detection visualization by x and y coordinates

Though seemingly obvious, the above image helps us tremendously to quickly understand a simple clash test. By plotting the x and y coordinates of each clash instance, and coloring them by the discipline, we are able to quickly identify the area and model that have the most issues. On the image above, the lighting model has an area of severe congestion on the north-east of the building. Without having gone through every clash, I already understand, at a high level, where most of my time should be spent.

Conflicts by x and y

Conflicts by x and y

Similarly, on this analysis we see an x y plot that quickly categorizes the source files of the clashes by color, labels the elements by name, and indicates the severity of the clash by the size of the bubble. This analysis looked at conflicts between curtain wall components against the building’s superstructure.

2. Ranking BIM elements by number of conflicts and severity

BIM visualization by element clash severity

BIM visualization by element clash severity

This image takes each distinct BIM element and ranks it based on number of identified conflicts and sizes them by severity. From this, we are able to quickly assess the type of elements that may need the most amount of attention. Not to imply that clash quantity is the single source for qualifying a coordination issue, it is not, but elements that have a high number of clashes of high severity should be addressed.

3. Model version comparison analysis

BIM version comparison

BIM version comparison

In this case, we are comparing one model version versus a previous one. We are interested in seeing how much, and what has changed between submissions. We see, very quickly that on this MEP model, most of the work has gone into re-routing plumbing systems and relocating electrical fixtures. This can be incredibly helpful on large projects to understand model progress and keep an eye on craft.