Photo courtesy of UT Austin
Ph.D. student Li Wang demonstrates the design coordination data capture prototype system in the Texas Advanced Computing Visualization Laboratory, at the University of Texas at Austin.

It often is said that experience is the best teacher, but what happens if the teacher is absent? How will the rising generation of construction professionals apply the insight of veteran practitioners in an industry where recent economic turmoil has driven droves of them to retire?

This is a problem researchers in my lab at the University of Texas at Austin have been studying, and we believe we have a partial solution in a problem-based learning tool that we have developed for mechanical, electrical and plumbing design coordination. We have a prototype and are ready to test and improve it through real-world application.

Decisions made and approaches taken in MEP design coordination largely depend on knowledge and expertise of professionals from multiple disciplines.

The MEP design coordinator—who usually represents the general contractor or the main mechanical contractor—coordinates the effort of collecting and identifying clashes and collisions between systems. He or she asks clarifying questions during coordination meetings and often proposes solutions. During the process the coordinator’s tacit and experiential knowledge frequently is called upon and transferred to less experienced members of the team.

In recent past, the design coordinator usually was an experienced engineer who knew how to differentiate between critical and non-critical clashes, as well as how to prioritize clashes by importance and provide suggestions to the team—or even make decisions, based upon his or her expertise and experience.

But increasingly, due to the recession’s depletion of the ranks of veteran engineers from the industry, as well as the rising use of building information modeling, general contractors have started to rely more and more on novice engineers to run conflict resolution sessions. Although those young engineers may be proficient in operating the coordination software systems, many have limited practical experience in MEP design and coordination.

Although the use of BIM in MEP design coordination has greatly increased the amount and quality of available data, significant experiential knowledge still is needed for efficient, high-quality decision making; yet the process for bringing that knowledge to the table is faltering.

One of my graduate students, Li Wang, has conducted a study comparing the behaviors of experienced MEP coordinators with novices on model-based design coordination. The results show that experienced coordinators can locate relevant information and identify external information sources more efficiently, as compared to the novice coordinators. Experienced coordinators also are able to perform more in-depth analysis within the model, based on their experiences.

Now my laboratory is investigating whether novices’ performance on coordination tasks will improve when experiential knowledge that has been extracted from past projects is made available to them through a software-enabled decision support system. Results show that such decision support significantly reduces the time spent on performing the tasks and brings increased accuracy to the solutions.

We are developing an innovative approach to capture, represent and formalize experiential knowledge in design coordination to inform better design decisions, improve collaboration efficiency and train novice designers and engineers. The approach will systematically capture expert decisions during design coordination in an object-oriented, computer-interpretable manner and leverage database and machine learning techniques for knowledge reuse.

We now have a prototype system that works as a plug-in for a widely used design coordination software system. It captures design coordination decisions and stores each instance directly to related 3D elements. We then store this information in a database of MEP clashes and related expert solution descriptions, and use the information to train algorithms to learn from the knowledge and, ultimately, provide novice designers with a problem-based learning platform to enhance their performance in design coordination tasks.

If you are interested in testing our prototype system and contributing to this research with additional MEP coordination cases, I encourage you to contact me by e-mail at

Fernanda Leite Ph.D., is an assistant professor in the Civil, Architectural and Environmental Engineering Dept. at the University of Texas at Austin.