The construction industry has been revolutionized by computers, particularly in the design process – the first and the most crucial part of any construction – where modern commercial software has come into heavy play. Building Information Modeling (BIM) also has become instrumental in documenting design, enhancing customer experience, and improving product functionality in capital projects. High-quality building models have been achieved through this managed process that involves multiple participants from different disciplines and backgrounds.
As the planning and design process is long, involving multiple professionals with different priorities of design, modelers often produce models that incorporate major conflicts. Resolving these conflicts – which is a costly process – results in billions of dollars of annual loss in the U.S. design and construction industry.
To eliminate or minimize such losses, the modeling process needs to be effectively managed. This kind of management requires an ability to closely monitor the modeling process and correctly measure the modelers’ performance. Unfortunately, currently existing methods of performance monitoring in building design practices lack an objective measurement system to quantify modeling progress.
To remedy this nationwide problem, Georgia Institute of Technology scientists Dr. Saman Yarmohammadi and Professor Daniel Castro-Lacouture from the School of Building Construction devised and developed a highly novel automated design performance measurement framework, and successfully identified the optimal modeling team configurations where modelers are selected according to different factors including their availability, past performance, and the requirements of remaining modeling tasks. This outstanding work, which is an enhancement of an innovative methodology they first introduced in 2016, was published in the research journal, Automation in Construction.
In this study, the researchers first established information protocols for accurate measurement of modeler performance in design settings. Based on these requirements, they implemented their proposed methodology by developing a plugin for Autodesk Revit which the most adopted BIM authoring software worldwide. Next, they conducted an experiment to capture performance data using the developed Revit plugin. The recorded data were then analyzed and used to identify the optimal design team configuration for a specific project. To this end, Dr. Yarmohammadi and Professor Castro-Lacouture introduced a new approach using the Earliest Due Date (EDD) sequencing rule in combination with the Critical Path Method (CPM).
From their experiments, the authors noted that the Revit plugin they developed was fully capable of recording several modeling events, such as element changes, command executions and errors, in real time. It also possesses the potential to bring opportunities for design organizations to assign training resources more efficiently, monitor modeling process in real-time, and visualize modeling progress in unprecedented details.
In summary, Dr. Yarmohammadi and Professor Castro-Lacouture presented an accurate and robust technique in the form of an Autodesk Revit software plugin, that enabled design project managers to identify the optimal modeling team configurations using empirical performance information for a given project.
Their research has proved to be a significant advance over previous efforts, presenting a novel application programming interface (API)-enabled approach to (a) automatically collect detailed model development data directly from BIM software packages in real-time, and (b) efficiently calculate multiple modeling performance measures during schematic and design development phases of building projects. In this way they established a technologically outstanding and highly practical approach that enables efficient, prompt and accurate evaluation of design modeling performance.
Saman Yarmohammadi, Daniel Castro-Lacouture. Automated performance measurement for 3D building modeling decisions. Automation in Construction, volume 93 (2018) page 91–111.Go To Automation in Construction