BIM analytics

Where technology and thorough processes intersect.

By Matt Miyamoto

Building information modeling (BIM) is a 3D, model-based approach to projects that has dramatically changed the way architects and engineers do their work. Building information models enable architects and engineers to visualize and communicate their designs to key stakeholders in compelling and engaging ways. These models contain richer data than 2D drawings, but that rich data can also translate into large files, sometimes in the range of gigabytes of information.

Fortunately, technology and cloud computing advances have evolved concurrently with building information modeling. In fact, cloud computing is the key enabler of two important BIM trends:

Sophisticated analyses — In the past, civil and structural engineers relied on manual calculations and tools such as Excel and Access databases running on local computers to conduct calculations such as thermodynamic or structural analyses. This approach was inefficient at best and, in some cases, it was simply impossible due to inadequate computing power on local computers.

Today, it’s common to store building information models in the cloud. Cloud-based storage is affordable and a good match for large BIM files. In addition, cloud-based computing resources are far more powerful and economical than office computers. A BIM analysis can be run in the cloud and, once completed, users can be automatically notified by email.

Increased collaboration among project stakeholders — When building information models reside in the cloud, it’s also easy for civil and structural engineers to share both models and analyses with other project participants using collaboration software tools. By obtaining feedback earlier in the project life cycle, teams can increase the predictability of projects and identify problems or issues earlier on in the design process, saving time and money on revisions and delays.

Collaboration can also minimize the number of unknowns in a building project. For example, during a road construction project, BIM analysis revealed previously unforeseen conflicts with a pipeline. The resulting construction phasing issues could be addressed proactively earlier in the project, reducing delays and change orders.

While BIM analytics can be incredibly useful for civil and structural engineers, it’s essential to recognize that generating valuable analytics isn’t just a technology issue. The old computer science adage of “garbage in, garbage out” definitely applies here. Unless the right data exists within a BIM, the resulting analytics won’t be helpful for project stakeholders.

With that in mind, following are four process and communication-related tips for more effective BIM analytics:

Engage in upfront communication about collaborators’ needs — A project kickoff is a good time for team members to discuss the data aspects of the BIM. Developing clear expectations about what types of data are needed, as well as the level of detail that’s required, will increase the likelihood that the resulting BIM analytics will be valuable for stakeholders. A best practice is to create a BIM standard for each project that outlines the type of information that must be provided and the level of detail needed in the data.

Create a plan for collaboration — This is especially important when a project includes cross-disciplinary collaboration. For example, engineers may be working on the exterior of a building while architects are working on the interior. Without a collaboration plan, their BIM-related efforts may not align properly.

As team members add data to the BIM, they must know how to deliver the information and where to store the information in the model. Details about how to structure these data-related workflows should be captured in a collaboration plan.

Develop a plan for model validation — Model validation is a regular checkup related to the quality of the model and its data. Healthy models aren’t bogged down by extra and unnecessary information. Several software packages include “housekeeping tools” that can identify redundant data in a BIM, check for background data, and identify data that is growing without a valid reason.

Another best practice related to model validation is establishing planned dates for collaboration to keep models in sync. On those collaboration days, the team members must ensure that the model is up-to-date. This activity helps ensure that the model remains in “good health” and can support robust analytics.

Consider how data will be kept up-to-date during the building life cycle — On some projects, the BIM is transferred to the building owner once the construction phase is complete. If a BIM will eventually be used for facilities management, for example, the architect or engineer should discuss what data and analytics will be needed to support building management and automation. These conversations must also cover how the owners will keep the data current over time to facilitate generation of BIM analytics in the future. Engaging in a dialogue early in the project is always advisable.

The transition from 2D drawings to BIM has had a profound and positive effect on the work that civil and structural engineers do. Thanks to BIM analytics and collaboration, these professionals can complete projects with greater confidence and fewer delays.

Matt Miyamoto, P.E., is a manager and senior application specialist with IMAGINiT (www.imaginit.com). Prior to joining IMAGINiT, he worked as a civil engineer, using Autodesk Civil 3D on a variety of projects, including site development, roadway improvements, and infrastructure design. With more than 15 years of experience in the civil engineering industry, Miyamoto provides training, consulting, technical support, and implementation strategies for organizations transitioning to Civil 3D. He is an Autodesk Certified Instructor (ACI) as well as an Autodesk Certified BIM Specialist: Roads and Highway Solutions. Additionally, he is an Autodesk Certified Professional for AutoCAD and AutoCAD Civil 3D.