5D BIM For Capital Planning

For construction projects, integrating all relevant dimensions holds promise for improved outcomes.

By Ray Diwakar
From the June 2018 Issue

construction projects
(Photo: Thinkstock)

Construction and facility professionals are challenged by the ever-evolving AEC industry demands of increasing productivity, quality, and efficiencies with project delivery all the while minimizing overall costs. To stay abreast of the evolution, it is imperative to implement data innovations on top of up-and-coming concepts such as 5D BIM to improve insights into project costs and risk elements across all stages of a project’s life cycle for maximized efficiencies and improved capital plans and budgets.

Data Innovations

There are many challenges when developing accurate capital plans and budgets for future new builds, renovations, and building maintenance and repairs. However, with recent data innovations, facility executives and building owners are now able to rely not only on proven historical data but also on new insights provided by artificial intelligence (AI), machine learning (ML), and data science. These advances foster better decision making through all stages of the building life cycle. This includes developing comprehensive capital plans and resource allocation schedules based on traditional material, labor, and equipment as well as verifying productivity rates, time, and location factors.

Facility management is growing smarter thanks to AI. This up and coming technology can now preemptively identify an equipment’s need for replacement or repair prior to probable breakdown by using historic and real-time data. Harvard research has shown AI enhances facility management response times by up to 97%.

AI can be leveraged even further when combined with the concept of Machine Learning (ML). ML enables computers/programs to learn from historical and predictive data, providing insights into construction trends and building materials, labor, and equipment costs. These insights allow facility managers and owners to see which factors will have direct impact on the future including: cost escalations, labor rates, and location. If the same general maintenance is performed at various locations across the country, ML allows owners and construction professionals to visualize trends across all locations to better understand what adjustments need to be made to avoid any unknown surprises in the future.

The last major component of data innovation revolves around data science and analytics which can be applied at various stages within the building life cycle. By applying predictive algorithms to historical trends based on time and location of the project, data science allows facility managers, owners, and their respective design teams to intuitively predict costs, productivity rates, and time schedules for various scenarios.

Because of these data innovations, facility managers can schedule general maintenance, plan for unexpected repairs, and keep facilities modernized with routine renovations. Serial builders have less worry about what the price of a building will be in one state versus another, or if a project today will cost dramatically less than a project two years from now. For instance, predictive analytics allow owners and facility managers to make smart plans while providing increased productivity and accuracy of only 3% up to 36 months in the future.

The progression of data science allows for precise project planning while staying on time and on budget, even with the known complexities of commodity prices and labor rates.

Emerging Technology

Advances in emerging technology are streamlining and shaping the future of the construction industry. According to a recent McKinsey study, projects generally take 20% longer to finish than scheduled and are coming in up to 80% over budget. Relying on the traditional 3D modeling system of what is being built is no longer sufficient. That is where Building Information Modeling (BIM) comes into play. BIM improves traditional project numbers, and more organizations are recognizing the value added in streamlining design, cost and scheduling in the pre-construction process.

construction projects
(Image: Gordian)

Coupled with data innovations, facility executives are also taking advantage of the emerging concept of 5D BIM—applying 3D designs with time schedules (4D) and costs (5D) for more accurate project plans and budgets. Because of this concept, project stakeholders no longer have to work in silos when developing capital plans and project schedules.

With the introduction of BIM, traditional 2D architectural drawings—modeled in programs such as Revit for 3D concepts—can be enhanced and expanded by way of software platforms for more intuitive project planning. As appropriate costs, productivity rates, and time schedules are mapped to familiar 3D models, project stakeholders gain increased visibility and a streamlined process for improved efficiencies and planning. There are 5D BIM platforms on the market that bring together the design, planning, and finance in real time, enabling users to make intuitive, accurate, and timely decisions.

Moving Construction Planning Forward

As the construction industry continues to evolve from leveraging outdated methodologies to complex multi-faceted approaches, facility executives and owners are approaching a world of opportunity. Combining historical knowledge with data innovations like AI and data mining into integrated platforms for robust 5D systems will lead with better capital plans, budgets, and projects.

And, by combining data innovations with integrated innovative software platforms, users have insights into all stages of the entire project to allow for smarter planning and proper budget allocation no matter the project circumstance.

construction projectsDiwakar is director of data innovation for Gordian, a provider of facility and construction cost data, software, and expertise. He earned his Bachelor’s Degree in Civil Engineering in India and attended Washington State University for his Master’s Degree in Architecture. After graduate school, Diwakar was involved with several software startups. His career in enterprise software has been deeply rooted in data and data management.

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