Best Interior/Tenant Improvement & Project of the Year Finalist: Capital One Incubator Recruiting Office at University of Maryland
Capital One Incubator Recruiting Office, University of Maryland
College Park, Md.
Best Project and Project of the Year Finalist
Owner: JLL (On Behalf of Capital One Workplace Solutions)
Lead Design Firm: Gensler
General Contractor: Turner Construction Co.
MEP Engineer: McKinney & Co.
Engineering Consultant: Inversity Consulting Engineers
Subcontractors: Azteca Carpentry (Drywall, Rough Carpentry and Ceilings); Rosendin Electric; Kensington Glass Arts (Glass and Glazing); Fidelity Engineering (Mechanical); Columbia Woodworking (Millwork); Baltimore Steel Erectors
Part of the revitalization of the neighborhood surrounding the University of Maryland, this $1.2-million, 7,500-sq-ft, open-concept workspace offers what the project team calls cutting-edge research opportunities in machine learning and data science and also connects students with industries that are eager for new talent.
Appropriately for a facility focused on rapidly evolving technology fields, a challenging fast-track project schedule left little room for error. Adopting a team-wide lean scheduling approach and collaborative project management tools, the project team synchronized milestones and resolved potential constraints before they turned into costly delays. To minimize uncertainties, subcontractors’ scopes of work went beyond conventional details to include key submittal, constructibility and quality issues and requirements.
Procedures and methods for material fabrication, installation and quality control were closely scrutinized as well. For example, initial mock-up installations for aluminum door frames, full-height glazed partitions, a ceiling baffle system and polished concrete floors provided a basis for quality standards that were strictly enforced through completion and final acceptance.
The high level of communication and coordination was exemplified in many ways, the team says, including meeting an owner’s request to minimize penetrations for new overhead cabling, power and utility systems in the exposed 25-ft-high ceilings and 6-in.-thick, foil-faced insulation, which would remain unpainted. To coordinate exact hanger locations for all suspended technology and building infrastructure systems, subcontractors located and verified final rough-in locations for hundreds of devices and equipment.
Safety likewise remained a paramount concern during the 15-month construction phase, according to the team, with front-line workers actively engaged each day in promoting safe practices for themselves and their peers. Despite the hectic schedule and the daily presence of more than 35 trade workers performing intricate work within a limited area, the project totaled 4,367 staff-hours with no recordable incidents or lost-time accidents.
ENR’s judges cited the project’s quality-control program, which spanned the full schedule from preconstruction to close-out. One judge said that quality control is “something that’s very important to make sure you finish your project on time.”
The intense focus on quality helped the team to complete the project ahead of schedule and on budget. It also helped produce what the team says is an attractive, efficient space that maximizes collaboration and creativity. Those are features that ENR’s judges noted are fundamental to any type of business incubator. Designed for both flexibility and a fluid learning philosophy, the workspace has more than 30 sit-to-stand desks that can convert to a large meeting area and multiple 80-in. high-definition video screens that support research and instruction. Other features include a kitchen, lounge, conference and huddle rooms, lab space and a game room.
The incubator’s most innovative focal point is a physical design experience wall, or feature wall, that the team says displays beautiful visualizations along with data-driven experiments and presentations. The feature wall celebrates researchers’ work by providing a platform to highlight exciting projects and acts as a connection between the abstract concepts used in machine learning and the physical experience of witnessing the work.