Roof fastener systems are comprised of metal screws and plates used to attach roof membranes, cover boards, and insulation. These systems can have an adverse impact on the thermal performance of roof assemblies, as the components create thermal bridges that bypass the thermal resistance of insulation in the roof assembly. This in turn allows heat to escape at an accelerated rate, flowing outward in cold weather and inward in warm weather.


There is a growing trend in the application of Unmanned Aerial Vehicle (UAV) systems for visual inspection of building facades. Current practices remain at a low efficiency to manage the large amount of UAV-collected close-range façade images to support the inspection and documentation of façade anomalies such as cracks and corrosions. This paper proposes a GIS-based two-step procedure to streamline the process of the management of UAV-collected images for supporting building façade inspection.

Nowadays, unmanned aerial vehicles (UAVs) are frequently used for periodic visual inspection of building envelopes to detect unsafe conditions or vulnerable damages. Inspection practitioners have to manually examine the large amounts of high-resolution images collected by UAVs to identify anomalies or damages on building facades for reporting and repairs. The computer vision and deep learning technologies have emerged as promising solutions to automate the image-based inspection process.


This article analyses the integration of green building with the largest low-income housing production programme in the US and the innovativeness of state housing agencies. Drawing on policy innovation literature, panel data and regression analysis are employed to quantify associations between state-level characteristics and the adoption of green building criteria into the Low-Income Housing Tax Credit (LIHTC) programme.

The management of society’s waste, once overlooked as a burden to simply dump onto the Earth, has grown in public awareness during the last several decades as critical to sustainable planning. For the 2020 Solar Decathlon Design Competition, we are excited to work with a client that is setting the gold standard for contemporary waste management in our home state of Virginia.

In order to provide a holistic design approach, Virginia Tech's team is composed of a variety of backgrounds, ranging from architecture and building construction to macromolecular science and civil engineering. As a team, we intend to positively impact the surrounding community with our design. Developing a community within our team is crucial to our success. Team members participated in various design charrettes, as well as individual research, which allowed our comprehensive design for the Eco-Park Learning Center to integrate all aspects of life and community.

The Mycorrho-grid is a blockchain based microgrid inspired by the way trees collect and distribute resources through mycorrhizal networks in the forest. It was first developed in conjunction with Virginia Tech’s grand-prize winning TreeHAUS entry in the 2019 Solar Decathlon Design Challenge and is expanded upon here with a focus on further potential for biologically inspired functionality. The baseline case as simulated in the competition is a 12-unit multi-family microgrid with a shared 50kW rooftop solar array and fixed price return from the grid.

There has been a trend in applying imaging techniques in building facade inspection, such as laser scanning, infrared thermography, high definition camera, etc. Utilizing such techniques creates a large amount of multi-sourced image data for building facade inspections. However, there is a lack of an effective way to manage these massive image data in aspects of spectral, spatial, and temporal data. As GIS is specialized in the storage and analysis of spatial and raster data, it can provide a good working platform for archiving and processing massive image data.

The role of building science education examined in the context of past DOE student competitions.

A presentation part of panel discussion at the 5th RBDCC. This paper presents findings from a meta analysis of DOE Student Competition Guidelines and competition winners in terms of Building Science keywords and requirements.