Building Construction Professor Awarded $306,043 from National Science Foundation

Assistant Professor Dr. Ioannis Brilakis was recently awarded a $306,043 grant from the National Science Foundation (NSF) to study infrastructure modeling. The three-year project titled “Reciprocal Reconstruction and Recognition for Modeling of Constructed Facilities” (NSF Grant #1031329) is an interdisciplinary project done in collaboration with Professor Patricio Vela from the School of Electrical and Computer Engineering.


The motivation behind this research project stems from the need for viable methods to map and label existing infrastructure. The National Academy of Engineering recently listed Restoring and Improving Urban Infrastructure as one of the Grand Challenges of Engineering in the 21st century. Two of the greatest issues that cause this grand challenge are the need for more automation in construction, through advances in computer science and robotics, and the lack of viable methods to map and label existing infrastructure. Over two thirds of the effort needed to model even simple infrastructure is spent on manually converting surface data to a 3D model. The result is that as-built models are not produced for the vast majority of new construction and retrofit projects, which leads to rework and design changes that cost up to 10 percent of the installed costs. Any efforts towards automating the modeling process will increase the percentage of infrastructure projects being modeled and, considering that construction is a $900 billion industry, each 1 percent of increase can lead up to $900 million in savings.


Dr. Brilakis joined the School of Building Construction in Spring 2009 and holds a joint appointment with the School of Civil and Environmental Engineering; he is the Director of the Construction Information Technology Laboratory (CITL) at Georgia Tech. In 2010, he received the prestigious NSF CAREER  Award for his project “CAREER: Visual Pattern Recognition Models for Remote Sensing of Civil Infrastructure,” a five-year project to enable automated, model based recognition of construction objects. His research interests include: computing and information technologies for the architecture, engineering, construction, and facilities management industries (AEC/FM); sensing and data collection for civil infrastructure development; and visual pattern recognition technologies for construction site multimedia data analysis.


A brief description of the research project is below:

The research objective of this project is to evaluate whether a novel framework proposed by the PIs can progressively reconstruct a reinforced concrete frame structure into an object-oriented geometric model, for the purpose of automating the Building Information Model (BIM) making process of constructed facilities in a cost-effective manner. According to the proposed framework, the modeler videotapes the structure from all accessible angles to minimize occlusions. During this stage, the structural members (concrete columns and beams in this study) in the resulting stream of images are detected and their occupying region is marked in all images. These regions are used to establish correspondence at the object level across images, and solve the rough registration problem efficiently. Line-based structure from motion is then applied to the result to produce a rendered 3D view of the structure with the recognized regions marked. This loops back to the detection of structural members, which can now be also performed on the spatial data covered by the visually marked regions. The result is more robust element detection (by combining visual and spatial detection results), and consequently improved element matching and reconstruction. The resulting object-oriented model is expected to be an accurate 3D representation of the structure with the load bearing linear members detected. This model is provided to the modeler, who can then use it to complete the model making process


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