The research objective of this Faculty Early Career Development (CAREER) award is the creation of pattern recognition models that will automate the recognition of civil infrastructure-related elements based on the knowledge of 3D surfaces from remote sensing, and visual features from pattern recognition. In other words, the intellectual merit of this project lies in the creation of the missing link between remote sensing and pattern recognition that will automate the transformation of 3D surfaces into information rich, 3D element models with the help of machine vision. The main contribution of this project is that, instead of manually recognizing each element every time it is encountered, we need only recognize its characteristics once and automatically detect it each subsequent time. This is analogous to defining an alphabet (letters = characteristics) so that this project will build the words (element models) and find them in a text (3D surface), instead of having to manually find the words in every text we encounter. The benefit comes from the ability to reuse the known letters (characteristics) and words (element models) every time we have a new text (3D surface).
The immediate advantage that will result from this work is the ability to automate the element recognition step of the "as-built" model generation process. 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% 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% of increase can lead up to $900 million in savings.
The outcomes of this project entail the creation of visual pattern recognition models for a variety of construction object types. The purpose is to assist as-built modelers of facilities by automatically recognizing the common and more frequent objects automatically, leaving only the specialty items to the hands of the modeler. By applying the results of this project, engineers will be able to automate the process of generating 3D as built models of specific built infrastructure objects ( e.g. beams and columns) by processing visual data (digital images and video clips). Other than automated as built modeling of built infrastructure, the results of this project are useful for other practical domains in the area of civil engineering including automated structural health monitoring, construction project progress monitoring, safety management and quality control of structural elements. As the practical example of the project, the results might be used to automatically extract measurements from construction jobsites. One specific case is conducting measurements in roof industry. A roofing contractor typically needs to acquire as-built dimensions of a roof structure several times over the course of its build because a structure is never built to the exact drawing dimensions. In the construction phase and in order to digitally fabricate sheet metal roof panels, the contractor has to measure end-to-end dimensions of boundaries of every roof plane with a certain level of accuracy (i.e., errors less than ±2 cm). This is necessary to be able to cut sheet metal coil such that different pieces perfectly fit together. Obtaining these measurements using the exiting roof surveying methods could be costly in terms of equipment, labor, and/or worker exposure to safety hazards.
