UTC 2022 Funding - Cycle 2 Research Projects

Project Number: CY2-UNM-02
Project Title: Automated Quality Assessment of Precast Members using Lidar and Augmented Reality
Performing Institution: University of New Mexico
Principal Investigator: Fernando Moreu and Su Zhang
Proposed Start and End Date: 10/01/2024 to 09/30/2025
Project Description: Prefabrication practices have been developed to manufacture concrete components of infrastructures in factories, transporting them to construction sites, and ultimately assembling them as final products onsite. However, there are a few problems with these processes, such as inaccuracy in benefit evaluation methods, and difficulties in the quality management of prefabricated construction projects. If we could track the quality in the fabrication process, then it is easier to keep a permanent record from cradle to grave for future implementations.

Construction, fabrication, and final product tolerances describe the dimensional relationships of each precast component that make up a whole structure at the different stages of production which will ultimately affect their durability and service life. It is crucial that the form’s dimensions are correct so that the manufactured product has the correct tolerances. Also, it is important to have a clear understanding of the appropriate product tolerances before a project begins, along with the correct interfacing tolerances and erection tolerances. This creates clear expectations for the project and ensures that everyone is working within the same agreed tolerances. This research project will develop an AR tool and interface as well as scanning in the field that enables easy information access, automatic data collection, and real-time data analysis by providing a dimensional verification of precast members inspection using 3D scanning. The focus will be prestressed I-beams that are cast in New Mexico, but it can be used for box girders, and other beams of interest in other transportation infrastructure.

This research aims to develop an innovative framework utilizing 3D point cloud data to provide insights into production variations. The objective is to empower factory operators with actionable information for enhancing quality control and optimizing processes with an interface both with the data and the product itself so it can be rectified in the field. To facilitate efficient decision-making by inspectors, a solution will be developed to expedite the identification of incorrect precast strands, rebar, stirrups, chairs, spacers, plates, and eventually the concrete dimensions of the casted members. AR Visualization technology will be employed to overlay holographic representations of precast concrete elements onto real-world objects. These holographic representations will be color-coded to indicate which errors are found during construction, and which members should not be utilized in construction, aiding inspectors in quickly identifying deviations from quality standards during fabrication and at the precast plant. They can display the design and error and keep it to compare later.
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