Earthquake Damage Identification via Machine Learning and 3D Point Cloud Data

Contact - Christine Wittich
Department - Civil & Environmental Engineering
Students Needed:
• Undergraduate

Seeking an hourly student (undergraduate or graduate) research assistant to assist in quantifying the post-earthquake damage that occurred to a full-scale unreinforced masonry (URM) specimen using 3D point clouds. A laser scanner was used to generate high-resolution point clouds of the URM specimen before and after a strong earthquake. This project aims to use deep learning approaches to process the collected point clouds and autonomously quantify the damage that occurred post-earthquake. This project will provide a unique opportunity for students to get involved in research on post-earthquake reconnaissance and build their resumes in a paid position ($13/hr). Students from any major may apply, and no research or software experience is required. Work can be done remotely, or office space can be provided.  

The student will be expected to work 8-10 hours per week to deliver the following tasks: 1) Process 3D point clouds using computer programs, such as Matlab, FARO Scene, and CloudCompare. Training in software will be provided; 2) Split the processed point clouds into several datasets for the training of deep learning models; and, 3) Document and report weekly research progress.  
How to apply? Email the PI at with your resume and a brief statement of interest.