Evan McLaughlin

Papers

Total Citations

147

H-Index

About

Dr. Evan McLaughlin is a leading researcher at the intersection of civil infrastructure and robotics, specializing in automated structural health monitoring. His work focuses on revolutionizing bridge inspection by integrating mobile ground robotics with advanced deep learning techniques. McLaughlin’s major contributions include developing end-to-end automated processes for detecting and quantifying critical area defects like spalls and delaminations in reinforced concrete bridges. His seminal 2020 paper, "Automated Defect Quantification in Concrete Bridges Using Robotics and Deep Learning," has garnered 82 citations, underscoring its impact on transforming traditional, labor-intensive inspection methods. His earlier foundational work, "Automated Bridge Inspection Using Mobile Ground Robotics" (55 citations), established a framework for using robotic platforms to enhance accessibility and repeatability in inspections. McLaughlin’s research addresses a critical need for safer, faster, and more reliable infrastructure assessment, directly influencing how engineers maintain aging bridge networks. His notable achievement lies in seamlessly combining robotics and AI to create practical, deployable solutions that reduce human risk and improve defect detection accuracy, marking him as a key innovator in smart infrastructure and robotic inspection technologies.