# Autonomous Robotic System using Non-Destructive Evaluation methods for Bridge Deck Inspection

## TL;DR

This paper presents an autonomous robotic system equipped with multiple NDE sensors for real-time, cost-effective bridge deck inspection, utilizing machine learning for automated steel rebar detection and condition mapping.

## Contribution

The paper introduces a novel robotic platform integrating GPR, ER, and camera sensors with machine learning for automated bridge deck assessment.

## Key findings

- Successful real-time data collection with multiple NDE sensors.
- Automated steel rebar detection using machine learning.
- Generation of real-time condition maps of bridge decks.

## Abstract

Bridge condition assessment is important to maintain the quality of highway roads for public transport. Bridge deterioration with time is inevitable due to aging material, environmental wear and in some cases, inadequate maintenance. Non-destructive evaluation (NDE) methods are preferred for condition assessment for bridges, concrete buildings, and other civil structures. Some examples of NDE methods are ground penetrating radar (GPR), acoustic emission, and electrical resistivity (ER). NDE methods provide the ability to inspect a structure without causing any damage to the structure in the process. In addition, NDE methods typically cost less than other methods, since they do not require inspection sites to be evacuated prior to inspection, which greatly reduces the cost of safety related issues during the inspection process. In this paper, an autonomous robotic system equipped with three different NDE sensors is presented. The system employs GPR, ER, and a camera for data collection. The system is capable of performing real-time, cost-effective bridge deck inspection, and is comprised of a mechanical robot design and machine learning and pattern recognition methods for automated steel rebar picking to provide realtime condition maps of the corrosive deck environments.