Comprehensive bridge condition intelligence repository covering TX, CA, PA, FL & NY — NBI/SNBI indices, Health Index analysis, failure case studies, rehabilitation methods, cost/duration data, and alternative route planning.

AI-powered bridge defect detection using YOLOv8 — trained for automated crack localisation in concrete infrastructure. Built in alignment with the EU STRUCTURE project on UAV-based inspection and digital twin development.

This system analyzes bridge repair method recommendation reports generated by AI agents and visualizes the decision-making pathway from damage → deterioration factors → repair methods as a Decision Tree. It aims to "make the thought process visible."

An AI-powered bridge health classification system that automatically categorizes bridge inspection reports into health levels using machine learning. The system leverages Explainable Boosting Machine (EBM) to achieve high accuracy while maintaining interpretability.

CNN-based bridge defect image classification using TensorFlow and MobileNetV2

An end-to-end pipeline for automated analysis of bridge structural damage (rebar exposure, cracks, corrosion) using **LLaVA (Large Language and Vision Assistant)**. The system generates expert-level damage descriptions from images and produces structured prioritization scores for repair planning.

Non-code case study of a bridge defect detection and quantification workflow: segmentation, stereo depth, geometry, data flow and system boundaries.