Reports & Technical Demos

Overview

This page presents selected technical reports demonstrating our AI-based infrastructure assessment workflow.

The examples cover the full lifecycle of crack intelligence applications: baseline condition assessment, damage evolution monitoring, and post-repair verification.

Each report illustrates how image-based AI analysis is translated into engineering-relevant severity scoring, prioritization, and maintenance decision support, enabling transparent and repeatable infrastructure management.

Technical Reports & Demonstrations

Below are selected technical reports and demonstration studies developed as part of applied research and pilot engineering projects.

The reports can be previewed directly and illustrate our methodology, analytical depth, and reporting standards. 

Baseline AI Crack Intelligence Report

This demonstration report presents a baseline AI-driven crack intelligence assessment applied to a mixed set of building surface and concrete pavement images.

The study establishes an initial crack inventory, assigns severity scores (0–100) and priority classes (Low to Critical), and provides engineering-oriented repair decision support together with a structured monitoring plan.

The workflow combines image-based crack feature extraction with quantitative proxies for crack extent and openness to demonstrate how AI outputs can be translated into actionable maintenance decisions, including:

• Crack classification by type and pattern
  
  

• Severity scoring and priority ranking
  
  

• Recommended intervention timeframes
  
  

• Repair strategy guidance
  
  

• Monitoring frequency and KPIs for evolution tracking
  
  

This report is intended as a baseline demonstration of the reporting format and decision logic. In full operational deployments, severity metrics and classifications are derived directly from trained CNN model outputs and calibrated with physical scale and asset context.

Report type: Baseline assessment · Application: Concrete & building surfaces · Method: AI-based image analysis · Purpose: Prioritization & monitoring 

This report is provided for demonstration purposes only. Final engineering decisions require site context, physical measurements, and professional verification. 

AI Crack Evolution Monitoring Report (Cycle Comparison) 

This report demonstrates an AI-based crack evolution monitoring workflow, comparing two inspection cycles to quantify damage progression, stability, or improvement over time.

Severity scores and priority classes are updated consistently between cycles, enabling objective tracking of deterioration, post-repair improvement, and priority migration.

The report supports maintenance planning over time, helping infrastructure owners decide when to intervene, escalate, or continue monitoring based on measured trends rather than isolated inspections.

AI Crack Repair Verification Report (Before–After)

This report verifies repair effectiveness using an AI-driven before–after comparison of crack evidence.

The workflow confirms whether detected defects were successfully resolved or stabilized after intervention, quantifies severity reduction, and defines the next monitoring actions.

This type of report provides auditable documentation for stakeholders and supports quality control, acceptance decisions, and post-repair monitoring strategies.

Decision Support Tools

In addition to full technical reports, standardized decision-support tools are used to support consistent and transparent maintenance decisions.