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April 10, 2026Open Access

Non Destructive Masonry Asessment using AI-Driven Image processing - 1Pager

Key Points

To develop an AI-based method for assessing the structural condition of masonry elements non-destructively.
Develop an AI-driven methodology for structural assessment
Utilize computer vision techniques for image analysis
Implement machine learning and deep learning models for crack detection
Validate the methodology through practical assessments
Successfully detected and classified cracks in masonry surfaces
Improved accuracy and reduced manual effort in evaluations
Enabled scalable inspection workflows for informed decision-making

Abstract

This work develops and validates an AI-driven, non-destructive methodology for assessing the structural condition of masonry elements. It combines computer vision techniques with machine learning and deep learning models to automatically detect, classify, and segment cracks in masonry surfaces. The approach aims to support scalable, data-driven inspection workflows that improve accuracy, reduce manual effort, and enable informed decision-making in circular construction contexts.

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Technische Universität Berlin

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Non Destructive Masonry Asessment using AI-Driven Image processing - 1Pager

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