Geopolymer Concrete Crack Prediction System Using Machine Learning and Image Processing

Abstract

Geopolymer concrete is a sustainable alternative to conventional cement concrete due to re-duced environmental impact and improved durability. However, crack formation remains a major challenge affecting structural safety and long-term service life.

This paper presents a machine learning based crack detection and prediction system using im-age processing techniques. Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Random Forest algorithms are used for crack classification and severity analysis.

Experimental results achieved an accuracy of 94.5%, proving the effectiveness and reliability of the proposed intelligent monitoring system.