Application of Machine Learning Approaches to Predict Calcium-Aluminate Cement

Abstract

Calcium-aluminate cement (CAC), also known as high-alumina cement, was commonly used in structural elements from buildings constructed during the period of 1950–1970 in Catalonia (Spain). The most suitable techniques to confirm the presence of hydrated CAC in a concrete sample are chemical analysis and X-ray diffraction (XRD), complemented by differential-thermal and gravimetric analysis (DTA/TG). Up to date, other rapid-detection techniques include the oxine test or colour test. In this line, this study has developed a machine-learning method to improve the rapid recognition process of this binder type. Based on two previ-ously labelled datasets, several avenues of resolution have been explored such as the use of data correlation statistics, and the automated preprocessing of forensic photographs. Later on, the algorithm uses binary classification by means of different supervised machine learning algorithms and, finally, through the training of various neural networks powered by transfer learning, the model successfully identifies the cement type. It has been possible to demonstrate the promising future of artificial intelligence (AI) in Architecture, Engineering and Construction (AEC), improving the ability to identify the CAC of a technician specializing in building diagnosis, obtaining a prediction accuracy of 96.5%.