Predicting material properties of concrete from ground-penetrating radar attributes

We present here a laboratory-based experimental protocol that seeks to establish and characterize the relationship between ground-penetrating radar attributes and the mechanical properties (density, porosity, and compressive strength) of typical industry concrete mixes. The experimental data consist of ground-penetrating radar attributes from 900 MHz radargrams that correspond to simultaneously measured physical properties of Portland cement concrete, alkali-activated concrete, and cement mortar. Appropriate regression models are trained and tested on this data set to predict each physical property from ground-penetrating radar attributes. From a small selection of individual attributes, including total phase and intensity, trained random forest regression models predict porosity (R-2 = 0.83 from the instantaneous amplitude), density (R-2 = 0.67 from the intensity attribute), and compressive strength (R-2 = 0.51 from instantaneous amplitude). These novel relationships between physical properties and ground-penetrating radar attributes indicate that material properties could be predicted from the attributes of ordinary ground-penetrating radar scans of concrete.

Automated summary

A laboratory-based experimental protocol establishes the relationship between ground-penetrating radar attributes and mechanical properties of concrete mixes, predicting physical properties from radar attributes using regression models. The novel relationships indicate that material properties could be predicted from ordinary ground-penetrating radar scans of concrete, offering a new approach for predicting concrete properties in practical engineering applications.