Data-driven evaluation of building materials using Ground Penetrating Radar

Abstract

Ground Penetrating Radar is a widely used technology in the nondestructive evaluation and monitoring of structures during repair and maintenance phases. Within this domain, a critical focus lies in assessing layered structures such as building envelopes. Evaluating attributes like thickness and material type in layered structures traditionally demands labor-intensive manual efforts involving calculations of dielectric properties and extensive signal processing. This study addresses these challenges by leveraging machine learning data-driven models that harness the entire recorded waveform, presenting a potential breakthrough for expediting diagnostic monitoring within the construction industry. We evaluate both supervised and unsupervised machine learning models for classification and regression tasks predicting the properties of common building materials. Experimental GPR A-scans serve as input for these models, with a thorough evaluation of both instance-based and parametric modeling approaches. A detection accuracy of 100% is achieved for identifying outlier scans, while material classification accuracy is 85%. Layer thickness predictions also had a high accuracy, with a typical error of 5%. Additionally, we explore the impact of feature learning and other preprocessing strategies on model performance. Our findings demonstrate the suitability of standard data-driven models for a spectrum of supervised learning tasks for layered structure diagnostics. However, we emphasize the importance of careful attention to the distribution of training data and its relevance to intended use in the field.