TY - JOUR
T1 - Predicting material properties of concrete from ground-penetrating radar attributes
AU - Morris, Isabel M.
AU - Kumar, Vivek
AU - Glisic, Branko
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: I.M.M. was financially supported by NSF GRFP (No. 1148900). Any opinions, findings, conclusions, or recommendations expressed herein are those of the authors and do not necessarily reflect the views of the funding agency.
Publisher Copyright:
© The Author(s) 2020.
PY - 2021/9
Y1 - 2021/9
N2 - 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 (R2 = 0.83 from the instantaneous amplitude), density (R2 = 0.67 from the intensity attribute), and compressive strength (R2 = 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.
AB - 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 (R2 = 0.83 from the instantaneous amplitude), density (R2 = 0.67 from the intensity attribute), and compressive strength (R2 = 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.
KW - Nondestructive evaluation
KW - concrete
KW - data-driven (machine learning) prediction models
KW - ground-penetrating radar
KW - material properties
KW - porosity
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U2 - 10.1177/1475921720976999
DO - 10.1177/1475921720976999
M3 - Article
AN - SCOPUS:85097309539
SN - 1475-9217
VL - 20
SP - 2791
EP - 2812
JO - Structural Health Monitoring
JF - Structural Health Monitoring
IS - 5
ER -