Biograph: Dr. Hai Liu received the B.E. and M.E. degrees in civil engineering from Tongji University, Shanghai, China, in 2007 and 2009, respectively, and the PhD degree in Environmental Studies from Tohoku University, Sendai, Japan, in 2013. From April 2013 to March 2014, he was with the Center for Northeast Asian Studies, Tohoku University, as a Research Fellow. From July 2014 to July 2017, he was an Assistant Professor with the Institute of Electromagnetics and Acoustics, Xiamen University, Xiamen, China. He is currently an Associate Professor with the School of Civil Engineering, Guangzhou University, Guangzhou, China. Dr. Liu is an IEEE senior member, and his current research interests include development of ground-penetrating radar systems and algorithms for a wide variety of applications, such as nondestructive testing in civil engineering, environmental monitoring, archeological investigation, and lunar exploration.
Dr. Liu received the Young Researcher Award of the 14th International Conference on Ground Penetrating Radar in 2012 and the Excellent Paper Award of the IET International Radar Conference in 2013.
Title: Automatic Detection and Characterization of Rebar in Concrete using A Dual Sensor of GPR and EMI
Abstract: Precise characterization of reinforcing bars (rebars) in a concrete structure is of signiﬁcant importance for construction quality control and post-disaster safety evaluation. This paper integrates ground-penetrating radar (GPR) and electromagnetic induction (EMI) methods for simultaneous estimation of rebar diameter and cover thickness. A prototype of GPR-EMI dual sensor is developed, and a calibration experiment is conducted to collect a standard EMI dataset corresponding to various rebar diameters and cover thicknesses. The handheld testing cart can synchronously collect both GPR and EMI data when moving on the concrete surface, from which a data processing algorithm is proposed to simultaneously estimate the rebar diameter and cover thickness. Firstly, the hyperbolic reﬂection from the rebar in the preprocessed GPR proﬁle is automatically detected by a pre-trained deep learning algorithm, the rebar position is determined from the apex of the hyperbola. Then, the rebar diameter and cover thickness are simultaneously estimated from the minimum mean square error between the measured and calibrated EMI data under the constraint of the GPR-estimated cover thickness. Both laboratory and field experiment were conducted to verify the accuracy. It is concluded that the developed GPR-EMI dual sensor and the proposed algorithm can estimate the rebar diameter and cover thickness accurately.