Detecting the corrosive environment of reinforced concrete (RC) bridge decks is of critical importance for evaluating the reliability and safety of bridge structures. However, accurately and automatically detecting a corrosive environment with traditional methods is challenging. This paper proposes a method for the automatic corrosive environment detection of bridge decks from ground-penetrating radar (GPR) data based on the single-shot multibox detector (SSD) model. This method can be divided into three steps: data preprocessing, automatic rebar picking, and corrosive environment mapping. First, the GPR data are preprocessed to enhance the contrast of the hyperbolic feature in GPR B-scans. Then the rebars in the B-scan images are automatically picked up by the trained SSD model. Finally, the corrosive environment contour map of the bridge deck is generated with the rebar reflection amplitudes after depth correction. The SSD model was trained with 10,316 B-scan images and tested with 2,578 images. The $300\times 300\text{-}\text{pixel}$ B-scan image typically included three to five hyperbolas. A case study with GPR data from a tested bridge was employed to validate the feasibility of the proposed method. The results show that the accuracy of the automatic corrosive environment detection method can reach 98% and is considerably higher than that of commercial software methods.

Determination of the depth of surface-opening (visible) cracks is of critical importance for evaluating the structural safety of concrete elements. Accurately determining the crack depth with traditional nondestructive testing methods, however, is challenging. This study developed a new method using ultrasonic shear-horizontal (SH) waves to detect the depth of surface-opening cracks in concrete. The method is based on the diffraction of ultrasonic SH waves at the bottom edge of a crack. An off-the-shelf ultrasonic imaging device was used with dry-point contact transducers having a set of transmitters and a set of receivers performing in a pitch-catch configuration. SH waves are superior to other waves in data interpretation because the SH waves have a higher signal-to-noise ratio as the only wave type generated by their transmitter and without mode conversion after diffraction. Thus, accurately identifying the travel time of diffracted SH waves in the time domain is very achievable. The crack depth can be calculated with the two-way travel time of SH waves, the spacing between the transducers, and the SH-wave speed. The method was validated with a-finite element model and experimental data from two case studies. Results indicated that this method can significantly improve the accuracy of determinations of the depth of surface-open cracks compared with the ultrasonic longitudinal wave method.

The Magnetic Flux Leakage (MFL) method can be nondestructively used to disclose the location and extent of corrosion or fracture in prestressed strands in concrete structures. In this study, parameters with the greatest effect on the performance of the MFL system are investigated using numerical simulations. The MFL system under study is based on two permanent magnets to magnetize embedded strands and Hall-effect sensors to detect normal magnetic flux leakage. The system is assessed using magnetostatic and transient numerical analysis to effectively simulate the MFL system. Results have been verified by laboratory and field experiments. Both normal and axial Hall-effect sensors are modeled in simulations to better identify magnetic signals at the corrosion zone. The sensor lift-off and the magnetic field masking by lateral reinforcements on nearby pitting corrosion are addressed as two main drawbacks of using MFL systems to detect corrosion in prestressed concrete structures. To provide more details about the flux leakage interference between the pitting corrosion and lateral reinforcements in prestressed concrete structures, linear and oriented magnets/sensor arrays are proposed and analyzed numerically.