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Reinforcement corrosion, due to the presence of chloride ions, is a major cause of the degradation of reinforced concrete structures. Nowadays, electrochemical rehabilitation (ER) is becoming a common technique for repairing reinforced structures. Due to the transmission properties of the micro-pores in concrete, chloride ions can be transferred to the outside of the concrete through the pores under the driven force of electric field. Compared with other conventional technologies, ER presents many advantages, such as high efficiency and little influence on the environment and surroundings. However, previous studies indicate that ER exhibits negative effect on the interfacial bonding properties of steel concrete. As the main influence factor for ER, varying current densities may consequently change the bond loss between steel and concrete. In addition, large current density significantly reduces interfacial bond. However, current studies lack relevant quantitative research results and fail to propose an effective method to solve the problem after electrochemical repair since aiming at electrochemical rehabilitation will most likely result in the bond deterioration of reinforced concrete. In this study, the bond-slip curves were obtained through central pull-out specimens after ER with various electrochemical parameters, and the relationship between the electrochemical parameters (current density and conduction time) and the bond behaviors were investigated. Finally, a degradation model of bond strength considering the influences of the two parameters mentioned was established. Results show that the bond strength decreases significantly with high current density and long conduction time. Using a current density of 5 A·m–2, reduction of the max bond force increased up to 22.6% and 56.9% under a conduction time of 15 and 28 d, respectively. The proposed model can be used to quantitatively characterize the reduction of bond strength after electrochemical rehabilitation. Good consistency of results was observed after comparing the evaluated results with that of the experiment.

Automated pavement distress detection benefits road maintenance and operation by providing the condition and location of various distress rapidly. Existing work generally relies on manual labor or specific algorithms trained by dedicated datasets, which hinders the efficiency and applicable scenarios of methods. Street view map provides interactive panoramas of a large scale of urban roadway network, and is updated in a recurrent manner by the provider. This paper proposed a deep learning method based on a pre-trained neural network architecture to identify and locate different distress in real-time. About 20,000 street view images were collected and labeled as the training dataset using the Baidu e-map. Eight types of distress are notated using Yolov3 deep learning architecture. The scale-invariant feature transform (SIFT) descriptors combined with GPS and bounding boxes were applied to judge the deterioration of the distress. A decision tree was designed to evaluate the change of the distress over some time. A typical road in Shanghai was selected to verify the effectiveness of the proposed model. The images of the road from 2015 to 2017 were collected from the street view map. The results showed that the mean average precision of the proposed algorithm is 88.37%, demonstrating the vast potential of applying this method to detect pavement distress. 43 distress were newly generated, and 49 previous distress were patched in the two years. The proposed method can assist the authorities to schedule the maintenance activities more effectively.