Impressed current cathodic protection (ICCP) is an effective and direct method for controlling the corrosion of reinforced concrete (RC) structures. However, few investigations related to ICCP in cracked RC structures have been reported. In this study, the effect of cracks in concrete cover on ICCP of chloride-contaminated RC structures was investigated through a numerical model including steel polarisation, electrode reactions, and ionic migration. In the developed numerical model, cracked concrete cover is assumed to consist of sound concrete and cracks, and cracks have their own ionic diffusion coefficients. The results indicate that the ICCP can maintain its ability to remove Cl⁻ if concrete cover does not completely crack. Once the complete cracking in concrete cover occurs, the Cl⁻ removal ability of ICCP would decrease or even disappear. Cracking does not cause any adverse effect on the pH improvement of ICCP. In this case, a stronger cathodic polarisation is recommended.

YouTube-like User Generated Content (UGC) sites are nowadays entertaining over a billion people. Resource provision is essential for these giant UGC sites as they allow users to request videos from a potentially unlimited selection in an asynchronous fashion. Still, the UGC sites are seeking to create new viewing patterns and social interactions that would engage and attract more users and complicate the already rigorous resource provision problem. In this paper, we seek to combine these two tasks by leveraging social features to provide the reference for resource provision. To this end, we conduct an extensive measurement and analysis of BiliBili, a YouTube-like UGC site with enhanced social features including user following, chat replay, and virtual money donation. Based on datasets that capture the complete view of BiliBili---containing over 2 million videos and over 28 million users---we characterize its video repository and user activities, we demonstrate the positive reinforcement between on-line social behavior and upload behavior, we propose graph models that reveal user relationships and high-level social structures, and we successfully apply our findings to build machine-learnt classifiers to identify videos that will need priority in resource provision.