Self-Healing Cementitious Composites (SHCC) with Ultrahigh Ductility for Pavement and Bridge Construction

Cracks and their formations in concrete structures have been a common and long-lived problem, mainly due to the intrinsic brittleness of the concrete. Concrete structures, such as rigid pavement and bridge decks, are prone to deformations and deteriorations caused by shrinkage, temperature fluctuation, and traffic load, which can affect their service life. Rehabilitation of concrete structures is expensive and challenging—not only from maintenance viewpoints but also because they cannot be used for services during maintenance. It is critical to significantly improve the ductility of concrete to overcome such issues and to enable better infrastructure quality. To this end, the self-healing cementitious composites (SHCC) investigated in this work could be a promising solution to the aforementioned problems. In this project, the team has designed a series of cementitious composites to investigate their mechanical performances and self-healing abilities. Firstly, various types of fibers were investigated for improving ductility of the designed SHCC. To enhance the self-healing of SHCC, we proposed and examined that the combination of the internal curing method with SHCC mixture design can further improve self-healing performance. Three types of internal curing agents were used on the SHCC mixture design, and their self-healing efficiency was evaluated by multiple destructive and non-destructive tests. Results indicated a significant improvement in the self-healing capacity with the incorporation of internal curing agents such as zeolite and lightweight aggregate. To control the fiber distribution and workability of the SHCC, the mix design was further adjusted by controlling rheology using different types of viscosity modifiers. The team also explored the feasibility of the incorporation of colloidal nano-silica into the mix design of SHCC. Results suggest that optimum amounts of nano-silica have positive influence on self-healing efficiency and mechanical properties of the SHCC. Better hydration was also achieved by adding the nano-silica. The bonding strength of the SHCC with conventional concrete was also improved. At last, a standardized mixing procedure for the large scale SHCC was drafted and proposed

Ranking-Incentivized Quality Preserving Content Modification

The Web is a canonical example of a competitive retrieval setting where many documents' authors consistently modify their documents to promote them in rankings. We present an automatic method for quality-preserving modification of document content -- i.e., maintaining content quality -- so that the document is ranked higher for a query by a non-disclosed ranking function whose rankings can be observed. The method replaces a passage in the document with some other passage. To select the two passages, we use a learning-to-rank approach with a bi-objective optimization criterion: rank promotion and content-quality maintenance. We used the approach as a bot in content-based ranking competitions. Analysis of the competitions demonstrates the merits of our approach with respect to human content modifications in terms of rank promotion, content-quality maintenance and relevance.Comment: 10 pages. 8 figures. 3 table

Evaluating the Self Healing Behavior of the Fiber-Reinforced Cementitious Composite Incorporating the Internal Curing Agents

The formation of the cracks in concrete materials can shorten the service life of the structure by exposing the steel rebar to the aggressive substances from the external environment. Self-healing concrete can eliminate the crack automatically, which has the potential to replace manual rehabilitation and repairing work. This thesis intends to develop a self-healing fiberreinforced cementitious composite by the use of internal curing agents, such as lightweight aggregate, zeolite and superabsorbent polymer (SAP). This study has evaluated the crack width control ability of three different types of fiber, polyvinyl alcohol fiber (PVA), Masterfiber Mac Matrix and Strux 90/40 fiber. Mechanical performance and flexural stress-strain behavior of the fiber-reinforced cementitious composite were tested and compared. In order to investigate the feasibility of using internal curing aggregate to enhance autogenous healing performance, two types of porous aggregates, zeolite and lightweight aggregate (LWA), were used as internal curing agents to provide water for the autogenous healing. The pore structure of the zeolite and lightweight aggregate was examined by the scanning electron microscopy (SEM). Two replacement ratios of sand with internal curing aggregates were designed and the healing efficiency was evaluated by the resonant frequency measurement and the optical microscopic observation. To further understand the influence of the internal curing on the designed material, water retention behavior of the bulk sample and the internal curing aggregates was evaluated. Moreover, to study the self-sealing effect of the superabsorbent polymer (SAP), the robustness of the SAP under various environmental conditions was first evaluated. The influence of the superplasticizer, hydration accelerator and fly ash on the absorption behavior of the SAP was investigated by the filtration test and void size analysis. Afterward, the self-sealing performance of the SAP in cement paste was evaluated by a water flow test

One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks

Unlearnable examples (ULEs) aim to protect data from unauthorized usage for training DNNs. Error-minimizing noise, which is injected to clean data, is one of the most successful methods for preventing DNNs from giving correct predictions on incoming new data. Nonetheless, under specific training strategies such as adversarial training, the unlearnability of error-minimizing noise will severely degrade. In addition, the transferability of error-minimizing noise is inherently limited by the mismatch between the generator model and the targeted learner model. In this paper, we investigate the mechanism of unlearnable examples and propose a novel model-free method, named \emph{One-Pixel Shortcut}, which only perturbs a single pixel of each image and makes the dataset unlearnable. Our method needs much less computational cost and obtains stronger transferability and thus can protect data from a wide range of different models. Based on this, we further introduce the first unlearnable dataset called CIFAR-10-S, which is indistinguishable from normal CIFAR-10 by human observers and can serve as a benchmark for different models or training strategies to evaluate their abilities to extract critical features from the disturbance of non-semantic representations. The original error-minimizing ULEs will lose efficiency under adversarial training, where the model can get over 83\% clean test accuracy. Meanwhile, even if adversarial training and strong data augmentation like RandAugment are applied together, the model trained on CIFAR-10-S cannot get over 50\% clean test accuracy

Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks

The score-based query attacks (SQAs) pose practical threats to deep neural networks by crafting adversarial perturbations within dozens of queries, only using the model's output scores. Nonetheless, we note that if the loss trend of the outputs is slightly perturbed, SQAs could be easily misled and thereby become much less effective. Following this idea, we propose a novel defense, namely Adversarial Attack on Attackers (AAA), to confound SQAs towards incorrect attack directions by slightly modifying the output logits. In this way, (1) SQAs are prevented regardless of the model's worst-case robustness; (2) the original model predictions are hardly changed, i.e., no degradation on clean accuracy; (3) the calibration of confidence scores can be improved simultaneously. Extensive experiments are provided to verify the above advantages. For example, by setting $\ell_\infty=8/255$ on CIFAR-10, our proposed AAA helps WideResNet-28 secure $80.59\%$ accuracy under Square attack ($2500$ queries), while the best prior defense (i.e., adversarial training) only attains $67.44\%$. Since AAA attacks SQA's general greedy strategy, such advantages of AAA over 8 defenses can be consistently observed on 8 CIFAR-10/ImageNet models under 6 SQAs, using different attack targets and bounds. Moreover, AAA calibrates better without hurting the accuracy. Our code would be released