School of Engineering Department of Civil and Environmental Engineering 83 A Cement-free Novel Concrete that Absorbs Greenhouse Gas CO2 to Heal itself and Improve its Mechanical Performance Supervisor: QIU, Jishen/ CIVL Student: YU, Wai Chung / CIVL Course: UROP1100, Summer Reactive magnesia cement (RMC) can sequestrate carbon dioxide (CO2) to gain comparable strength to the Portland cement-based concrete, however the formation of dense hydrated magnesium carbonates (HMCs) on outer layer hinders the CO2 diffusion inwardly. Incorporation of hollow natural fiber (HNF) in RMC can provide lumens for CO2 diffusion freely. However, the natural fibers are sensitive to moisture, hence the lumens are likely to be filled with moisture, this slows down the diffusion of CO2. This work aims at investigating the influence of internal free moisture content (FMC) on the CO2 absorption, strength development and microstructure of HNF-RMC. Pre-drying was employed on specimens to alter the FMC. After carbonation, the compressive test, carbonation test of the composites, e.g., phenolphthalein test, acid digestion test, CO2 absorption test and water absorption test are conducted. The results revealed FWC50% had the best performance among four FWCs. You will See a Hoopoe Supervisor: WANG, Yu-Hsing / CIVL Student: ZHANG, Hongyi / SENG Course: UROP1000, Summer Deep learning is one form of machine learning, which features the use of multiple layers of activation functions in the network. Among various deep learning architectures, convolutional neural network (CNN) is specially designed for computer vision as well as some other aspects and has achieved great success. In this project, You will see a Hoopoe, we set AI-enabled cameras to capture bird videos in HKUST on continuous progress and apply deep learning algorithms to identify and classify them. As a start, an image classifying network taking example from LeNet, one of the most classic CNN architectures, is constructed to enhance our understanding of deep learning.
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