Self‐compacting concrete (SCC) is a type of concrete that can consolidate itself without external compaction. High‐strength SCC (HS‐SCC) is becoming more popular having a very broad range of application in Civil Engineering, such as piles, columns of tall buildings and piers for long‐span bridges. However, HS‐SCC is very brittle that limits its application above. To this, present study aims at mitigating the brittleness of HS‐SCC having a fixed water/binder ratio of 0.30 and binder content of 500 kg/m3 by blending cement with condensed silica fume (CSF). The percentage of CSF that replaced cement was 0–15% by weight. Apart from measuring the brittleness, mechanical property such as splitting tensile and compressive strength, as well as fresh property, such as slump flow, V‐funnel time, and L‐box passing ability was also obtained. The experimental results indicated that 10% replacement of cement by CSF could effectively decrease the brittleness of HS‐SCC and simultaneously increase the 28‐day compressive strength. On the other hand, the slump flow of concrete decreased as the content of CSF increased, but nonetheless was able to maintain at above 600 mm, which is a commonly accepted criteria for SCC. Lastly, scanning electron microscope figures showed that the microstructure of concrete and hydration morphology were enhanced by CSF particle.
Aiming at the problem that the tracking precision of small mobile robot decreases or even fails due to the change of illumination or the fast motion of human body, a kernel correlation filter guided by motion model (MMKCF) is proposed. By building a feet motion model to predict the position of the feet in video tracking, the algorithm obtains the target detection area of the kernel correlation filter tracking algorithm, which effectively solves the drift of the tracking box. Based on four groups of human walking video, target tracking experiments are carried out, and tracking performance of five algorithms such as MMKCF and KCF are compared. The experimental results show that the average tracking precision of MMKCF algorithm is about 0.77 when the illumination changes and the target moves rapidly, which is much higher than the other four tracking algorithms. Finally, the MMKCF algorithm is applied to the target tracking of TurtleBot robot under the robot operating system (ROS), and the foot tracking of human body during fast motion is successfully completed, which proves that the proposed algorithm has strong robustness and real-time performance.
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