Abstract
It is of great significance to study the positive characteristics of concrete bearing cracks, fire and other adverse environment for the safety of human life and property and the protection of environmental resources. However, there are still some challenges in traditional concrete composition evaluation methods. On the one hand, the traditional method needs a lot of experimental work, which is time-consuming and laborious; On the other hand, the cost of new technology is high, and its applicability needs further study. Therefore, this paper proposes an improved lightweight model based on fully connected neural network (FCNN) to discover the relationship between the performance of different concrete mixtures and the visual (image) performance of the final synthesis process, so as to realize the prediction of concrete composition. The model is built in a distributed environment, and it can achieve lightweight and convenient effect through remote call learning model. The experimental results show that the method greatly improves the accuracy of concrete composition prediction.
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Acknowledgements
This work has received funding from the Key Laboratory Foundation of National Defence Technology under Grant 61424010208, National Natural Science Foundation of China (No. 41911530242 and 41975142), 5150 Spring Specialists (05492018012 and 05762018039), Major Program of the National Social Science Fund of China (Grant No. 17ZDA092), 333 High-Level Talent Cultivation Project of Jiangsu Province (BRA2018332), Royal Society of Edinburgh, UK and China Natural Science Foundation Council (RSE Reference: 62967_Liu_2018_2) under their Joint International Projects funding scheme and basic Research Programs (Natural Science Foundation) of Jiangsu Province (BK20191398 and BK20180794).
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Lu, H. et al. (2022). A Lightweight FCNN-Driven Approach to Concrete Composition Extraction in a Distributed Environment. In: Khosravi, M.R., He, Q., Dai, H. (eds) Cloud Computing. CloudComp 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 430. Springer, Cham. https://doi.org/10.1007/978-3-030-99191-3_4
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DOI: https://doi.org/10.1007/978-3-030-99191-3_4
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