Edinburgh Napier University

  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.