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In this paper, eight variables of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate and flow are used as network input and slump is used as network output to construct a back-propagation (BP) neural network. On this basis, the learning rate, momentum factor, number of hidden nodes and number of iterations are used as hyperparameters to construct 2-layer and 3-layer neural networks respectively. Finally, the response surface method (RSM) is used to optimize the parameters of the network model obtained previously. The results show that the network model with parameters obtained by the response surface method (RSM) has a better coefficient of determination for the test set than the model before optimization, and the optimized model has higher prediction accuracy. At the same time, the model is used to evaluate the influencing factors of each variable on slump. The results show that flow, water, coarse aggregate and fine aggregate are the four main influencing factors, and the maximum influencing factor of flow is 0.875. This also provides a new idea for quickly and effectively adjusting the parameters of the neural network model to improve the prediction accuracy of concrete slump.

Traditional vibrating equipment and technology combined with manual experience to judge the quality of concrete vibrating is highly subjective and poorly standardized. Due to under-vibration, over-vibration and leakage vibration, the concrete has defects such as holes, segregation and cracks, which lead to the pouring quality not meeting the design requirements. The research progress of vibrating technology is introduced from four aspects: parameters affecting vibrating quality, evaluation method of vibrating quality, key technologies and development trends of vibrating. The research shows that intelligent vibrating has a significant role in promoting the development of civil engineering construction, and also lays a good foundation for the development of related engineering equipment and vibrating machinery automation.

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