| As China's urbanization process accelerated,the total amount of construction waste generated each year,which will reach billions of tons.These construction waste recycling use,will reduce the exploitation of resources due to damage caused by sand and gravel aggregate,but also solve the problem of construction waste,reduce environmental pollution,and reduce production costs and improve the economic and social benefits.Therefore,recycled concrete technology research and utilization is an effective way to conserve resources and protect the ecology,its performance is not as a relatively mature theory and empirical formula of ordinary concrete,in addition to considering mixing ratio factors,the need to consider replace the basic physical properties of recycled aggregate and recycled aggregate rate and other factors.In this paper,BP neural network model to achieve the performance of recycled concrete,combined with our group test and scholars study the data,analyze the main factors of the basic performance of recycled concrete,recycled coarse aggregate selected apparent density,water absorption,crushing index,cement,sand ratio of recycled coarse aggregate replacement rate,water-cement ratio seven major factors as input parameters BP network to slump,28 d compressive strength,elastic modulus of the output parameter,establish BP neural network model to implement linear mapping between input and output.Studies have shown that: BP network model established in this paper,based on the reproduction of recycled aggregate concrete mix and performance data can be more accurately predict the recycled concrete slump,28 d compressive strength and modulus of elasticity.Waste and promote successful application of this model can be avoided with recycled concrete pre-trial and post-test to bring human and material resources,reduce production costs,speed up the construction progress,improve project quality,conserve natural resources,has great research and application prospects. |
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