轻质混凝土承压监测方法设计与仿真

Design and Simulation of Pressure Monitoring Method for Lightweight Concrete

利用当前已有方法对轻质混凝土承压时,忽略了对轻质混凝土承压强度影响因素的全面分析,导致监测效果低、与实际质量损失率相差大。现提出基于卷积神经网络的轻质混凝土承压监测方法。分析轻质混凝土的承压强度影响因素,采集不同龄期下的轻质混凝土承压强度数据。构建卷积神经网络模型对轻质混凝土采集数据,并对其训练,最终将训练结果输出,其训练结果就是轻质混凝土承压监测结果。依据监测结果即可判定轻质混凝土承压状态,实现轻质混凝土承压监测。实验结果表明,通过对所提方法的监测效果以及对比监测结果与实际质量损失率的测试结果,证明上述方法的监测准确率更高,应用效果更好。

The traditional bearing strength analysis method of lightweight concrete has many defects, such as poor monitoring effect, quality and actual loss rate, being caused by the one-sided analysis of the influencing factors of bearing strength of lightweight concrete. In this regard, the pressure monitoring method of lightweight concrete based on convolution neural network was studied. The influencing factors of bearing strength of lightweight concrete were analyzed, and the bearing strength data of lightweight concrete in different age periods were collected. The convolution neural network model was established to collect the data of lightweight concrete at different ages. Meanwhile, the data were trained and the results were output. The results were the monitoring results of lightweight concrete bearing pressure. Based on the monitoring results, the pressure bearing state of lightweight concrete was determined, thus achieving the pressure bearing monitoring of lightweight concrete. The experimental results show that the monitoring accuracy and application effect of this method are superior to those of traditional methods.