In this study, the radiation attenuation coefficients (μ_m) of different proportions of additives were produced in dry mixture shotcrete both by experimental processes and by deep neural network based on DBN. Fly ash, silica fume, and polypropylene fiber were used as additives of dry mix shotcrete. In the first part of the two-part study, μ_m values were obtained from seven samples produced and a data set was created along with the input parameters of the experiment. In the second part, a model was developed for predicting the value of μ_m with input parameters using the DBN deep neural network Algorithm. Experimental data obtained in accordance with both applications and data generated by the Deep Belief Network (DBN) model were analyzed. As a result, the DBN model prediction μ_m values with an accuracy performance of 87.86%.

在这项研究中，通过实验过程和基于 DBN 的深度神经网络，在干混喷射混凝土中产生了不同比例添加剂的辐射衰减系数（_μm）。粉煤灰、硅粉和聚丙烯纤维用作干混喷射混凝土的添加剂。在两部分研究的第一部分中，从产生的七个样品中获得μ _m值，并创建了一个数据集以及实验的输入参数。在第二部分中，开发了一个模型来预测 μ _m的值输入参数使用 DBN 深度神经网络算法。分析了根据应用程序获得的实验数据和由深度信念网络 (DBN) 模型生成的数据。因此，DBN 模型预测 μ _m值的精度性能为 87.86%。