## 1 Introduction
The former propagation can first transform the input mode onto the hidden layer, and then pass the weighted sum of inputs to the output layer through an activation function, resulting in one output value. In this stage, the sigmoidal function (\(f\) (.)) is generally used, and the output can be calculated according to Equation (1). Immediately after that, the back-propagation propagation works by passing the error of network backwards from the output layer to the input layer, with the weights adjusted based on some learning strategies to reduce the network error. \[f_{j}=\frac{1}{1+\exp(-\sum w_{ji}o_{i}+b)} \tag{1}\]  
where \(w_{ji}\) is the connection weight from neuron \(i\) in the lower layer to neuron \(j\) in the upper layer and an initially small random value, \(o_{i}\) is the output of neuron \(i\), and \(b\) is the bias value. [PERSON] et al. [20] carried out a study on predicting the compressive strength of RAC at the curing time of 28 days using an ANN model. The authors collected a large amount of published data on the 28-day compressive strength of RAC, which came from previous research. The RA used was derived from different countries and sources. The data were used for the construction of the ANN model, and the predicted results of the ANN model were quite accurate. Moreover, the same author also established another ANN model for predicting the elastic modulus of RAC [21]. This model was built based on regression analysis and performed better predictions. Sensitivity analysis, an uncertainty analysis technique in relation to quantitative analysis, is a study to assess the sensitivity of the prediction results of the model to the change in the selected input variables [22]. It also determines the significance of these uncertain factors on the results [23; 24; 25]. Therefore, it is of interest to apply the sensitivity analysis to the constructed ANN model to further study the influence of each input variable on the output. By conducting a sensitivity analysis, [PERSON] et al. [26] determined the effect of the constituents of concrete mixes on the desired workability. To predict the compressive strength of NAC (natural aggregate concrete) using ANN, the concrete mix proportions used [27; 28; 29; 30] and the time of testing of the compressive strength [31; 32; 33] were generally selected as the input variables. For concrete made with RAs from different sources, the difference between the properties of different RAs should be taken into account. The aggregate characteristics, such as water absorption, specific gravity, and aggregate crush value are closely related to the properties of the old mortar attached, which can affect the properties of RAC by different levels. In theory, the more factors are taken into consideration, the more accurate the model is. However, in practice it is unsuitable to use all the affected factors due to the complicated calculation and the measuring error. Therefore, it is important to determine the significance of each RA characteristic and the optimal combination of factors, which aims to ensure the factors can be applicable to the majority of RAs from different sources. In other words, how to fully represent the aggregate properties in the ANN model is an important issue. The purpose of this study is to examine the relative importance of the different characteristics of RA in affecting RAC properties. Moreover, it also aims to determine which factor or combination of factors is most suitable for representing RA properties when used in ANN model for compressive strength and elastic modulus prediction. In this study, the following steps were used for this purpose.