## 1 Introduction  
In Asian countries, where the population density is very high, high-rise residential buildings are common. The issue of floor impact sound tends to occur in most high-rise buildings. Many countries have set regulations for controlling floor impact sounds [1; 2; 3]. Various systems are needed to minimize the floor impact sounds and one of the most effective ways is to use a floating floor system. Many studies have shown that lightweight and heavyweight impact sounds can be reduced by using resilient materials. [PERSON] [4] developed an empirical and analytical model to evaluate the influence of impact sound transmission on low frequencies. Experimental tests on the response between the floor impact sound reduction and dynamic stiffness showed that as the dynamic stiffness decreases, the lightweight impact sound reduction tends to increase [5]. Measuring the apparent dynamic stiffness is an important procedure for evaluating the impact sound reduction. [PERSON] and [PERSON] [6] conducted an experimental study on the relation between the long-term deflection and dynamic stiffness considering various resilient materials. The creep behavior of polymer materials has been extensively studied in the chemical engineering field [7;  
study to evaluate the response between the deflection and dynamic stiffness under long-term loading, and proposed an equation to predict the dynamic stiffness. An artificial neural network (ANN) is a generalized mathematical model that resembles human neural biology. Many ANN techniques have been proposed to predict or classify various data. These techniques are useful to predict unknown output data, depending on various input values [11; 12]. Many studies have presented the feasibility of using ANNs in civil and other engineering areas with good results. [PERSON] et al. [13] researched an optimum mixture design of reactive powder concrete based on an ANN to establish the relationship between the design parameters and properties of reactive powder concrete. The proposed ANN model can be used for optimum design in different regions. [PERSON] et al. [14] reported computational intelligence techniques to estimate concrete properties using previously collected data. Linear and nonlinear regression were considered as statistical techniques. The backpropagation neural network and self-organization feature map were used as neural network techniques. [PERSON] et al. [15] presented an automatic mix design process to optimize asphalt mix constituents. A simple multilayer perceptron structure was developed using Marshall mix design data. [PERSON] et al. [16] investigated the effect of using marble slurry to partially replace cement by weight in concrete. A compressive strength prediction model was developed to verify the experimentally evaluated 28-day compressive strength. They reported that the proposed model would be useful in predicting the 28-day compressive strength for concrete incorporating marble slurry. [PERSON] and [PERSON] [17] proposed ANN and genetic expression programming models to predict the effect of nano silica and micro silica on cement mortar properties. The ANN model was applied to experimental results to verify the performance of the cement mortar. The ANN model was reported to be an alternative approach for evaluation of the silica effect in cement mortar. [PERSON] et al. [18] introduced the prediction of mechanical properties of engineered cementitious composite (ECC) using ANN technique. ANN models were developed for ECC reinforced with polyvinyl alcohol fiber, and experimental data from other researchers were used as a training set. The predicted results showed excellent consistency with the test results. Various ANN models were proposed to estimate the strength of concrete members. [PERSON] et al. [19] studied different input parameters on Interface shear Transfer capacity using artificial neural network models that produce consistent levels of accuracy.