Sensitivity and regression analysis of acoustic parameters for determining physical properties of frozen fine sand with ultrasonic test

Determining the development of artificial frozen walls by present methods is challenging where substantial seepage occurs because fixed monitoring points only indicate physical properties in small areas. Here we use ultrasonic acoustic methods to determine the physical properties between two freezing pipes during freezing. Sensitivity analysis indicates that wave velocity is sensitive to physical properties, and the sensitivity rank is water content > temperature > density. The attenuation coefficient has a low sensitivity to physical parameters, whereas dominant frequency is sensitive to temperature and water content but insensitive to density. Wave velocity increases with temperature and density in a quadratic relationship, and with water content in a linear relationship. Dominant frequency increases with temperature and water content in a quadratic relationship. A multiple regression model of wave velocity and dominant frequency established by stepwise regression can be used to predict the relationship between wave velocity and temperature of frozen fine sand in different areas where the soil properties are similar to those reported here. Wave velocity and dominant frequency measured in the laboratory can be used to predict the relationship between acoustic parameters and temperature in field conditions after curve move based on the first data point from field measurements. The procedure of curve moving involves calculating the difference in value of the first data point between laboratory and field measurements at the same temperature level, and then moving the predicted curve of the laboratory test upward or downward according to the difference. Supplementary material: Experimental datasets are available at

Automated summary

Determining the physical properties between two freezing pipes during freezing using ultrasonic acoustic methods shows that wave velocity is sensitive to physical properties and dominant frequency is sensitive to temperature and water content. The established multiple regression model can predict the relationship between wave velocity and temperature.