Numerical Simulation Of Ultrasonic Detection In Pile And Time-frequency Analysis

Pile foundation is the key to the structure of the whole quality of structures and the integrity of piles is directly related to the bearing capacity and endurance, Therefore, the pile’s integrity can be used as an indicator to determine the quality of concrete infusion piles. The development of Ultrasonic detection method for pile foundation has gradually matured, criteria related improved next to nothing. Determining the pile’s integrity are mostly qualitative assessment based on a single parameter, and there are still difficulties for the quantitative evaluation of specific defects. Numerical Simulation can be used to demonstrate the ultrasonic propagation inside the concrete visually. It helps to guide pile testing work to observe and analyze the changes and features of wave field when ultrasonic counters lacunas, defect, crack, and fissure water in the concrete.Finite Difference Method is used to calculate the wave field of the ultrasonic in this paper, especially when it propagates in models including three common defects, that is, broken, necking and lacunas. We have simulated two measurement modes for each defect model and determine the position of the defect by analyzing the time of first wave arrival, characteristics of waveform, the way of amplitude attenuation in the mode of single point detection. We also obtain the features in two-dimensional plane of the transmitted wave when defects exist in the mode of sectorial scanning simulation, and analyze the reasons for the changes of wave field with the snapshots of wave fields.Curvelet transform is applied to the data processing of the ultrasonic transmission signal. Through the denoise processing in the Curvelet domain, valid information of the analog signal recognition strengthened, moreover, random interference information and dispersion information are suppressed. Random interference information from random aggregates in ultrasound simulation data has been eliminated through Curvelet transform, while dispersion information smoothing achieved good results.