# Spatiotemporal Monitoring and Evaluation Method for Sand-Filling of Immersed Tube Tunnel Foundation
###### Abstract
[PERSON] [17] proposed a method to evaluate soil moisture and shear deformation by compression wave velocities in a shallow slope surface layer. [PERSON] et al. [18] and [PERSON] et al. [19] used guided waves for damage detection of structures. [PERSON] et al. [20] used guided ultrasonic waves for nondestructive monitoring of stress levels in prestressed steel strands. Machine-learning methods have the advantage of constructing high latitude and nonlinear mapping relationships between different factors. The application of machine-learning algorithms in the field of civil engineering began in the 1990s to identify structural damage [21]. With the rapid development of computing technology, machine learning has been gradually combined with civil engineering in recent years. [PERSON] et al. [22] covered the anomaly data diagnosis approach using a deep-learning algorithm, crack identification approaches using computer vision techniques, and condition assessment approaches for bridges using machine-learning algorithms. [PERSON] [23] used decision tree (DT), gradient boosted trees (GBTs), and hybrid DT-GBT models to estimate soil temperature. [PERSON] [24] developed a machine-learning approach based on the support vector machine method to prevent misdirection of the event interpretation of what is happening in the material. As a mature machine-learning method, neural networks have a strong self-learning ability and high-speed ability to find the optimal solution [25; 26; 27; 28]. Neural networks have good robustness when solving certain civil engineering problems. Compared with other machine learning methods such as extreme learning and the support vector machine, the application of the neural network is more extensive and its training process is simpler [29]. Especially, the neural network with multiple hidden layers has excellent capabilities for learning features, compared to other machine learning methods [30]. [PERSON] et al. [31] found that a system based on neural networks remained sensitive and robust even when using raw signals with a relatively low sampling rate in a fairly narrow time window. [PERSON] [32] found that U-Net, a fully convolutional neural network, is a powerful and robust tool to map erosion sites in a predictive manner utilizing large amounts of new aerial imagery. Neural networks also have good applications for classification problems. [PERSON] et al. [33] proposed the use of artificial neural networks for the classification and clustering of steel according to strength characteristics, achieving a good classification effect. At the same time, some scholars have successfully combined elastic waves with neural networks to solve nonlinear problems. [PERSON] et al. [34] investigated the use of elastic waves and artificial neural networks for the purpose of the force of identification, conducted experiments, and achieved good recognition results. [PERSON] [35] conducted research on elastic wave properties and machine learning for the distribution of petrophysical properties in reservoir modelling. In summary, in the field of civil engineering, neural networks have been extensively applied to classification and recognition problems, and have also been well combined with elastic waves. To realize the elastic wave monitoring of sand-filling in the immersed tube tunnel, a connection needs to be established between the elastic wave monitoring data and the state of the sand deposit. Therefore, the neural network can be adopted combined with the elastic wave, in the monitoring and evaluation of immersed tube tunnel foundation sand-filling. To evaluate the sand-filling state of the immersed tube tunnel foundation in real-time, a spatiotemporal monitoring and evaluation method is proposed. The elastic wave data under different sand-filling conditions were collected and analyzed in the time domain, frequency domain, and time-frequency domain, and the feature parameters of the elastic wave were extracted.