Abstract
Monitoring and prediction of ground settlement during tunnel construction are of great significance to ensure the safe and reliable operation of urban tunnel systems. Data-driven techniques combining artificial intelligence (AI) and sensor networks are popular methods in the field, which have several advantages, including high prediction accuracy, efficiency, and low cost. Deep learning, as one of the advanced techniques in AI, is demanded for the tunnel settlement forecasting problem. However, deep neural networks often require a large amount of training data. Due to the tunnel construction, the available training data samples are limited, and the data are univariate (i.e., containing only the settlement data). In response to the above problems, this research proposes a deep learning model that only requires limited number of training data for short-period prediction of the tunnel surface settlement. In the proposed complete ensemble empirical mode decomposition with adaptive noise long short term memory (CEEMDAN-LSTM model), single-dimensional data is divided into multidimensional data by CEEMDAN through the complete ensemble empirical mode decomposition. Each component is then predicted by a LSTM neural network and superimposed for obtaining the final prediction result. Experimental results show that, compared with existing machine learning techniques and algorithms, this deep learning method has higher prediction accuracy and acceptable computational efficiency. In the case of small samples, this method can significantly improve the accuracy of time series forecasting.
Highlights
Monitoring and prediction of ground settlement during tunnel construction are of great significance to ensure the safe and reliable operation of urban tunnel systems
Many scientists have conducted a lot of research on the settlement prediction of tunnels with sensor data. e research methods can be roughly divided into the theoretical calculation empirical method and sensor-based data measurement and analysis method. e theoretical calculation empirical method is represented by the Peck empirical formula method, including the numerical analysis method, numerical simulation method, semitheoretical analysis method, and random theoretical model [5, 6]
One measures the surface settlement of the tunnel constructed by Ningbo Metro Line 3 in Ningbo city, China. e other dataset measures the surface settlement of the subway constructed in Zhuhai, China. e original data was available at https:// downloads.hindawi.com/journals/mpe/2019/7057612.f1.zip, which was uploaded by Hu [15]
Summary
Monitoring and prediction of ground settlement during tunnel construction are of great significance to ensure the safe and reliable operation of urban tunnel systems. In response to the above problems, this research proposes a deep learning model that only requires limited number of training data for short-period prediction of the tunnel surface settlement. E long short-term memory (LSTM) network extends the traditional recurrent neural networks (RNN) and is widely adopted for the sensor-based time series data forecasting problems [9]. E LSTM network has many successful application cases in the fields of pattern recognition, machine translation, audio and video processing analysis, traffic flow prediction, medicine, etc., which can be adopted for the tunnel surface settlement problem [10,11,12,13,14]. In the research of Hu et al, through a series of comparative studies, three machine learning techniques were selected, namely, backpropagation neural network, extreme learning machine, and support vector regression machine, as the best way of short-term predictions of tunnel settlement under various conditions [15]. Based on this research and using the same dataset, this research shows that there is a complex nonlinear relationship between tunnel settlement and many random uncertain factors; it is difficult to predict tunnel settlement using a single machine learning technique
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