Advanced prediction of tunnel boring machine performance based on big data

Computational Intelligence and Neuroscience

Yang

Cai

et al. 2021

A tunnel boring machine (TBM) is a type of heavy load equipment that is widely used in underground tunnel construction. The geological conditions in the tunneling process are decisive factors that directly affect the control of construction equipment. Because TBM tunneling always takes place underground, the acquisition of geological information has become a key issue in this field. This study focused on the internal relationships between the sequential nature of tunnel in situ data and the continuous interaction between equipment and geology and introduced the long short-term memory (LSTM) time series neural network method for processing in situ data. A method for predicting the geological parameters in advance based on TBM real-time state monitoring data is proposed. The proposed method was applied to a tunnel project in China, and the R2 of the prediction results for five geological parameters are all higher than 0.98. The performance of the LSTM was compared with that of an artificial neural network (ANN). The prediction accuracy of the LSTM was significantly higher compared with that of the ANN, and the generalization and robustness of LSTM are also better than those of ANN, which indicates that the proposed LSTM method could extract the sequence properties of the in situ data. The rule of equipment-geology interaction was reflected by increasing the memory structure of the model through the introduction of the “gate” concept, and the accurate prediction of geological parameters during tunneling was realized. Additionally, the influence of time window and distance of prediction on the model is discussed. The proposed method provides a new approach toward obtaining geological information during TBM construction and also provides a certain reference for the effective analysis of the in situ data with sequence properties.

“…All the airborne parameters and geological parameters were normalized for subsequent model training and testing. The calculation method is expressed as [ 18 ]

…”

Section: Case Studymentioning

confidence: 99%

, measured value y ={ y 1 , y 2 ,…, y n }, and the average value

, the above-mentioned evaluation indices are calculated by equations ( 5 )–( 7 ) [ 18 , 34 ]:

…”

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Geological Parameters during Tunneling by Time Series Analysis on In Situ Data

Computational Intelligence and Neuroscience

Yang

Cai

et al. 2021

“…LSTM neural network belongs to the dynamic RNNs category. These networks can model temporal sequences and time dependencies more reliably than conventional RNNs, which in general cannot handle long sequences (Vincent et al, 2010;Li et al, 2020). The LSTM neural network includes an input layer, one or several hidden layer(s), and an output layer.…”

Section: Long Short Term Memory Neural Network Modelmentioning

confidence: 99%

Algorithms for intelligent prediction of landslide displacements

J. Zhejiang Univ. Sci. A

Guo

Lacasse

et al. 2020

Self Cite

Landslides represent major threats to life and property in many areas of the world, such as the landslides in the Three Gorges Dam area in mainland China. To better prepare for landslides in this area, we explored how several machine learning algorithms (long short term memory (LSTM), random forest (RF), and gated recurrent unit (GRU)) might predict ground displacements under three types of landslides, each with distinct step-wise displacement characteristics. Landslide displacements are described with trend and periodic analyses and the predictions with each algorithm, validated with observations from the Three Gorges Dam reservoir over a one-year period. Results demonstrated that deep machine learning algorithms can be valuable tools for predicting landslide displacements, with the LSTM and GRU algorithms providing the most encouraging results. We recommend using these algorithms to predict landslide displacement of step-wise type landslides in the Three Gorges Dam area. Predictive models with similar reliability should gradually become a component when implementing early warning systems to reduce landslide risk.

“…Qu et al [17] established the concrete dam deformation prediction model based on LSTM. Li et al [18] developed an LSTM model to predict the TBM performances including the total thrust and the cutter-head torque in a real-time manner. In recent years, gated recurrent unit (GRU) has been successfully applied to spatial-temporal data and has been quite popular in many fields.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Diaphragm Wall Deflection Induced by Excavation Based on Machine Learning

Huajing

Mathematical Problems in Engineering

Guan

et al. 2021

For the concrete diaphragm wall (CDW) supported excavation, excessive wall deflection may pose a potential risk to adjacent structures and utilities in urban areas. Therefore, it is of significance to predict the CDW deformation with high accuracy and efficiency. This paper investigates three machine learning algorithms, namely, back-propagation neural network (BPNN), long short-term memory (LSTM), and gated recurrent unit (GRU), to predict the excavation-induced CDW deflection. A database of field measurement collected from an excavation project in Suzhou, China, is used to verify the proposed models. The results show that GRU exhibits lower prediction errors and better robustness in 10-fold cross validation than BPNN and executes less computational time than LSTM. Therefore, GRU is the most suitable algorithm for CDW deflection prediction considering both effectiveness and efficiency, and the predicted results can provide reasonable assistance for safety monitoring and early warning strategies conducted on the construction site.