Data-driven multi-step robust prediction of TBM attitude using a hybrid deep learning approach

Wang, Kunyu; Wu, Xianguo; Zhang, Limao; Song, Xieqing

doi:10.1016/j.aei.2022.101854

Cited by 29 publications

(3 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Xiao et al [18] proposed a shield attitude prediction method combining the Adaboost algorithm and the GRU model and the prediction effect was verified by five shield machines data. To enhance the robustness of model, Wang et al [19] fused the CNN model with the GRU model and validated the prediction performance on the multi-step prediction task.…”

Section: Introductionmentioning

confidence: 99%

Prediction of shield tunneling attitude: a hybrid deep learning approach considering feature temporal attention

Zeng,

Chen,

Zhang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Accurate prediction of shield attitude deviation is essential to ensure safe and efficient shield tunneling. However, previous studies have predominantly emphasized temporal correlation, which has limitations in engineering guidance and prediction accuracy. This research proposes a hybrid deep learning approach considering feature temporal attention (FTA-N-GRU) for shield attitude prediction. To elucidate the contributions of each parameter, the Integrated Gradients algorithm is leveraged for parameter sensitivity analysis. The results from the Bangladesh Karnaphuli River Tunnel Project indicate that: the proposed model outperforms other models in prediction accuracy. The integration of feature attention can adaptively allocate attention weights to input parameters, facilitating inexperienced operators in discerning crucial parameter variations and decision-making. By incorporating temporal attention, the model effectively explores the connection among different output time steps, improving overall prediction accuracy and reliability. Consequently, operators are empowered with timely information to proactively adjust operations before deviations occur, underscoring the significance of this approach in promoting safe and efficient shield tunneling practices.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of shield tunneling attitude: a hybrid deep learning approach considering feature temporal attention

Zeng,

Chen,

Zhang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…On the contrary, meta-heuristic algorithms are problem independent techniques that prove promising in areas where integer programming cannot cope with the sheer number of feasible solutions in a near optimal time frame [12]. These algorithms provide an acceptable solution within a reasonable amount of time with the help of random search capability [13]. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are among the pioneer meta-heuristic algorithms that have been consistently used for a variety of optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Parallel Enhanced Whale Optimization Algorithm for Independent Tasks Scheduling on Cloud Computing

Khan,

Aziz,

Osman

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Cloud computing has been imperative for computing systems worldwide since its inception. The researchers strive to leverage the efficient utilization of cloud resources to execute workload quickly in addition to providing better quality of service. Among several challenges on the cloud, task scheduling is one of the fundamental NP-hard problems. Meta-heuristic algorithms are extensively employed to solve task scheduling as a discrete optimization problem and therefore several meta-heuristic algorithms have been developed. However, they have their own strengths and weaknesses. Local optima, poor convergence, high execution time, and scalability are the predominant issues among meta-heuristic algorithms. In this paper, a parallel enhanced whale optimization algorithm is proposed to schedule independent tasks in the cloud with heterogeneous resources. The proposed algorithm improves solution diversity and avoids local optima using a modified encircling maneuver and an adaptive bubble net attacking mechanism. The parallelization technique keeps the execution time low despite its internal complexity. The proposed algorithm minimizes the makespan while improving resource utilization and throughput. It demonstrates the effectiveness of the proposed PEWOA against the best performing enhanced whale optimization algorithm (WOAmM) and Multi-core Random Matrix Particle Swarm Optimization (MRMPSO). The algorithm consistently produces better results with varying number of tasks on GoCJ dataset, indicating better scalability. The experiments are conducted in CloudSim utilizing a variety of GoCJ and HCSP instances. Various statistical tests are also conducted to evaluate the significance of the results.

show abstract

“…Data-driven machine learning has attracted attention in geotechnical engineering due to its powerful data processing ability [5][6][7][8][9][10]. Among various machine learning algorithms, recurrent neural networks (RNNs) are generally proven to be the most effective method for time-series problems like shield movement performance prediction [11][12][13][14][15][16]. For the shield attitude prediction, Zhou et al [17] proved the accuracy of an LSTM-based model in predicting the attitude and position of the shield machine.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Multi-Ring Shield Movement Performance Prediction and Control Method

Zeng,

Yu,

Chen

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Accurate control of the shield attitude can ensure precise tunnel excavation and minimize impact on the surrounding areas. However, neglecting the total thrust force may cause excessive disturbance to the strata, leading to collapse. This study proposes a Bayesian optimization-based temporal attention long short-term memory model (BOTA-LSTM) for multi-objective prediction and control of shield tunneling, including shield attitude and total thrust. The model can achieve multi-ring predictions of shield attitude and total thrust by allocating larger weights to significant moments through a temporal attention mechanism. The hyperparameters of the proposed model are automatically selected through Bayesian hyperparameter optimization, which can effectively address the issue of complex parameter selection and optimization difficulties in multi-ring, multi-objective tasks. Based on the predictive results of the optimal model, an intelligent control method that considers both shield attitude and total thrust is proposed. Compared to a method that solely predicts and corrects for the next ring, the proposed multi-ring correction method provides the opportunity for further adjustments, if the initial correction falls short of expectations. A shield tunneling project in Hangzhou is used to demonstrate the effectiveness of the proposed model. The results show that the BOTA-LSTM model outperforms the models without the integration of a temporal attention mechanism and Bayesian hyperparameter optimization. The proposed multi-ring intelligent correction method can adjust the shield attitude and total thrust to a reasonable range, providing references for practical engineering applications.

show abstract

Data-driven multi-step robust prediction of TBM attitude using a hybrid deep learning approach

Cited by 29 publications

References 40 publications

Prediction of shield tunneling attitude: a hybrid deep learning approach considering feature temporal attention

Prediction of shield tunneling attitude: a hybrid deep learning approach considering feature temporal attention

Parallel Enhanced Whale Optimization Algorithm for Independent Tasks Scheduling on Cloud Computing

An Intelligent Multi-Ring Shield Movement Performance Prediction and Control Method

Contact Info

Product

Resources

About