Use of deep learning, denoising technic and cross-correlation analysis for the prediction of the shield machine slurry pressure in mixed ground conditions

Wang, Qiang; Xie, Xuansong; Shahrour, Isam; Huang, Yu

doi:10.1016/j.autcon.2021.103741

Cited by 17 publications

(2 citation statements)

References 41 publications

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“…Combining denoising technology, Wang et al used LSTM and two regression models, a temporal convolutional network and random forest, to predict the slurry pressure of the slurry pressure balance shield machine. The model's root mean square error (RMSE) and coefficient of determination (R 2 ) were 1.83 kPa and 0.9974, respectively, indicating its high accuracy [86]. The effective management of resources and cash flow during the construction phase can be a determining factor in the success or failure of a project.…”

Section: Intelligent Algorithms In the Whole Life Cycle Of Constructi...mentioning

confidence: 99%

Recent Research Progress in Intelligent Construction: A Comparison between China and Developed Countries

et al. 2023

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Intelligent construction (IC) has emerged as a new approach to transforming the architecture, engineering, and construction (AEC) industry through the integration of advanced information technologies such as artificial intelligence (AI) and the Internet of Things (IoT). However, due to its interdisciplinary nature, the relevant documents on IC are diverse and fragmented. To provide a comprehensive understanding of research progress and future opportunities in IC and to offer suggestions for both developing and developed countries, this study employed VOSviewer and Gephi to conduct a comparative review of relevant literature from the 21st century. A keyword search of Web of Science (WOS) identified 2788 relevant documents which were subjected to an overall co-citation and co-authorship analysis. To illustrate the differences between developing and developed countries, China, a representative developing country, was taken as the candidate to be compared with developed countries via a co-occurrence analysis. Differences between China and developed countries in the three sub-directions of IC, research foundation and domain knowledge transformation; information perception, fusion, and decision making; and embodied AI, were qualitatively discussed. Finally, four future research directions were suggested: (1) data fusion and decision-making, (2) improving the accuracy and efficiency of knowledge representation, learning, and utilization, (3) the establishment of large, pre-trained models in the field, and (4) embodied AI for taking actions according to the decisions made. This paper provides an overview of the relevant literature and the IC context for practitioners and scholars in the AEC industry in countries with different levels of development, as well as suggestions for the future development of IC. The findings of this study can serve both academia and industry in promoting IC in the AEC industry.

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Section: Intelligent Algorithms In the Whole Life Cycle Of Constructi...mentioning

confidence: 99%

Recent Research Progress in Intelligent Construction: A Comparison between China and Developed Countries

et al. 2023

View full text Add to dashboard Cite

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“…Currently, the prediction model of shield tunnelling parameters is based on the fixed dataset, and the fixed model is applied to predict the subsequent construction [17,27,37,40]. The trained model is not updated in realtime to consider the expansion of the dataset, which obviously wastes a lot of high-quality data.…”

Section: Introductionmentioning

confidence: 99%

An intelligent construction model in whole process for shield tunnelling

Lu,

Liu,

Kong

et al. 2024

Preprint

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Predicting shield tunnelling parameters in the whole construction process is of great importance, which can effectively control ground stability and improve tunnelling efficiency. A novel deep learning method is developed considering transfer learning, incremental learning and Bi-LSTM fusing with available data of the next ring to be excavated (ADNRE) to predict shield tunnelling parameters in the whole process. Before construction, transfer learning uses data from similar projects to determine initial network parameters, then solve the insufficient data in the prophase of the project. As the shield machine begins to excavate, incremental learning is used to continuously accept new data and adjust model parameters in real time during the whole process. A feature fusion module in Bi-LSTM is proposed to integrate ADNRE and data of the adjacent excavated rings. The proposed Bi-LSTM method can consider the mutation of stratum conditions during tunnelling. The applicability of the proposed method is explored by predicting the shield cutter head torque of a tunnel project in Qingdao, China. The influence of fine-tuning epochs and project similarity on model performance is further discussed. Overall, the proposed method can provide reasonable whole process prediction for shield tunnelling parameters, which improves construction safety and efficiency.

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Geological challenges of tunnelling using large slurry shield machine in dense urban centre

Zeng,

Elbaz,

Xiong

et al. 2024

Geological Journal

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Tunnelling in complex geological strata is a challenging task that can affect the performance of shield machines and result in unexpected hazards. Therefore, extensive knowledge of unfavourable environments and shield operational parameters is essential for ensuring successful and safe tunnelling. This study aims to analyse the geological and hydrological conditions of the Chunfeng Tunnel and present the key construction technologies of a large slurry shield machine when crossing complex strata. To this end, attempts were made to (i) discuss the geological and environmental challenges encountered during the tunnelling process and present some early interpretations based on the database information, (ii) analyse the factors affecting tunnel performance and simulate the ground surface settlement along the tunnel alignment and (iii) discuss the countermeasures under adverse ground conditions and propose future directions. The results revealed that tunnelling in fault zones with soft upper and hard lower strata led to severe wear of disc cutters and controlling the tunnelling parameters could provide a suitable method for adjusting the construction process. This study leveraged extensive experience gained during tunnelling using a large slurry shield machine, with special emphasis on mixed ground strata, by providing valuable insights and suggested responses for effective tunnelling strategies.

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Use of deep learning, denoising technic and cross-correlation analysis for the prediction of the shield machine slurry pressure in mixed ground conditions

Cited by 17 publications

References 41 publications

Recent Research Progress in Intelligent Construction: A Comparison between China and Developed Countries

Recent Research Progress in Intelligent Construction: A Comparison between China and Developed Countries

An intelligent construction model in whole process for shield tunnelling

Geological challenges of tunnelling using large slurry shield machine in dense urban centre

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