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Dynamic prediction of jet grouted column diameter in soft soil using Bi-LSTM deep learning

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Abstract

The bidirectional long short-term memory (Bi-LSTM) network is an innovative computation paradigm that learns bidirectional long-term dependencies between time steps and sequence data to predict future occurrences. This study proposes a framework to incorporate Bi-LSTM and data sequencing to predict diameter of jet grouted columns in soft soil in real time. The models are tested using a case study of jet grouting treatment of soft soil. The results show that the proposed strategies can efficiently predict the variation in column diameter with the depth. A comparative performance analysis among the Bi-LSTM, original long short-term memory (LSTM) and support vector regression (SVR) approaches is also conducted. The Bi-LSTM performs better than both the LSTM and SVR in root-mean-square error. This result substantiates the efficacy of modeling sequential step-by-step jet grouting process using the Bi-LSTM. Based on the analyzed results, some recommendations for improving the current design of jet grout columns are proposed.

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Acknowledgements

The research work described herein was funded by “The Pearl River Talent Recruitment Program” in 2019 for Professor Shui-Long Shen (Grant No. 2019CX01G338), Guangdong Province and the Research Funding of Shantou University for New Faculty Member (Grant No. NTF19024-2019), and the National Natural Science Foundation of China (NSFC) (Grant No. 41372283). The financial supports mentioned above are gratefully acknowledged.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Manufacturing Technology (Shantou University), Ministry of Education, China and College of Engineering, Shantou University, Shantou, 515063, Guangdong, China

    Shui-Long Shen & Pierre Guy Atangana Njock

  2. Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Department of Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Shui-Long Shen & Pierre Guy Atangana Njock

  3. Discipline of Civil and Infrastructure, School of Engineering, Royal Melbourne Institute of Technology (RMIT), Victoria, 3001, Australia

    Shui-Long Shen & Annan Zhou

  4. State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, China

    Hai-Min Lyu

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  1. Shui-Long Shen
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  2. Pierre Guy Atangana Njock
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  3. Annan Zhou
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  4. Hai-Min Lyu
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Correspondence to Shui-Long Shen or Pierre Guy Atangana Njock.

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Shen, SL., Atangana Njock, P.G., Zhou, A. et al. Dynamic prediction of jet grouted column diameter in soft soil using Bi-LSTM deep learning. Acta Geotech. 16, 303–315 (2021). https://doi.org/10.1007/s11440-020-01005-8

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