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Time-series forecasting of consolidation settlement using LSTM network

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Abstract

Consolidation settlement refers to the deformation of soil due to external forces resulting in a reduction in the soil volume, posing a significant challenge for construction on soft ground due to the high compressibility of the soil. Methods, such as preloading and prefabricated vertical drains, have been applied to enhance the strength of the ground and accelerate the consolidation process. However, measurement-based methods, which are commonly used in practice, tend to produce inaccurate results when the measurement records are limited, and the prediction results may have a large variance depending on an engineering judgment. This study aimed to overcome these limitations by developing a long short term memory (LSTM) based model for predicting consolidation settlement with improved accuracy. The model was evaluated through 120 cases with different amounts of training data (10%–70%) and was compared with practical methods such as the hyperbolic and Asaoka methods. The LSTM model outperformed the practical methods, with an average RMSE and MAPE of less than 0.1m and 2%, respectively. The model was also capable of predicting the final settlement with an average MAPE of less than 3% regardless of the amount of training. Statistical analysis have also indicated that the LSTM model had the highest probability of accurately predicting the settlement. The results of this study indicate that deep learning algorithms can be successfully used to predict consolidation settlement and provide highly accurate predictions for construction projects on soft ground. This study has the potential to assist the design and construction of civil engineering projects.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Funding

This research was supported by Ministry of Oceans and Fisheries (project no. 20230207-002)

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Seoul National University, 1 Gwanak-Ro, Gwanak-Gu, Seoul, Republic of Korea

    Seongho Hong & Sung-Ryul Kim

  2. Geotechnical & Geology Engineering Team, GS Engineering and Construction (GS E&C), 2477, Nambusunhwan-ro, Seocho-gu, Seoul, Republic of Korea

    Seok-Jun Ko

  3. Department of Civil and Environmental Engineering, Incheon National University, 119 Academy-Ro, Yeonsu-Gu, Incheon, Republic of Korea

    Sang Inn Woo

  4. Geotechnical Engineering Research Division, Korea Institute of Civil Engineering and Building Technology, 283 Goyang-dearo, Ilsanseo-Gu, Gyeonggi-Do, Republic of Korea

    Tae-Young Kwak

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  1. Seongho Hong
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Correspondence to Sung-Ryul Kim.

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Hong, S., Ko, SJ., Woo, S.I. et al. Time-series forecasting of consolidation settlement using LSTM network. Appl Intell 54, 1386–1404 (2024). https://doi.org/10.1007/s10489-023-05219-7

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