Prediction of Pile Running during Installation Using Deep Learning Method
Abstract
:1. Introduction
2. Methodology
2.1. Dataset
2.2. DL Model Development
2.3. Traditional Methods
3. Performance
3.1. Predictive Accuracy
3.2. Sensitivity Analysis
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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No. | Soil Type | d (m) | (kN/m3) | Dr (%) | φ (°) | cu (kPa) |
---|---|---|---|---|---|---|
1 | Mud | 2.4 | 6.5 | / | / | 26 |
2 | Silty clay | 7.6 | 7.0 | / | / | 31.3 |
3 | Silty clay with silty sand | 12.0 | 8.4 | / | / | 57.3 |
4 | Silty sand with silty clay | 16.5 | 9.5 | 35 | 31 | / |
5 | Silty clay with silty sand | 20.7 | 8.5 | / | / | 78.3 |
6 | Silty sand-1 | 21.9 | 9.5 | 30 | 30 | / |
7 | Silty sand-2 | 25.5 | 10.0 | 55 | 35.3 | / |
8 | Silty sand-3 | 41.3 | 10.6 | 65 | 36.3 | / |
9 | Silty sand-4 | 60.5 | 10.5 | 60 | 36.3 | / |
Hidden Size | Activation Function | Validation Split | Batch Size | Optimizer | Loss Function | Early Stopping Patience |
---|---|---|---|---|---|---|
3 × 64 | ReLU | 0.1 | 128 | Adam | MSE | 20 |
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He, B.; Shi, R.; Guan, Q.; Yang, Y. Prediction of Pile Running during Installation Using Deep Learning Method. J. Mar. Sci. Eng. 2024, 12, 1099. https://doi.org/10.3390/jmse12071099
He B, Shi R, Guan Q, Yang Y. Prediction of Pile Running during Installation Using Deep Learning Method. Journal of Marine Science and Engineering. 2024; 12(7):1099. https://doi.org/10.3390/jmse12071099
Chicago/Turabian StyleHe, Ben, Ruilong Shi, Qingzheng Guan, and Yitao Yang. 2024. "Prediction of Pile Running during Installation Using Deep Learning Method" Journal of Marine Science and Engineering 12, no. 7: 1099. https://doi.org/10.3390/jmse12071099