Prediction of the compression index and swell index of soft soils via an optimized multiple-output neural network

CHEN Kai; LIN Jun; NIE Liqing; DUAN Wei

doi:10.19509/j.cnki.dzkq.tb20240439

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Article Navigation > Bulletin of Geological Science and Technology > 2025 > Corrected proof

Suitability and potential evaluation of geological storage of carbon dioxide in saline aquifer of Ying-Qiong Basin[J]. Bulletin of Geological Science and Technology. doi: 10.19509/j.cnki.dzkq.tb20240366

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CHEN Kai，LIN Jun，NIE Liqing，et al. Prediction of the compression index and swell index of soft soils via an optimized multiple-output neural network[J]. Bulletin of Geological Science and Technology，2025，44（2）：1-16 doi: 10.19509/j.cnki.dzkq.tb20240439

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Prediction of the compression index and swell index of soft soils via an optimized multiple-output neural network

doi: 10.19509/j.cnki.dzkq.tb20240439

CHEN Kai^1
,,
LIN Jun^{1, 2
,
,},
NIE Liqing¹,
DUAN Wei³

1.
Anhui Provincial Key Laboratory of Intelligent Underground Detection, Anhui Jianzhu University, Hefei 230601, China
2.
School of Architecture and Civil Engineering, Jiangsu Open University, Nanjing 210036, China
3.
College of Civil Engineering, Taiyuan University of Technology, Taiyuan 030024, China

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Author Bio:
E-mail：kaichen20010126@163.com
Corresponding author: E-mail：aslinjun@163.com
Received Date: 07 Aug 2024
Accepted Date: 19 Nov 2024
Rev Recd Date: 15 Nov 2024

Available Online: 21 Mar 2025

Abstract

Abstract

Objective
The compression index C_c and swell index C_s of soil are critical parameters for calculating soil settlement and swelling. Utilizing machine learning algorithms to predict these indices quickly and efficiently can significantly reduce testing duration and costs.
Methods
In this study, we introduce Piezocone Penetration Test (CPTU) in-situ data and quantify soil layer information using the Soil Behaviour Type (SBT) index I_c. We then combine laboratory data with CPTU data to develop a multi-output genetic algorithm-optimized backpropagation neural network (GA-BPNN) model. The input parameters for the multi-output GA-BPNN model were determined through correlation analysis. Using the TC304 standard site database, the prediction results from the multi-output GA-BPNN model were compared with those from the multi-output BPNN model and the single-output GA-BPNN model, verifying the effectiveness of the multi-output GA-BPNN model and obtaining pre-trained model parameters. For sites with limited data in Nanjing, the superiority of the multi-output BPNN model was further evaluated by analyzing the impact of pre-training and in-situ test data on model performance. A sensitivity analysis was also conducted to assess the robustness of the model.
Results
The results demonstrate that the pre-trained multi-output GA-BPNN model, derived from standard site data, can effectively predict the compression and swell indices under limited data conditions. When combined with in-situ test data, the multi-output GA-BPNN model exhibits high prediction accuracy for these indices, with predicted values closely matching measured data. The consistency of the predicted results aligns well with existing studies.
Conclusion
The pre-trained multi-output GA-BPNN model can efficiently predict the compression and swell indices of soft soil under limited data conditions. The proposed method shows significant potential for multi-parameter prediction in engineering practice, enhancing the efficiency and reliability of geotechnical engineering assessments.
- compression index,
- swell index,
- multiple-output,
- optimized neural network,
- GA-BPNN model,
- soft soil

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Prediction of the compression index and swell index of soft soils via an optimized multiple-output neural network

doi: 10.19509/j.cnki.dzkq.tb20240439

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Prediction of the compression index and swell index of soft soils via an optimized multiple-output neural network

doi: 10.19509/j.cnki.dzkq.tb20240439

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