A Deep Learning–Enhanced Framework for Predicting Lique-faction Susceptibility of Sandy Soils Using SPT-Based Geotech-nical Data
Subject Areas : Soil-Structure Interaction
shima aghakasiri
1
,
ghodratollah mohammadi
2
,
amir taban
3
,
mohammad emami kourandeh
4
1 -
2 - Department of Civil Engineering, S.T.C., Islamic Azad University, Tehran, Iran
3 - 3 Department of Civil Engineering, KH.A.C, Islamic Azad University, Khorramabad (Iran)
4 - Department of Civil Engineering, S.T.C., Islamic Azad University, Tehran, Iran.
Keywords: Liquefaction, Deep Learning, CNN–MVO, SPT, Geotechnical Engineering, Soft Computing.,
Abstract :
Soil liquefaction remains one of the most critical challenges in geotechnical earthquake engineering, often resulting in severe ground deformation, settlement, and infrastructure failure during strong seismic events. Traditional empirical methods, while widely used, are limited in their ability to capture the nonlinear and complex interactions among soil parameters. This study introduces a hybrid deep learning framework based on a Convolutional Neural Network (CNN) optimized using the Multi-Verse Optimizer (MVO) to predict liquefaction potential and post-liquefaction settlement. A comprehensive geotechnical database consisting of 300 borehole records from the northern provinces of Iran—including SPT data, groundwater level, soil type, fine content, and liquefiable depth—was used for model training and evaluation. The hybrid CNN–MVO model demonstrated high predictive capability, achieving regression coefficients exceeding 90% and Mean Squared Error (MSE) values below 0.5 across training, testing, and validation sets. Sensitivity analysis revealed that fine content (FP) had the strongest influence on liquefaction potential, followed by SPT-N and soil type. The results confirm that combining CNN with MVO significantly enhances model accuracy and parameter interpretability, offering a robust alternative to traditional liquefaction assessment methods. The proposed model can support engineers in developing more reliable seismic risk evaluations and mitigation strategies in liquefaction-prone regions.
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