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Developing effective optimized machine learning approaches for settlement prediction of shallow foundation.

Mohammad Khajehzadeh1,2, Suraparb Keawsawasvong1, Viroon Kamchoom3

  • 1Research Unit in Sciences and Innovative Technologies for Civil Engineering Infrastructures, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, 12120, Thailand.

Heliyon
|September 19, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid machine learning method, AEFSCO, to accurately predict shallow foundation settlement in sandy soils. The optimized LSTM model significantly improved prediction accuracy, outperforming other models.

Keywords:
Foundation settlementHybrid metaheuristicLong short-term memoryParameter optimization

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Area of Science:

  • Geotechnical Engineering
  • Machine Learning
  • Computational Science

Background:

  • Accurate prediction of shallow foundation settlement on cohesionless soils is crucial but challenging due to complex influencing factors.
  • Existing methods often struggle with the inherent uncertainties in geotechnical parameters.
  • Developing advanced predictive models is essential for reliable foundation design.

Purpose of the Study:

  • To develop and validate a novel hybrid machine learning methodology for estimating shallow foundation settlement (Sm).
  • To optimize machine learning models using a new hybrid optimization algorithm for enhanced prediction accuracy.
  • To assess the performance of optimized models in predicting foundation settlement based on soil and geometric properties.

Main Methods:

  • Development and validation of the Artificial Electric Field and Single Candidate Optimizer (AEFSCO) hybrid optimization algorithm.
  • Optimization of Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and Multilayer Perceptron Neural Network (MLPNN) models using AEFSCO.
  • Training and testing models on a database of 189 case histories with five input parameters (foundation geometry, soil properties) and one output (settlement).

Main Results:

  • The AEFSCO optimization significantly improved the coefficient of determination (R²) for all tested machine learning models.
  • MLPNN, SVR, and LSTM models showed R² increases of 9.3%, 8%, and 22%, respectively, after AEFSCO optimization.
  • The LSTM-AEFSCO model demonstrated superior performance with an R² of 0.9903, outperforming SVR-AEFSCO (0.9494) and MLPNN-AEFSCO (0.9290).

Conclusions:

  • Hybrid optimization using AEFSCO substantially enhances the accuracy of machine learning models for predicting shallow foundation settlement.
  • The optimized LSTM model (LSTM-AEFSCO) provides the most accurate predictions among the evaluated methods.
  • This advanced hybrid approach offers a promising tool for reliable geotechnical engineering assessments of foundation settlement.