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Multi-factor settlement prediction around foundation pit based on SSA-gradient descent model.

Zhengcai Li1, Xinmin Hu1, Chun Chen2

  • 1College of Construction Engineering, Jilin University, Changchun, 130026, China.

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This summary is machine-generated.

This study introduces the Sparrow Search Algorithm (SSA) to optimize gradient descent models for land subsidence prediction, improving model stability and efficiency. SSA demonstrated superior performance compared to other algorithms and neural network models.

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Area of Science:

  • Geotechnical Engineering
  • Machine Learning Applications
  • Environmental Monitoring

Background:

  • Traditional gradient descent algorithms like Backpropagation (BP) and Elman for settlement prediction are sensitive to initial weight and threshold selection.
  • Machine learning offers powerful tools for complex prediction tasks, but algorithm optimization is crucial for accuracy.

Purpose of the Study:

  • To enhance the performance and stability of gradient descent-based settlement prediction models.
  • To evaluate the effectiveness of the Sparrow Search Algorithm (SSA) in optimizing these models.
  • To compare SSA with other optimization algorithms and neural network architectures for land subsidence prediction.

Main Methods:

  • Developed a joint model using the Sparrow Search Algorithm (SSA) to optimize gradient descent algorithms.
  • Utilized two datasets of land subsidence monitoring data from a foundation pit excavation in South China for validation.
  • Compared the performance of SSA with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).
  • Further compared SSA optimized models including SSA-KELM, SSA-LSSVM, and SSA-BP.

Main Results:

  • SSA significantly improved the optimization of gradient descent models, enhancing their stability.
  • SSA exhibited higher optimization efficiency compared to GA and PSO algorithms.
  • SSA demonstrated superior optimization efficiency for the Backpropagation (BP) neural network compared to other neural network types.
  • The seven selected input variables were validated as feasible for the prediction model, aligning with grey relational analysis findings.

Conclusions:

  • The Sparrow Search Algorithm is a highly effective optimizer for gradient descent models in land subsidence prediction.
  • SSA offers improved stability and efficiency over traditional optimization methods and other neural network models.
  • The chosen input variables are robust for predicting land subsidence, providing a reliable foundation for the developed model.