Scholarlink

This study presents a data-driven approach to predicting settlement of statically loaded pile foundations. We employ an Artificial Neural Network (ANN) model whose hyperparameters are fine-tuned using the Tree-Structured Parzen Estimation (TPE) method. The training procedure uses data obtained from physical tests, incorporating key factors such as nodular pile size, vertical loading conditions, and soil resistance characteristics. The results demonstrate that the optimized ANN model provides robust predictive performance. This approach offers valuable potential to improve the reliability of geotechnical design practices.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

TPE-Optimized Neural Network Framework for Predicting Settlement of Nodular Pile Foundations

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Hung La,
  • Tan Nguyen,
  • Khiem Quang Tran

摘要

This study presents a data-driven approach to predicting settlement of statically loaded pile foundations. We employ an Artificial Neural Network (ANN) model whose hyperparameters are fine-tuned using the Tree-Structured Parzen Estimation (TPE) method. The training procedure uses data obtained from physical tests, incorporating key factors such as nodular pile size, vertical loading conditions, and soil resistance characteristics. The results demonstrate that the optimized ANN model provides robust predictive performance. This approach offers valuable potential to improve the reliability of geotechnical design practices.