开发有效的优化机器学习方法,用于对浅基础的定居预测.
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
概括
这项研究引入了一种混合机器学习方法,AEFSCO,以准确预测沙地土壤中浅层基础沉积的情况. 优化的LSTM模型显著提高了预测准确性,超过了其他模型.
科学领域:
- 地质技术工程 地质技术工程
- 机器学习 机器学习
- 计算科学 计算科学
背景情况:
- 在无凝聚性土壤上准确预测浅基础定居点至关重要,但由于复杂的影响因素,这是具有挑战性的.
- 现有的方法经常与地质技术参数固有的不确定性作斗争.
- 开发先进的预测模型对于可靠的基础设计至关重要.
研究的目的:
- 开发和验证一种新的混合机器学习方法,用于估计浅基础结算 (Sm).
- 为了优化机器学习模型,使用新的混合优化算法来提高预测准确度.
- 评估优化模型在根据土壤和几何性质预测基础沉积的性能.
主要方法:
- 开发和验证人工电场和单一候选优化器 (AEFSCO) 的混合优化算法.
- 使用AEFSCO的长短期记忆 (LSTM),支持向量回归 (SVR) 和多层感知神经网络 (MLPNN) 模型的优化.
- 在189个病例历史数据库上的培训和测试模型中,有五个输入参数 (基础几何,土壤特性) 和一个输出 (结算).
主要成果:
- 经过AEFSCO优化,所有测试的机器学习模型的确定系数 (R2) 显著提高.
- 在AEFSCO优化后,MLPNN,SVR和LSTM模型显示R2的增加分别为9.3%,8%和22%.
- LSTM-AEFSCO模型表现出优异的性能,R2为0.9903,表现优于SVR-AEFSCO (0.9494) 和MLPNN-AEFSCO (0.9290).
结论:
- 使用AEFSCO的混合优化大大提高了机器学习模型的准确性,用于预测浅基础结算.
- 优化的LSTM模型 (LSTM-AEFSCO) 在评估的方法中提供了最准确的预测.
- 这种先进的混合方法为基础结算的可靠地质工程评估提供了一个有希望的工具.
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