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用机器学习算法对酶诱导碳酸沉 (EICP) -固化粘土混合物的剪切强度进行预测建模
Qiang Ma1,2,3, Meng Li1,2,3, Hang Shu1,2,3
1Hubei Key Laboratory of Environmental Geotechnology and Ecological Remediation for Lake & River, Hubei University of Technology, Wuhan 430068, China.
Polymers
|April 12, 2025
概括
这项研究使用颗粒和酶诱导碳酸沉 (EICP) 来增强粘土的剪切强度. 一个新的CPO-CNN-LSTM模型准确地预测了强度,优化了地质工程应用.
科学领域:
- 地质技术工程 地质技术工程
- 材料科学 材料科学 材料科学
- 计算科学 计算科学
背景情况:
- 优化土壤成分和减少实验变异性是地质工程的关键挑战.
- 确定土壤中最佳含量和颗粒大小的传统方法耗时且资源密集.
- 酶诱导碳酸沉 (EICP) 为土壤固化提供了一种新的方法.
研究的目的:
- 为了研究颗粒对EICP处理的粘土的剪切强度的影响.
- 为这种复合材料的剪切强度开发和验证一个预测机器学习模型.
- 为了优化预测模型使用皇冠白优化 (CPO) 算法.
主要方法:
- 实验分析混合不同含量和颗粒大小的粘土,通过EICP固化.
- 混合卷积神经网络 (CNN) 和长短期记忆 (LSTM) 模型的开发.
- 优化CNN-LSTM模型使用Crown Porcupine Optimization (CPO) 算法进行剪切强度预测.
主要成果:
- 添加颗粒显著提高了EICP处理的粘土的剪切强度,在5%的含量下观察到最佳结果.
- 拟议的CPO-CNN-LSTM模型与其他机器学习算法相比,显示出优异的预测性能.
- 该模型实现了高预测准确度,训练数据的确定系数 (R2) 为0.98,测试数据的确定系数为0.97.
结论:
- CPO-CNN-LSTM模型提供了一种可靠且准确的方法,用于预测用颗粒增强的EICP处理粘土的剪切强度.
- 这种预测建模方法可以显著减少实验不确定性,并优化地质工程中的材料组成.
- 这些发现强调了将EICP固化与颗粒强化相结合的潜力,以改善土壤性能.
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