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首页> 外文期刊>Neural computing & applications >Strength retrieval of artificially cemented bauxite residue using machine learning: an alternative design approach based on response surface methodology
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Strength retrieval of artificially cemented bauxite residue using machine learning: an alternative design approach based on response surface methodology

机译：采用机器学习强度检索人工粘液铝土矿残渣：一种基于响应面方法的替代设计方法

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摘要

The aim of the present study is to propose an alternative artificial neural network model based on response surface methodology over conventional approach to estimate the unconfined compressive strength of artificially cemented bauxite residue. The artificial neural network model uses molding moisture content (w), curing time (t) and porosity/volumetric lime (eta/L-v ') as input parameters and unconfined compressive strength as the output parameter. Bayesian regularization as training function with sigmoid and pure linear at hidden and output layers is used for modeling the artificial neural network. The proposed response surface methodology designed ANN model is comparable with the conventional designed ANN model and can be used effectively with significantly less number of data set. Sensitivity analysis, to make out the significant input factors based on connection-weight approach, is also discussed. Further, neural interpretation diagram is incorporated to study the effects of individual input parameters over the response. Finally, a predictive equation is presented based on response surface methodology designed artificial neural network model for the range of parameters studied.
机译：本研究的目的是提出基于常规方法的响应面方法的替代人工神经网络模型来估计人工粘合的铝土矿残留物的非整合抗压强度。人工神经网络模型使用模塑水分含量（W），固化时间（T）和孔隙度/体积钙（ETA / L-V'）作为输入参数和作为输出参数的非整合压缩强度。贝叶斯正常化作为训练功能，隐藏和输出层的Sigmoid和纯线性用于建模人工神经网络。所设计的ANN模型所设计的ACHACE方法与传统设计的ANN模型相当，并且可以有效地使用明显更少的数据集。还讨论了敏感性分析，制定基于连接重量方法的重要输入因素。此外，结合了神经解释图，以研究各个输入参数对响应的影响。最后，基于研究的参数范围的响应面方法提供了一种预测方程。

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