基于云的机器学习技术的水泥强度预测

IF 3 Q2 ENGINEERING, CIVIL
Nand Kumar, V. Naranje, S. Salunkhe
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引用次数: 9

摘要

本文介绍了一种基于云的水泥强度预测软件框架,用于预测2天、7天和28天的水泥强度。利用Levenbarg-Marquardt反向传播人工神经网络(LMBP-ANN)建立预测模型。该人工神经网络模型使用70%的数据进行训练(70%,212条数据记录),测试(15%,46条数据记录)和验证(15%,46条数据记录)。水泥强度预测共考虑了16个重要的输入参数。用户界面和软件框架使用Python编程语言构建。多个Python包用于ANN模型的实现。采用Ubuntu操作系统的云服务器来承载水泥强度预测的web应用程序。软件应用程序使用来自各个水泥行业的实时数据进行测试。所提出的基于人工神经网络的软件应用程序对水泥强度的预测似乎与目前水泥制造业实验数据中产生的预测非常相似。通过实验值与预测值的Pearson相关性,验证了基于反向传播人工神经网络算法所建立模型的充分性。对数据进行实验的R计算值为0.82539,为0.6813。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cement strength prediction using cloud-based machine learning techniques
ABSTRACT This paper describes a cloud-based software framework to predict cement strength for 2 days, 7 days and 28 days. Levenbarg-Marquardt back-propagation-artificial neural network (LMBP-ANN) is used to build a prediction model. This ANN model uses 70% of data for training (70%, 212 data records), testing (15%, 46 data records) and for validation (15%, 46 data records). A total of 16 significant input parameters are considered for the cement strength prediction. The user interface and software framework are built using the Python programming language. Multiple Python packages are used for the implementation of the ANN model. The cloud server having Ubuntu operating system has been used to host the web application for prediction of cement strength. The software application is tested using real-time data from various cement industries. The prediction of the cement strength of the proposed ANN-based software application appears to be very similar to those currently generated in experimental data in the cement manufacturing industry. The adequacy of the developed model based on the back-propagation ANN algorithm is confirmed as the Pearson correlation of experimental value and predicted value. The calculated value of R for experimentations on the data is 0.82539 and is 0.6813.
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来源期刊
CiteScore
3.90
自引率
9.50%
发文量
24
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