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Prediction of 28-day compressive strength of concrete using relevance vector machines (RVM).

机译：使用相关矢量机（RVM）预测混凝土的28天抗压强度。

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摘要

Early and accurate prediction of the compressive strength of concrete is important in the construction industry. Modeling the compressive strength of concrete to obtain a balance and equality between prediction accuracy, time and uncertainty of the prediction is a very difficult task due to the highly nonlinear nature of concrete. For structural engineering purposes, the 28- day compressive strength is the most relevant parameter. In this study, an attempt has been made to predict the 28-day compressive strength of concrete using Relevance Vector Machine (RVM). An RVM belongs to the class of sparse kernel classifiers, which are powerful tools in classification and regression. It has a model of identical functional form to the popular and state-of-the-art `Support Vector Machine (SVM)'. The benefits of using RVM include automatic estimation of nuisance parameters, probabilistic prediction and the ability to model complex data with little information. A total of 425 different data of high performance mix designs were collected from the University of California, Irvine repository. The data used to predict the compressive strength consisted of nine components. The RVM model was trained and tested using 395 and 30 data sets respectively. The model's performance was assessed at the end of the training and testing period using four performance measures; coefficient of determination, root-mean-square error, percentage of relevance vectors and residual plots. All the performance measures confirmed the accuracy of the model. The results of the study suggested that RVM is an effective tool for predicting the 28- day compressive strength of concrete from its mix ingredients.
机译：早期准确预测混凝土的抗压强度在建筑行业中很重要。由于混凝土的高度非线性特性，对混凝土的抗压强度进行建模以在预测精度，时间和预测的不确定性之间取得平衡和相等是一项非常困难的任务。对于结构工程而言，28天抗压强度是最相关的参数。在这项研究中，已尝试使用相关矢量机（RVM）来预测混凝土的28天抗压强度。 RVM属于稀疏内核分类器的类别，它们是分类和回归中的强大工具。它具有与流行和最新的“支持向量机（SVM）”相同的功能形式的模型。使用RVM的好处包括对干扰参数的自动估计，概率预测以及仅需很少信息即可对复杂数据进行建模的功能。从加利福尼亚大学尔湾分校的库中收集了总共425种不同的高性能混合物设计数据。用于预测抗压强度的数据由九个组成部分组成。 RVM模型分别使用395个和30个数据集进行了训练和测试。在训练和测试阶段结束时，使用四个绩效指标评估了模型的绩效。确定系数，均方根误差，相关向量的百分比和残差图。所有性能指标均证实了模型的准确性。研究结果表明，RVM是一种有效的工具，可根据其混合成分预测混凝土的28天抗压强度。

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  • 作者

    Owusu Twumasi, Jones.;

  • 作者单位

    Southern Illinois University at Carbondale.;

  • 授予单位 Southern Illinois University at Carbondale.;
  • 学科 Engineering Civil.
  • 学位 M.S.
  • 年度 2013
  • 页码 51 p.
  • 总页数 51
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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