Prediction of elastic modulus of normal and high strength concrete by support vector machine

Kezhen Yan; Caijun Shi

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Prediction of elastic modulus of normal and high strength concrete by support vector machine

机译：支持向量机预测高强混凝土的弹性模量。

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Elastic modulus is an important property of concrete and is used to calculate deformation of structures. Support vector machine (SVM) is firmly based on learning theory and uses regression technique by introducing accuracy insensitive loss function. This paper investigates the use of SVM to predict elastic modulus of normal and high strength concrete. The elastic modulus predicted by SVM was compared with the experimental data and those from other prediction models. SVM demonstrated good performance and proven to be better than other models.

机译：弹性模量是混凝土的重要特性，用于计算结构的变形。支持向量机（SVM）牢固地基于学习理论，并通过引入精度不敏感的损失函数来使用回归技术。本文研究了使用支持向量机预测普通和高强度混凝土的弹性模量。将SVM预测的弹性模量与实验数据以及其他预测模型的弹性模量进行了比较。 SVM表现出良好的性能，并被证明比其他模型更好。

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来源
《Construction and Building Materials》 |2010年第8期|p.1479-1485|共7页
作者
Kezhen Yan; Caijun Shi;
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作者单位

College of Civil Engineering, Hunan University, Changsha, Hunan 410082, China;

rnCollege of Civil Engineering, Hunan University, Changsha, Hunan 410082, China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
support vector machine; concrete; compressive strength; elastic modulus;

机译：支持向量机具体;抗压强度;弹性模量;

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1. Prediction of Compressive Strength of Self-Compacting Concrete using Least Square Support Vector Machine and Relevance Vector Machine [J] . Bhairevi Ganesh Aiyer, Dookie Kim, Nithin Karingattikkal, KSCE journal of civil engineering . 2014,第6期

机译：最小二乘支持向量机和相关向量机预测自密实混凝土的抗压强度。
2. Prediction of elastic modulus of normal and high strength concrete using ANFIS and optimal nonlinear regression models [J] . Behrouz Ahmadi-Nedushan Construction and Building Materials . 2012,第期

机译：使用ANFIS和最佳非线性回归模型预测高强混凝土的弹性模量。
3. Prediction of elastic modulus of normal and high strength concrete by artificial neural networks [J] . Fuat Demir Construction and Building Materials . 2008,第7期

机译：用人工神经网络预测普通高强混凝土的弹性模量
4. Machine Learning-Based Double-Profile Intersection for Pointwise Prediction of Shear Elastic Modulus through Support Vector Regression [C] . Nada Rahmouni, Keita A. Yokoyama, Caterina M. Gallippi IEEE International Ultrasonics Symposium . 2020

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5. Prediction of 28-day compressive strength of concrete using relevance vector machines (RVM). [D] . Owusu Twumasi, Jones. 2013

机译：使用相关矢量机（RVM）预测混凝土的28天抗压强度。
6. Prediction of Static Modulus and Compressive Strength of Concrete from Dynamic Modulus Associated with Wave Velocity and Resonance Frequency Using Machine Learning Techniques [O] . Jong Yil Park, Sung-Han Sim, Young Geun Yoon, 2020

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7. Prediction of Concrete Strength with P-, S-, R-Wave Velocities by Support Vector Machine (SVM) and Artificial Neural Network (ANN) [O] . Jong Yil Park, Young Geun Yoon, Tae Keun Oh 2019

机译：用支持向量机（SVM）和人工神经网络（ANN）对P型，S型，R波速度的混凝土强度的预测
8. Study on Strength, Elastic Modulus of Artificial Lightweight Aggregate Concrete [R] . Kakizaki, M. 1982

机译：人工轻骨料混凝土的强度，弹性模量研究

Prediction of elastic modulus of normal and high strength concrete by support vector machine

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