1.
Prediction model of concrete strength based on fuzzy system method
基于模糊系统方法的混凝土强度预测模型
2.
A Prediction Model of Concrete Strength Based on Artificial Neural Network;
基于人工神经网络的混凝土强度预测模型
3.
Study on the prediction model of cement fly ash steel slag base intensity
水泥粉煤灰钢渣基层强度预测模型研究
4.
Forecast of cement strength based on RBF neural networks
基于RBF神经网络水泥强度预测模型的研究
5.
The Research on Interfacial Bond Quality Inspection of New-to-old Concrete and the Prediction Model for the Pull Strength of the Bonding Interface;
新老混凝土界面粘结质量检验与粘结强度预测模型研究
6.
STATISTICAL FA RE PREDICTION MODELS FOR ST UCTURAL CERAMICS
结构陶瓷材料断裂强度统计预测模型
7.
DOUBLE PARAMETER MODEL PREDICTING STRENGTH OF CONCRETE WITH FLY ASH
预测粉煤灰混凝土强度的双参数模型
8.
The Study on Concrete Shrinkage;
混凝土强度和干燥收缩预测模型的研究
9.
Application of model GA-LSSVM in fly ash concrete strength prediction
GA-LSSVM模型在粉煤灰混凝土强度预测中的应用
10.
Study on the Neural Network Model for Forecasting the Blasting Vibration Intensity
爆破震动强度预测的神经网络模型研究
11.
A Model for Scenario Analysis of China s Energy Requirement and Energy Intensity and Its Applications;
中国能源需求和能源强度预测的情景分析模型及其应用
12.
Mechanical strength prediction of plain woven fabric composite:1.finite element model of composite RVE
基于有限单元法的平纹织物复合材料强度预测:1.RVE的有限元模型
13.
Predicting Model and Influencing Factors Analysis of Tensile Strength of PP/Inorganic Particles Composites
PP/无机粒子复合材料拉伸强度的预测模型及影响因素分析
14.
Current Status of Research on Platability of Advanced High Strength Steel Sheet for Automobile and Efficiency of Surface Oxidation-Forecasting by Wagner Model
汽车用先进高强度钢板的可镀性与Wagner模型预测效果研究现状
15.
Grey Prediction Model for Flexural Strength of Concrete Corroded by Acid Rain
基于灰色理论的受酸雨侵蚀混凝土抗折强度的预测模型
16.
Prediction model for compressive strength of concrete with binary fly ash and slag by BP neural network
双掺粉煤灰和矿渣混凝土强度的BP网络预测模型
17.
The Tensile Breaking Progress Simulation and Strength Prediction of Woven Fabric;
机织物拉伸断裂过程模拟及强度预测
18.
The reliable explanation and the less error of forecasting will be helpful in predicting the drilling cost and planning the projects in management.
模型较强的解释力度和较小的预测误差，将有助于钻井成本预测和计划管理。