A Method to Improve the Concrete Shrinkage Prediction Based on Short-Time Tests

2019 5th International Conference on Energy Materials and Environment Engineering

能源学;生态环境科学

Hu, Shi-Xiang^1 ; Luo, Min^2^3 ; Cong, Ling^1 ; Lin, Min^1

School of Economics and Management, Nanjing Institute of Technology, Nanjing

211167, China^1

Jincheng College, Nanjing University of Aeronautics and Astronautics, Nanjing

211156, China^2

School of Civil Engineering, Southeast University, Nanjing

210096, China^3

关键词: Calculation accuracy;    Computational accuracy;    Concrete shrinkage;    Ill posed problem;    Improvement methods;    Physical mechanism;    Shrinkage deformation;    Statistical scatter;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/295/4/042034/pdf
DOI  :  10.1088/1755-1315/295/4/042034

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

The computational accuracy of concrete prediction models is the key to investigate shrinkage influence on performance of concrete structure. Commonly used concrete prediction models are evaluated for their accuracy by comparing their predicted results against the six groups of test data that are collected from published papers. The results show that prediction models were not coincided very well with the experimental data. The phenomenon of concrete shrinkage is a result of several interacting physical mechanisms and is influenced by many variable factors. The shrinkage deformations invariably exhibit large statistical scatter. The calculated results of prediction models could not agree very well with the test results. In order to improve the calculation accuracy of concrete shrinkage prediction models, updating the prediction models based on short-time tests is an effective method. And the general method is not proper because of ill-posed problem. So a new improvement method was proposed and suggested in this study. Seven groups of concrete shrinkage test data are used to evaluate the suggested method. It could be found that the suggested method could match better with the test data.