scholarly journals Early Cracking Risk Prediction Model of Concrete under the Action of Multifield Coupling

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Caiyun Jin ◽  
Jianglin Liu ◽  
Zigeng Wang ◽  
Yue Li
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Temperature Rise ◽  
Coupling Effect ◽  
Degree Theory ◽  
Risk Prediction Model ◽  
Adiabatic Temperature ◽  
Hydration Degree ◽  
Adiabatic Temperature Rise ◽  
Cracking Risk

Through the adiabatic temperature rise experiment, the adiabatic temperature rise of concrete with hydration time was recorded. Based on the maturity degree theory, the relationship between the hydration degree of the concrete and the equivalent age was determined. Then, the hydration degree prediction model of the concrete's early elastic modulus and tensile strength was established. The local temperature and humidity of the concrete were measured by the shrinkage experiment, and based on the capillary water tension theory, a temperature-humidity prediction model for the early shrinkage of the concrete was designed. According to the ratio of the creep deformation and elastic deformation of concrete which were obtained through the restraint ring experiment, a model for predicting the early creep coefficient of concrete was proposed. Based on the coupling effect of “hydration-temperature-humidity,” a prediction model of early cracking risk coefficient of concrete under multifield coupling was proposed. Finally, several groups of slab cracking frame experiments were carried out, and the cracking risk prediction results of concrete were consistent with the actual situation, which indicated the correctness of the early cracking risk prediction model of concrete.

Influence of the Fly-Ash Content of Concrete at Low Water-Binder Ratio on Hydration Degree of Binders and Cement

2011 ◽  
Vol 250-253 ◽  
pp. 445-449
Author(s):  
Li Wei Xu ◽  
Jian Lan Zheng
Keyword(s):  
Fly Ash ◽  
Temperature Rise ◽  
High Performance Concrete ◽  
Ash Content ◽  
Adiabatic Temperature ◽  
Mineral Admixture ◽  
Hydration Degree ◽  
Adiabatic Temperature Rise ◽  
Binder Ratio ◽  
Water Binder Ratio

The hydration degree of binders and cement is investigated by measuring the adiabatic- temperature rise of concrete at low water-binder ratio with different fly-ash content. The results denote that, with a constant water-binder ratio, both of the hydration degree of binders and that of cement decrease with the increasing fly-ash content in the early stage. In a later stage, however, the hydration degree of cement increases with the increasing fly-ash content and the hydration degree of binders peaks when the fly-ash content is 35%. Fly ash is one of the mineral admixture of which high-performance concrete is made up. It brings down the rise of concrete temperature significantly and helps solve the problems of shrinkage and crack of concrete structure. Because the hydration mechanism in common concrete is different from that in concrete with low water-binder ratio, and the hydration environment is different between concrete and cement pastes, to determine the adiabatic-temperature rise of concrete directly conforms to the actual situation. The adiabatic-temperature rise, adiabatic-temperature-rise rate, hydration degree of both binders and cement are investigated by measuring adiabatic-temperature rise of concrete with different fly-ash content.

scholarly journals Study on Adiabatic Temperature Rise Reflecting Hydration Degree of Concrete

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Yanhua Han ◽  
Shaojun Fu ◽  
Shufa Wang ◽  
Zuowei Xie
Keyword(s):  
Neural Network ◽  
Temperature Rise ◽  
Bp Neural Network ◽  
Thermal Model ◽  
Concrete Structures ◽  
Thermal Parameters ◽  
Adiabatic Temperature ◽  
Mass Concrete ◽  
Hydration Degree ◽  
Adiabatic Temperature Rise

The thermal model and the relevant parameters of concrete are the most important issues to study the space-time characteristics of temperature field, which are also the theoretical foundation of temperature control and crack prevention for the mass concrete structures. In this research, the improved adiabatic temperature rise test is carried out, and the temperature variation of fly ash concrete is analyzed. Furthermore, a thermal model of concrete considering the hydration degree is established based on the existing achievements. Meanwhile, the thermal conductivity and specific heat of concrete are measured via three approaches: by treating the parameters as constant values, by computing the parameters as variables of the degree of hydration, and by back-analyzing the parameters through BP neural network. Finally, the thermal parameters determined by different methodologies are substituted into the thermal model, respectively, and the finite element analysis of the concrete specimen is performed. By comparing simulated temperatures with various measured results, it can be found that the numerical analysis results of parameters calculated by BP neural network are closest to the measured values in the whole curing ages. Therefore, BP neural network method is an effective way to calculate the thermal parameters, and BP inversion algorithm provides a new way for accurately study the temperature profile of mass concrete structures.

Development of Neurobehavioral Deterioration Risk Prediction Model for Welder: A Proposed Study

2018 ◽  
Vol 10 (5) ◽  
Author(s):  
Nuur Azreen Paiman ◽  
◽  
Azian Hariri ◽  
Ibrahim Masood ◽  
Arma Noor ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model

Wound dehiscence after radical cystectomy: A novel risk-prediction model

2021 ◽  
Vol 79 ◽  
pp. S1112-S1113
Author(s):  
A.A. Nasrallah ◽  
M. Mansour ◽  
C.H. Ayoub ◽  
N. Abou Heidar ◽  
J.A. Najdi ◽  
...  
Keyword(s):  
Prediction Model ◽  
Radical Cystectomy ◽  
Risk Prediction ◽  
Wound Dehiscence ◽  
Risk Prediction Model

scholarly journals Protocol for the development and validation of a risk prediction model for stillbirths from 35 weeks gestation in Australia

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Jessica K. Sexton ◽  
Michael Coory ◽  
Sailesh Kumar ◽  
Gordon Smith ◽  
Adrienne Gordon ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Risk Prediction ◽  
Late Pregnancy ◽  
Risk Prediction Model ◽  
Past Century ◽  
Reporting Standards ◽  
Robust Method ◽  
Predictive Values ◽  
Multivariable Logistic Regression

Abstract Background Despite advances in the care of women and their babies in the past century, an estimated 1.7 million babies are born still each year throughout the world. A robust method to estimate a pregnant woman’s individualized risk of late-pregnancy stillbirth is needed to inform decision-making around the timing of birth to reduce the risk of stillbirth from 35 weeks of gestation in Australia, a high-resource setting. Methods This is a protocol for a cross-sectional study of all late-pregnancy births in Australia (2005–2015) from 35 weeks of gestation including 5188 stillbirths among 3.1 million births at an estimated rate of 1.7 stillbirths per 1000 births. A multivariable logistic regression model will be developed in line with current TransparentReporting of a multivariable prediction model forIndividualPrognosis orDiagnosis (TRIPOD) guidelines to estimate the gestation-specific probability of stillbirth with prediction intervals. Candidate predictors were identified from systematic reviews and clinical consultation and will be described through univariable regression analysis. To generate a final model, elimination by backward stepwise multivariable logistic regression will be performed. The model will be internally validated using bootstrapping with 1000 repetitions and externally validated using a temporally unique dataset. Overall model performance will be assessed with R2, calibration, and discrimination. Calibration will be reported using a calibration plot with 95% confidence intervals (α = 0.05). Discrimination will be measured by the C-statistic and area underneath the receiver-operator curves. Clinical usefulness will be reported as positive and negative predictive values, and a decision curve analysis will be considered. Discussion A robust method to predict a pregnant woman’s individualized risk of late-pregnancy stillbirth is needed to inform timely, appropriate care to reduce stillbirth. Among existing prediction models designed for obstetric use, few have been subject to internal and external validation and many fail to meet recommended reporting standards. In developing a risk prediction model for late-gestation stillbirth with both providers and pregnant women in mind, we endeavor to develop a validated model for clinical use in Australia that meets current reporting standards.

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