A new formula to estimate final temperature rise of concrete considering ultimate hydration based on equivalent age

© 2017 When simulating the temperature field of concrete, the conventional adiabatic temperature rise models, which only take the age of concrete into account, can lead to a significant deviation (the maximum relative error nearly 73%) from predicted values to measured values under extreme conditions. To solve this problem, a new prediction formula is presented in this paper for estimating the final temperature rise of concrete, by considering ultimate hydration based on the equivalent age. The formula is developed on the basis of measured data obtained in some real construction cases during the recent years. It essentially reveals the ultimate degree of hydration for concrete with a variation in the placing temperature at the construction site. The degree of hydration at the construction site is not as accurate as measured with an adiabatic calorimeter. Also, the measured data shows that the ultimate degree of hydration of concrete under the non-adiabatic condition is related to its placing temperature. A logarithmic function is proposed to approximate this relationship. The equivalent age is developed to consider the effects of both the age of concrete and its temperature. The comparison shows that the proposed combination of equivalent age and the new formula can reduce the maximum relative error substantially from 73% to 15% than those algorithms which do not consider equivalent age or our proposed formula

Prediction of Seepage Pressure Based on Memory Cells and Significance Analysis of Influencing Factors

Seepage analysis is always a concern in dam safety and stability research. The prediction and analysis of seepage pressure monitoring data is an effective way to ensure the safety and stability of dam seepage. With the timeliness of a change in a monitoring value and lag due to external influences, a RS-LSTM model written in Python is developed in this paper which combines rough set theory (RS) and the long- and short-term memory network model (LSTM). The model proposed calculates the prediction score of the seepage pressure of a dam experiencing multiple effects by preordering factor importance values to eliminate the interference of redundant factors. A case study shows that the water level, rainfall, temperature, and duration are all factors that affect the seepage pressure, and their importance values decrease successively. Thus, the seepage pressure of a dam can be predicted with a determination coefficient R2 of 0.96. Compared with the recurrent neural network (RNN) model and BP neural network model, the training time of the RS-LSTM model proposed is 6.37 s, and the operation efficiency is 41% and 59% higher than that of the RNN and BP models, respectively. The mean relative error is also 3.00%, which is 50% lower than that of the RNN model and 31% lower than that of the BP model. Based on these results, this model has the advantages of fast computation speed and high accuracy in prediction

Statistical Law and Predictive Analysis of Compressive Strength of Cemented Sand and Gravel

A data set of cemented sand and gravel (CSG) mix proportion and 28-day compressive strength was established, with outliers determined and removed based on the Boxplot. Then, the distribution law of compressive strength of CSG was analyzed using the skewness kurtosis and single-sample Kolmogorov-Smirnov tests. And with the help of Python software, a model based on Back Propagation neural network was built to predict the compressive strength of CSG according to its mix proportion. The results showed that the compressive strength follows the normal distribution law, the expected value and variance were 5.471 MPa and 3.962 MPa respectively, and the average relative error was 7.16%, indicating the predictability of compressive strength of CSG and its correlation with the mix proportion