Statistical filtering of useful concrete creep data from imperfect laboratory tests

Abstract

The reporting and evaluation of creep tests of concrete is complicated by the fact that creep is significant even for the shortest observable load durations. Compared to the strain after 0.1 s load duration, the strain at 2 h duration is typically 53% greater. Most experimenters have for decades been unaware of this fact. Consequently, the reported creep curves require correction by a time shift, which ranges from 0 to 2 h. This further implies a vertical shift of entire creep curve, important for all times up to structure lifetime. To filter out the errors, it is argued that, within an initial period during which the advance of hydration is negligible, which is normally about 1 day, the initial basic creep must follow a power law of the time. Creep test data from the literature are used to prove it. Corrections by time and deformation shifts are determined by minimization of the sum of squared deviations of the power law from the creep test data. For a fixed exponent n and time shift s, the optimization is reduced to linear regressions of two kinds, depending on whether the data are given in terms of either the compliance function or the creep coefficient. For both, the linear regression parameters depend nonlinearly on the chosen values of n and s. To avoid nonlinear optimization, which need not converge to the correct result, a set of many discrete values of n and s within their realistic ranges is selected and the (n, s) combination minimizing the objective function is obtained by a search. Enforcing a power law form of the initial creep curve is found to lead to better data fits. The optimum exponent n for the entire database is around 0.3, applicable to the time period cca (10 s, 1 day). After that, the exponent transits to about 0.1, and prior to that it is about 0.08. After filtering out the errors, the corrected database will allow better calibration of the general creep prediction model such as B3 or B4.

Prediction of autogenous shrinkage in concrete from material composition or strength calibrated by a large database, as update to model B4

“…Because of the unfortunate ingrained habit of engineers to plot the test results in the linear time scale [9], it has been widely believed that the autogenous shrinkage reaches an upper bound within a few months. But data plots in the logarithmic time scale reveal that the hydration, and thus also the autogenous shrinkage, continues for decades, with no bound in sight.…”

Enhancing Concrete Creep Prediction With Deep Learning: A Soft-Sorted One-Dimensional CNN Approach

“…The rate of creep is usually high in the early stages of loading and decreases with time, but it does not increase again after that [7]. While some recovery occurs when the sustained load is removed, the concrete usually does not return to its original state, and its elastic recovery and creep recovery effects are less than the deformation induced under a load [8,9].…”

Moisture Diffusion in Unsaturated Self-Desiccating Concrete with Humidity-Dependent Permeability and Nonlinear Sorption Isotherm

“…However, they do not suffice for predicting the autogenous shrinkage. The reason is that the autogenous shrinkage is highly sensitive to an initial decrease of relative humidity, and a small error in predicting the self-desiccation and hydration can cause a large error in predicting the autogenous shrinkage Rasoolinejad et al 2018). To avoid major errors, the complete hydration model must be used for that purpose.…”