Suctions, fracture energy, and plastic cracking of cement mortar and concrete

Plastic cracking of cement mortar and concrete is primarily attributable to desiccation by evaporation from unprotected surfaces. This causes high suctions (negative pressures) to develop in the pore water adjacent to these surfaces. Dissolved salts in the pore water can also contribute significantly to suctions. Quantitative expressions are available for all of the components of the total suction. The development of suctions over time is illustrated by the results of desiccation tests conducted on cement mortars, supplemented by data from the literature. It is shown that ambient conditions conducive to plastic cracking can arise almost anywhere, but that the extremely high suctions that develop in mature cement mortar and concrete do not imply that compression failures should occur A high value of fracture energy is derived from data from the desiccation tests that implies that plastic cracking is characterized by a significant zone of plastic straining or microcracking.

Analytical solutions of linear finite- and small-strain one-dimensional consolidation

Analytical solutions are presented for linear finite-strain one-dimensional consolidation of initially unconsolidated soil layers with surcharge loading for both one- and two-way drainage. These solutions complement earlier solutions for initially unconsolidated soil layers without surcharge and initially normally consolidated soil layers with surcharge. Small-strain solutions for the consolidation of initially unconsolidated soil layers with surcharge loading are also presented, and the relationship between the earlier solutions for initially unconsolidated soil without surcharge and the corresponding small-strain solutions, which was not addressed in the earlier work, is clarified. The new solutions for initially unconsolidated soil with surcharge loading can be applied to the analysis of low stress consolidation tests and to the partial validation of numerical solutions of non-linear finite-strain consolidation. They also clarify a formerly perplexing aspect of finite-strain solution charts first noted in numerical solutions. Copyright (C) 2004 John Wiley Sons, Ltd.

Crack depths in desiccating plastic concrete

The depths of cracks in desiccating plastic concrete are estimated by considering the effects of the suction (negative pore pressure) associated with desiccation and applying five failure models derived from fracture, theories combined with theories drawn from geotechnical engineering under the assumption that plastic concrete is a frictional particulate material. The estimated crack depths vary with the depth of desiccation, the suction profile, and a small number of material parameters that depend on the model adopted and are comparatively easy to estimate accurately. Four of the models predict excessively large crack depths. The fifth, however, predicts shallower crack depths that increase with the age of the concrete and are consistent with those of analogous desiccation cracks in coal mine tailings. It thus offers a relatively robust method of estimating the depth of desiccation cracks. Confirmation of this with data for plastic concrete is clearly desirable but not possible at present.

A review of ACI recommendations for prevention of plastic cracking

The ACI recommendations for the prevention of cracking of plastic concrete attempt to eliminate such cracking by ensuring that the rate of evaporation from unprotected concrete surfaces does not exceed the estimated rate of bleed water production. The current recommendations, however do not account for the large scatter of the underlying experimental evaporation data nor the effect of altitude on evaporation rate. Ignoring the scatter of the evaporation data frequently leads to an unacceptably high probability that the evaporation rate will exceed the bleed rate. Ignoring the effect of altitude leads to similar high probabilities, but in only a comparatively small number of cases. Simple modifications of the ACI recommendations are suggested that can account for both effects. However; insufficient data on the variability of bleed rates are currently available to allow the scatter of the evaporation data to be accounted for completely.

Use of a probabilistic neural network to reduce costs of selecting construction rock

Rocks used as construction aggregate in temperate climates deteriorate to differing degrees because of repeated freezing and thawing. The magnitude of the deterioration depends on the rock's properties. Aggregate, including crushed carbonate rock, is required to have minimum geotechnical qualities before it can be used in asphalt and concrete. In order to reduce chances of premature and expensive repairs, extensive freeze-thaw tests are conducted on potential construction rocks. These tests typically involve 300 freeze-thaw cycles and can take four to five months to complete. Less time consuming tests that (1) predict durability as well as the extended freeze-thaw test or that (2) reduce the number of rocks subject to the extended test, could save considerable amounts of money. Here we use a probabilistic neural network to try and predict durability as determined by the freeze-thaw test using four rock properties measured on 843 limestone samples from the Kansas Department of Transportation. Modified freeze-thaw tests and less time consuming specific gravity (dry), specific gravity (saturated), and modified absorption tests were conducted on each sample. Durability factors of 95 or more as determined from the extensive freeze-thaw tests are viewed as acceptable—rocks with values below 95 are rejected. If only the modified freeze-thaw test is used to predict which rocks are acceptable, about 45% are misclassified. When 421 randomly selected samples and all four standardized and scaled variables were used to train aprobabilistic neural network, the rate of misclassification of 422 independent validation samples dropped to 28%. The network was trained so that each class (group) and each variable had its own coefficient (sigma). In an attempt to reduce errors further, an additional class was added to the training data to predict durability values greater than 84 and less than 98, resulting in only 11% of the samples misclassified. About 43% of the test data was classed by the neural net into the middle group—these rocks should be subject to full freeze-thaw tests. Thus, use of the probabilistic neural network would meanthat the extended test would only need be applied to 43% of the samples, and 11% of the rocks classed as acceptable would fail early.