Particle Size Effect of Oyster Shell on Mortar: Experimental Investigation and Modeling

In order to solve the problem of lack of natural river sand, crushed waste oyster shells (WOS) were used to replace river sand. By replacing 20% river sand, WOS mortar with different particle sizes of WOS were made for the experiment. Through experimental observation, the initial slump and slump flow loss rate were studied. The effects of different particle sizes and curing times on the compressive strength, flexural strength, static elastic modulus, and dry shrinkage of WOS mortar were analyzed. The relationship formulas between the compressive strength, flexural strength, particle size, and curing age were proposed. The results showed that the setting time and slump flow decreased with a decrease in the particle size of WOS. It was also found that the mortar with fine crushed WOS had high compressive strength, flexural strength, and static elastic modulus at both early and long-term curing age. A formula was proposed to describe the development of the compressive strength with the particle size of WOS and curing time, and the relations among these mechanical properties were discussed. Furthermore, drying shrinkage increased when WOS was used and could not satisfy the standard requirement of 0.075%. In contrast, the addition of fine WOS and double-dose sulfonated naphthalene-formaldehyde superplasticizer (SNF SP) reduced the shrinkage rate of the mortar by 8.35% and provided better workability and mechanical properties for mortar.

Mechanical Properties and Uniaxial Compression Stress-Strain Relation of Recycled Coarse Aggregate Concrete after Carbonation

The application of recycled coarse aggregate (RCA) made from waste concrete to replace natural coarse aggregate (NCA) in concrete structures can essentially reduce the excessive consumption of natural resources and environmental pollution. Similar to normal concrete structures, recycled concrete structures would also suffer from the damage of carbonation, which leads to the deterioration of durability and the reduction of service life. This paper presents the experimental results of the cubic compressive strength, the static elastic modulus and the stress-strain relation of recycled coarse aggregate concrete (RAC) after carbonation. The results show that the cubic compressive strength and the static elastic modulus of carbonated RAC gradually increased with the carbonation depth. The uncarbonated and fully carbonated RAC show smaller static elastic modulus than natural aggregate concrete (NAC). As the carbonation depth increased, the peak stress increased, while the peak strain decreased and the descending part of the curves gradually became steeper. As the content of RCA became larger, the peak stress decreased, while the peak strain increased and the descending part of the curves gradually became steeper. An equation for stress-strain curves of RAC after carbonation was proposed, and it was in good agreement with the test results.

Prediction of Static Modulus and Compressive Strength of Concrete from Dynamic Modulus Associated with Wave Velocity and Resonance Frequency Using Machine Learning Techniques

The static elastic modulus (Ec) and compressive strength (fc) are critical properties of concrete. When determining Ec and fc, concrete cores are collected and subjected to destructive tests. However, destructive tests require certain test permissions and large sample sizes. Hence, it is preferable to predict Ec using the dynamic elastic modulus (Ed), through nondestructive evaluations. A resonance frequency test performed according to ASTM C215-14 and a pressure wave (P-wave) measurement conducted according to ASTM C597M-16 are typically used to determine Ed. Recently, developments in transducers have enabled the measurement of a shear wave (S-wave) velocities in concrete. Although various equations have been proposed for estimating Ec and fc from Ed, their results deviate from experimental values. Thus, it is necessary to obtain a reliable Ed value for accurately predicting Ec and fc. In this study, Ed values were experimentally obtained from P-wave and S-wave velocities in the longitudinal and transverse modes; Ec and fc values were predicted using these Ed values through four machine learning (ML) methods: support vector machine, artificial neural networks, ensembles, and linear regression. Using ML, the prediction accuracy of Ec and fc was improved by 2.5–5% and 7–9%, respectively, compared with the accuracy obtained using classical or normal-regression equations. By combining ML methods, the accuracy of the predicted Ec and fc was improved by 0.5% and 1.5%, respectively, compared with the optimal single variable results.