Accurately predicting the compressive strength of concrete is crucial in ‎various fields, including construction and engineering. This research paper proposes two mathematical models based on non-linear regression and Artificial Neural Networks (ANN) to predict the compressive strength of concrete accurately based on Ultrasonic Pulse Velocity (UPV) measurements. This paper outlines the proposed models’ formulation, calibration, evaluation, and validation. An experimental program was designed to calibrate and evaluate the models, and the analysis of the results reveals the robust fit of the proposed models to the experimental data. Both models exhibit exceptional accuracy, effectively predicting compressive strength values. The ANN and non-linear regression models attained high coefficients of determination of 0.993 and 0.992, respectively, demonstrating their reliability. Additionally, the standard errors of the ANN and non-linear regression models are 2.41 MPa and 2.52 MPa, respectively. Practical applications of these models extend to concrete characterization, enabling efficient quality control and structural integrity assessment.

High cement content is often found in concrete mix designs to achieve the unique fresh-state behavior requirements of 3D Printable Concrete (3DPC), i.e., to ensure rapid stiffening of an extruded layer without collapsing under the stress applied by the following layers. Some materials with high water absorption, such as recycled concrete aggregates, have been incorporated in concrete mix designs to minimize environmental impact, nevertheless, the fine powder fraction that remains from the recycled aggregate processing still poses a challenge. In the case of 3DCP, few studies are available regarding mix designs using Recycled Concrete Powder (RCP) for 3D printing. In this context, this study presents the use of RCP as a filler to produce a printable mixture with low cement content. An RCP with 50 μm average particle size was obtained as a by-product from Recycled Concrete Aggregate production. Portland cement pastes were produced with 0%, 10%, 20%, 30%, 40% and 50% of cement mass replacement by RCP to evaluate its effects on the hydration reaction, rheology, and compressive strength. It was found that the studied RCP replacement was not detrimental for the hydration reaction of Portland cement during the initial hours, and at the same time it was capable of modifying the rheological parameters of the paste proportionally to the packing density of its solid fraction. The obtained results indicated the viability of 3DCP with up to 50% cement replacement by RCP. It was concluded that RCP presents good potential for decreasing the cement consumption of 3DPC, which in turn could decrease its associated environmental impact while providing a destination for a by-product from recycled concrete aggregate production.

Statistical process control (SPC) procedures are proposed to improve the production efficiency of precast concrete tunnel segments. Quality control test results of more than 1000 ASTM C1609/C1609M beam specimens were analyzed. These specimens were collected over 18 months from the fiber-reinforced concrete (FRC) used for the production of precast tunnel segments of a major wastewater tunnel project in the Northeast United States. The Anderson-Darling (AD) test for the overall distribution indicated that the data are best described by a normal distribution. The initial residual strength parameter for the FRC mixture, f D 600, is the most representative parameter of the post-crack region. The lower 95% confidence interval (CI) values for 28-day flexural strength parameters of f1, f D 600, and f D 300 exceeded the design strengths and hence validated the strength acceptability criteria set at 3.7 MPa (540 psi). A combination of run chart, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) control charts successfully identified the out-of-control mean values of flexural strengths. These methods identify the periods corresponding to incapable manufacturing processes that should be investigated to move the processes back into control. This approach successfully identified the capable or incapable processes. The study also included the Bootstrap Method to analyze standard error in the test data and its reliability to determine the sample size.

The early-age material parameters of three-dimensional (3-D)-printable concrete defined under the umbrella of printability, namely, pumpability, extrudability, buildability, and the “printability window/open time,” are subjective measures. The need to correlate and successively substitute these subjective measures with objective and accepted material properties, such as tensile strength, shear strength, and compressive strength, is paramount. This study validates new testing methodologies to quantify the tensile and shear strengths of printable fiber-reinforced concretes still in their fresh state. A tailored mixture with high sulfoaluminate cement and nonstructural basalt fibers has been assumed as a reference. The relation between the previously mentioned parameters and rheological parameters, such as yield strength obtained through International Center for Aggregates Research (ICAR) rheometer tests, is also explored. Furthermore, in an attempt to pave the way and contribute toward a better understanding of the mechanical properties of 3-D-printed concrete, to be further transferred into design procedures, a comparative study analyzing the work of fracture per unit crack width in three-point bending has been performed on printed and companion nominally identical monolithically cast specimens, investigating the effects of printing directions, position in the printed circuit, and specimen slenderness (length to depth) ratio.

This experimental study evaluated the correlation between measured concrete expansion from a modified version of the miniature concrete prism test (MCPT) with the concentration of chemical markers leached from the prisms into an alkaline soak solution. Fifteen concrete mixture designs were tested for expansion and soak solution concentrations over time. The changes in expansion and soak solution concentrations were found to correlate well even with variations in alkali loading and substitution of cement with Class F fly ash. A model was developed to estimate the expansion potential of concrete based on an expansion reactivity index (ERI) that incorporated the concentrations of silicon, sulfate, calcium, and aluminum. The relationship between ERI and expansion was then used to identify potentially expansive concrete mixtures using the ERI of cores taken from a structure exhibiting potential alkalisilica reaction (ASR) expansion and concrete cylinders matching the mixture designs of the MCPT specimens.