Lightweight concrete (LWC) finds wide-ranging applications in the construction industry due to its reduced dead load, good fire resistance, and low thermal and acoustic conductivity. Lightweight geopolymer concrete (LWGC) is an emerging type of concrete that is garnering attention in the construction industry for its sustainable and eco-friendly properties. LWGC is produced by using geopolymer binders instead of cement, thereby reducing the carbon footprint associated with conventional concrete production. However, the absence of standard codes for geopolymer concrete restricts its widespread application. To address this limitation, an investigation focused on developing a new mixture design for LWGC by modifying the existing ACI 211.2-98 provisions has been carried out. In this study, crucial parameters of LWGC such as alkaline/binder ratio, molarity, silicate/hydroxide ratio, and curing temperature were established using machine learning techniques. As a result, a simple and efficient method for determining the mix proportions for LWGC has been proposed.

In this study, low-carbon ultra-high-performance concrete (UHPC) was designed by adding fly ash-based mineral admixtures (SD-FA). The improved Andreasen & Andersen model was used to obtain SD-FA, which was then used to replace part of UHPC cement, to achieve the effect of low-carbon emission reduction. The effects of the composition and dosage of cement-based materials, the water-cement ratio, the composition of sand, the steel fiber content, and the lime-sand ratio on the properties of UHPC were studied, and the design of the batches was optimized. On this basis, the performance changes were analyzed at the micro level. The results show that when the 1~3 grade fly ash content after screening treatment is quantitative, the densest stacking is theoretically reached. The SD-FA optimized design improves the bulk density of UHPC and realizes the dense microstructure of UHPC. Under the optimal mixing ratio, its processability is guaranteed and the mechanical properties are enhanced.

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.

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.

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.