Search Results (4)

Reducing cement consumption in mortar systems is essential for lowering the environmental impact of cement-based materials. Conventional mix design approaches rely mainly on particle size distribution and fineness modulus, which do not fully capture the effects of aggregate packing, morphology, and petrographic composition on paste demand and mechanical performance. Fourteen fine aggregates of distinct geological origins were experimentally characterized in terms of physical and petrographic properties. A dataset of 211 mortar mixtures, yielding 633 transverse-strength observations, was used to train a Random Forest Regressor (RFR) model for strength prediction. The model achieved $R^2=0.762$ (RMSE = 0.223 kN; MAE = 0.165 kN), demonstrating its reliability as a surrogate screening tool. This study presents a hybrid framework that integrates particle packing theory with machine learning to optimize fine aggregate blends. By introducing a Paste Demand Index (PDI)—combining normalized uncompacted void content, surface texture, and shape—the framework enables the identification of mixtures that minimize paste demand while maintaining mechanical performance under strength constraints. Results confirm that the proposed PDI and strength-based filtering are robust, offering a physically grounded decision-support methodology for narrowing the design space. Ultimately, this approach provides an efficient strategy for resource optimization, effectively bridging the gap between computational screening and laboratory validation in cement-reduction initiatives driven by the cement-based tile manufacturing industry. Full article

The increasing demand for sustainable construction materials has motivated the optimization of mortar mix designs to reduce cement consumption and its environmental impact while maintaining adequate mechanical performance. This study develops a machine learning (ML) model for optimizing mortar mixtures used in concrete roofing tiles by integrating aggregate particle packing techniques with non-linear regression algorithms, using an industry-grade dataset generated in the Central Laboratory of Wienerberger Ltd. Unlike most previous studies, which mainly focus on compressive strength, this research targets the transverse strength of industrial roof tile mortar. The proposed approach combines Tarantula Curve gradation limits, experimentally derived packing density ($\eta$), and ML regression within a unified and application-oriented workflow, representing a research direction rarely explored in the literature for optimizing concrete mix transverse strength. Fine concrete aggregates were characterized through a sand sieve analysis and subsequently adjusted according to the Tarantula Curve method to optimize packing density and minimize void content. Physical properties of cements and fine aggregates were assessed, and granulometric mixtures were evaluated using computational methods to calculate fineness modulus summation (FMS) and packing density. Mortar samples were tested for transverse strength at 1, 7, and 28 days using a three-point bending test, generating a robust dataset for modeling training. Three ML models—Random Forest Regressor (RFR), XG-Boost Regressor (XGBR), and Support Vector Regressor (SVR)—were evaluated, confirming their ability to capture nonlinear relationships between mix parameters and transverse strength. The analysis of input variables, which consistently ranked as the highest contributors according to impurity-based and permutation-based importance metrics, revealed that the duration of curing, density, and the summation of the fineness modulus significantly influenced the estimated transverse strength derived from the models. The integration of particle size distribution optimization and ML demonstrates a viable pathway for reducing cement content, lowering costs, and achieving sustainable mortar mix designs in the tile manufacturing industry. Full article

Lightweight aggregate concrete is known for its potential to decrease overall building load and cost. Aero Aggregates’ Aerolite is a foamed glass aggregate (FGA) available in seven different sizes which has the potential to replace normal weight aggregates to create lightweight concrete. This research analyzes the feasibility of using FGAs in optimized concrete mix designs and employing those designs in a full-scale building. Nine different mix designs were created using optimization methods, including the Tarantula Curve and 0.45 power chart, to determine the ideal aggregate proportions. All mixes were cast in 0.1 m diameter, 0.2 m tall cylinders and tested after 7 and 28 days to determine unit weight (density), compressive strength, and modulus of elasticity. After testing, the optimal design was identified as 65% coarse and 15% fine aggregates to be replaced with FGAs because it gave the best unit weight and compressive strength for structural lightweight concrete. The optimal concrete mix design was used to create an example building model in RAM Structural Systems to prove that FGA concrete can reduce cost, materials required, and carbon emissions on a larger scale. Full article

The durability of concrete is compromised by early-age cracking, which provides a pathway for harmful ions and water to penetrate the material. Early-age cracking, however, is most commonly caused by concrete shrinkage. This study investigates strategies for minimizing the shrinkage of concrete by optimizing aggregate gradation via the Tarantula Curve, reducing cement content, and incorporating lightweight fine aggregates (LWFA) as internal curing agents. The commercially adopted mix design was used as a reference, with the cementitious materials-to-aggregate (C/A) ratio reduced from 0.21 (reference) to 0.15 (proposed), incorporating 0–15% LWFA replacement levels. Workability (ASTM C143), mechanical performance (ASTM C39, ASTM C78), durability (AASHTO TP 119-21), and dimensional stability (ASTM C157) were evaluated through ASTM standard tests. The results highlight that optimizing the C/A ratio cannot only improve both compressive and flexural strengths in regular concrete but also mitigate the total shrinkage by 12.68%. The introduction of LWFA further reduced shrinkage, achieving a 19.72% shrinkage reduction compared to regular concrete. In addition, the sustainability of the developed mix designs is enhanced by the reduced cement usage. A Life Cycle Assessment (LCA) based on the TRACI method confirmed the sustainability advantages of cement reduction. The optimized mix designs resulted in a 30% decrease in CO₂ emissions, emphasizing the role of mix design in developing environmentally responsible concrete. Overall, lowering the cement amount and the addition of LWFA provide an optimal combination of shrinkage control, strength retention, and sustainability for applications. Full article