Eco-efficient concrete, optimized by Alfred’s particle packing model, with partial replacement of Portland cement by stone powder

The partial replacement of clinker by complementary cementitious materials can significantly contribute to the reduction of carbon emissions in the production of concrete. Another alternative to reduce these emissions is to increase the efficiency of the concrete, achieving higher compressive strength with lower consumption of cement. Particle packing models are efficient tools to optimize the composition of the matrix and contribute to the production of more eco-efficient concretes. In this context, the objective of the present study is evaluating the production of concretes with partial replacement of cement by stone powder, optimized by Alfred’s particle packing model, seeking to reduce cement consumption and CO2 emissions per MPa of compressive strength. The replacement content of cement by stone powder was 20% by mass (equivalent to 22.4% by volume). Concretes were produced with different distribution factor (q) - 0.37; 0.21; 0.45 - to verify the influence of fines on the flow between particles and on the efficiency of the produced concrete. The analyses were carried out in terms of properties in the fresh state, hardened state, and sustainability parameters (cement consumptions and CO2 emissions). The application of the proposed method resulted in a higher compressive strength than the expected for the water/cement ratio used (0.5). The most efficient concrete reached the compressive strength of 68 MPa with 240 kg/m3 of cement, which represents 3.5 kg of cement/m3/MPa and 3.1 kg of CO2/m3/MPa, a value below the references found in the literature for conventional concretes. Therefore, the proposed method allows to produce more eco-efficient concrete, contributing to the use of waste and reducing CO2 emissions.

Particle Generation to Minimize the Computing Time of the Discrete Element Method for Particle Packing Simulation

There are computation time constraints caused by the number and size of particles in the powder packing simulation using DEM. In this paper, newly suggested packing model transforms a general packing sequence –particle generation, stack, and compression – into particle generation and packing by growing particles. To verify the new packing model, it was compared using three contact models widely used in DEM, in terms of Radial Distribution Function, porosity, and Coordination Number. As a result, contact between particles showed a similar trend, and the pore distribution was also similar. Using the new packing model can reduce simulation time by 400% compared to the normal packing model without any other coarse graining methods. This model has only been applied to particle packing simulations in this paper, but it can be expanded to other simulations with complex domain based on DEM.

Unified Packing Model for Improved Prediction of Porosity and Hydraulic Conductivity of Binary Mixed Soils

Binary mixed soils, containing coarse sand particles mixed with variable content of fines (fine sand, silt, or clay) are important for several environmental and engineering applications. The packing state (or porosity) of such sand-fines mixtures controls several important physical properties such as hydraulic conductivity. Therefore, developing an analytical packing model to predict porosity of binary mixed soils, based on properties of pure unmixed sand and fines (endmembers), can contribute to predicting hydraulic conductivity for the mixtures without the need for extensive laboratory measurements. Toward this goal, this study presents a unified packing model for the purpose of predicting the porosity and hydraulic conductivity of binary mixed soils as function of fines fraction. The current model modifies an existing packing model developed for coarse binary mixed soils to achieve three main improvements: (1) being inclusive of wide range of binary mixed soils covering the whole range particle sizes, (2) incorporating the impact of cohesive packing behavior of the fines on binary mixture porosity, and (3) accounting for the impact of clay swelling. The presented model is the first of its kind incorporating the combined impact of all three factors: particle size ratio, fines cohesive packing and swelling, on binary mixtures porosity. The predictions of the modified model are validated using experimental published data for the porosity of sand-fines mixtures from 24 different studies. The model shows significant improvement in predicting porosity compared to existing packing models that frequently underestimate the porosity. By using the predicted porosity as an input in Kozeny–Carman formulation, the absolute mean error in predicting hydraulic conductivity, as function of fines fraction for 16 different binary mixed soils, is reduced by 50% when compared to the use of the previous packing model. The current model provides insights about the endmembers properties (porosity, hydraulic conductivity, and grain size) and fines content required to achieve a certain target desirable porosity and hydraulic conductivity of the mixed soils. This can assist the optimization of soil mixing design for various applications.