Predicting the rheological flow of fresh self-consolidating concrete mixed with limestone powder for slump, V-funnel, L-box and Orimet models using artificial intelligence techniques

Abstract

In this paper, selected materials that influence the viscosity of the self-consolidating concrete (SCC) are introduced like the Limestone Powder (LSP), the High Range Water Reducing Admixture (HRWRA), which reduce the interparticle force between concrete constituents like the aggregates, and other superplasticizers. Moreover, in serious attempts to design the SCC for different infrastructure requirements, there have been repeated laboratory visits, which need to be reduced. In this research paper, the artificial intelligence (AI) methods: Artificial Neural Network (ANN), Evolutionary Polynomial Regression (EPR), and Genetic programming (GP) have been deployed to predict the slump flow (SF), V-funnel flow time (VFFT), L-box ratio (LBR) or passing ratio, and Orimet flow time (OFT) of LSP-admixed SCC. The independent variables of the predictive model were cement, LSP, water, water-binder ratio, HRWRA, sand, and coarse aggregates of 4/8 mm and 8/16 mm sizes. The flow tests were conducted after 5 minutes of waiting time after mixing. The model results showed ANN with superior intelligent learning ability over previous models in terms of overall performance.

Numerical modeling of the funnel multiphysical flow of fresh self-compacting concrete considering proportionate heterogeneity of aggregates

“…The multiphysical flow of fresh self-compacting concrete (SCC) takes into account the proportionate heterogeneity of fine and coarse aggregates 1 . SCC is a specialized type of concrete that is highly workable and able to flow and fill complex forms under its own weight without the need for mechanical consolidation 2 . When evaluating the funnel flowability of fresh self-compacting concrete (SCC) while considering the heterogeneity of aggregates, you may need to account for the variation in particle sizes and shapes within the mixture 1 .…”

“…When evaluating the funnel flowability of fresh self-compacting concrete (SCC) while considering the heterogeneity of aggregates, you may need to account for the variation in particle sizes and shapes within the mixture 1 . Approaches that can help incorporate aggregate heterogeneity into the V-funnel flowability assessment include 1 : (a) Aggregate characterization: by characterizing the aggregates in the SCC mixture 2,3 . Obtain information about the particle size distribution, shape, and surface texture of the aggregates.…”

“…Adjust the proportion of aggregates and other constituents (cement, water, admixtures) to achieve the desired flowability and workability [5][6][7] . (d) Numerical simulation: Utilize numerical simulation techniques, such as the Discrete Element Method (DEM), to simulate the flow behavior of SCC in the V-funnel [2][3][4][5][6][7][8] . DEM allows for the individual representation of aggregates as discrete particles, accounting for their size, shape, and interactions 6 .…”

“…Also, note that the coupling process between ANSYS and SPH may require custom development of data exchange interfaces and scripts, as well as a deep understanding of both software packages 18 . Additionally, the numerical parameters and modeling assumptions in both ANSYS and SPH need to be carefully selected and validated to ensure accurate and reliable results 2,8,19 . Also, the specific implementation details may vary depending on the versions of ANSYS and the SPH software you are using, as well as the specific requirements of your simulation 20 .…”

Influence of the ANN Hyperparameters on the Forecast Accuracy of RAC’s Compressive Strength

“…Specific areas of structures and materials have been used ANN to evaluate the applicability of machine learning in civil engineering, with the majority of the studies in this field aiming to predict physical, chemical, or mechanical properties, especially in concrete. Examples include the prediction of compressive strength [26,28,29,[34][35][36][37], the elastic modulus [38][39][40][41][42][43], the determination of the workability of concrete and its consistency in the fresh state [44][45][46][47], the mapping of composite degradation mechanisms [48][49][50][51][52][53][54], and the development of a concrete-mix design model [55][56][57][58].…”