Mohamed Sahraoui

Self-compacting concrete (SCC) performance depends on the complex interactions between water availability, chemical dispersion, and powder characteristics. This study evaluates the rheological and mechanical behavior of high-powder SCC incorporating limestone filler as an additional powder constituent rather than as a cement replacement. A response surface methodology (RSM) experimental design comprising 28 mixtures was developed to evaluate the effects of the water-to-powder ratio (W/P), filler-to-cement ratio (Lf/C), superplasticizer dosage (SP%), and limestone filler particle size (DLf). Fresh-state properties were assessed through slump-flow, V-funnel, L-box, and segregation-resistance tests, while apparent rheological coefficients of the modified Bingham model were derived from torque–rotational speed measurements. The results showed that W/P was the dominant factor controlling the fresh-state behavior, followed by SP%; Lf/C mainly controlled compressive strength, whereas the effects of DLf, evaluated only for the filler-containing mixtures, were limited to slump flow and sieve segregation, with no significant effect on compressive strength when all six half-prisms of each mixture were used. The central formulation (W/P = 0.40, Lf/C = 0.35, SP = 0.9%, and DLf = 45 μm) is retained only as a reference composition within the studied domain: its four replicates showed consistent filling ability, but only one of them met both an L-box ratio of at least 0.80 and a sieve-segregation ratio of at most 15%. In a strength model that describes trends but has no predictive ability, limestone filler content had a significant positive effect on compressive strength that decreased at higher W/P, and SEM observations showed a more continuous fracture morphology; because the paste volume and the cement content per unit volume changed together with Lf/C, this effect cannot be attributed to a specific mechanism, and the SEM and XRD observations do not establish packing or nucleation effects. Overall, within the investigated domain, the fresh-state behavior of high-powder SCC depended mainly on water availability and admixture dosage and their interaction, whereas the effects of filler content and grade were smaller and partly confounded with the associated changes in paste composition.

Introduction: The rheological behaviour of Self-Compacting Mortar (SCM) is very sensitive to the addition of various additives. This paper examines the impact of recycled Polyethylene Terephthalate (PET) fibres (obtained from plastic brooms) on the fresh and rheological properties of Self-Compacting Mortar (SCM). Methods: The fibre SCM mix was prepared with four different volume fractions (0.5% to 2.0%) of wavy and linear PET fibres, both having the same length of 20 ± 2 mm. The workability was assessed using the slump-flow and V-funnel tests, and rheological properties were determined using an Anton Paar rotational rheometer. Results: Flowability decreased as the fibre volume increased, with a maximum decrease of around 8% at 2% fibre volume. An increase in the V-funnel flow time of up to 1.5 seconds was observed, corresponding to an increased resistance to flow between the minimum and maximum fibre content. The rheometer results showed that all mortars followed the Bingham model. The fibres showed little influence on plastic viscosity and yield stress at low fibre dosages, but at high dosages (1.5% and above) the parameters were significantly increased. The wavy fibre showed lower viscosity and improved adaptability to the granular structure of the mortar than the linear fibre. Discussion: According to rheometer data, all mixtures complied with the Bingham model; low dosages showed that fibres had no effect on plastic viscosity or yield stress. However, beyond 1.5%, there were noticeable increases, and wavy fibres were shown to be less viscous and better able to adapt to the granular structure than linear fibres. Conclusion: In conclusion, the use of PET Fibre-reinforced Self-Compacting Mortar (PETF-SCM) exhibits satisfactory fresh workability, rheological properties, and good sustainability, making it a potential candidate for eco-efficient concrete.

The increasing need for efficient and sustainable construction materials has prompted the investigation of local and recycled resources to improve the characteristics of poor soils. This research aims to enhance dune sand (DS)—a plentiful yet geotechnically weak material—by integrating rubber crumb (RC) and brick powder (BP), with the objectives of soil stability and waste valorization. Experimental formulations were created with RC at concentrations of 10%, 30%, and 50%, and BP at concentrations of 1%, 2%, and 3%. A response surface methodology (RSM) and artificial neural network (ANN) techniques were utilized to examine the influences of RC and BP content on three primary responses: maximum dry density (MDD), internal friction angle (ϕ), and cohesion (C). Ideal conditions with 40.7% RC and 3% BP were achieved through optimization utilizing RSM and ANN-GA, greatly enhancing compaction and shear strength for geotechnical applications. According to Life Cycle Assessment (LCA), a high CR content raises energy consumption (E-Energy) and greenhouse gas emissions (E-CO₂), primarily as a result of rubber recycling. However, the reduction in sand mining and the diversion of tyre waste balance these effects. Therefore, the combination of RC with BP is turning out to be a viable and efficient infrastructure solution, especially in dry regions.

Introduction/purpose: Self-compacting mortars possess very good flowability which enables them to consolidate only through the force of their self-weight, not demanding any mechanical vibration. This type of mortars has a significant value in complicated construction and repair works. In this paper, research will be conducted on using brick powder as replacement for cement in SCM with a detailed study of effects on workability, compressive strength, and all other performance. Methods: The proposed research models the relationships of different parameters, brick powder content and fineness, with the resultant properties of mortar using artificial neural networks (ANN) models. Results: The results showed that the addition of brick powder up to 10% of cement as replacement improves mortar workability, giving the slump flow ranging from 306 to 309 mm and the funnel flow time between 4.8 and 5.4 s, while its compressive strength was ranging from 45 to 60 MPa at 28 days. However, at replacement levels higher than 20%, the slump flow was reduced to 285 mm, the time to funnel flow increased to 9 s, and the compressive strength decreased to 35 MPa. Conclusion: The study illustrates brick powder as a promising recycled material for SCM applications not only to reduce environmental impacts but also to improve performance, although optimization of its replacement levels should be taken carefully to balance workability and strength.

This paper addresses the effect of mineral admixtures on fresh and hardened properties of self compacting concrete (SCC). Artificial neural networks (ANN) and simplex lattice design approach was integrated to predict and evaluate the effect of limestone, marble powder, natural pozzolan and slag on rheological and mechanical properties of SCC evaluated by slump flow, L-Box, V-funnel, sieve segregation test and 28 days compressive strength. The modelling results show an acceptable prediction accuracy of SCC behaviour containing mineral admixtures as substitution of cement especially related to the flow time measured with the V-funnel test and mechanical compressive strength at 28 days.

The objective of this investigation is to illustrate the effect of aggregates types and contents on fresh and hardened properties of self-compacting concrete (SCC) considering Algerian experience. Based on experimental data available in the literature, Artificial neural network (ANN) models are established to illustrate the variation of aggregate types and contents (sand and gravel) in binary and ternary contour plots. Modelling results concerning the effect of sand types and proportions in binary and ternary combinations show the beneficial effect of river sand (RS) and crushed sand (CS) on slump flow. The highest L-Box ratio was obtained for mixtures composed of 50% of both RS and CS for binary and ternary mixtures. The increase in CS content enhance static stability, while the increase in RS gives higher compressive strength at 28 days. Concerning the study of aggregate sizes and contents, it was found that the increase of sand content leads to an increase in flowability and a decrease in static stability. An increase in gravel content leads to a decrease in passing ability, while a significant improvement in viscosity, static stability and mechanical strength with an increase in gravel content were observed.

This paper presents research on the use of artificial neural networks (ANNs) to predict fresh and hardened properties of self compacting concrete (SCC) made with Algerian materials. A multi-layer perceptron network with 5 nodes, 12 inputs, and 5 outputs is trained and optimized using a database of 167 mixtures collected from literature. The inputs for the ANN models are ordinary Portland cement (Cm), polycarboxylate ether superplasticizer (Sp), river sand (RS), crushed sand (CS), dune sand (DS), Gravel 3/8 (G1), Gravel 8/15 (G2), Water (W), Limestone filler (Lim), Marble powder (MP), blast furnace slag (Slag) and natural pozzolan (Pz). Instead, Slump flow (Slump), V-funnel, L-Box, static stability (Pi) and 28 days compressive strength (Rc28) were the outputs of the study. Results indicate that ANN models for data sets collected from literature have a strong potential for predicting 28 days compressive strength. Slump flow, V-funnel time and L-Box ratio could be moderately identified while an acceptable prediction has been obtained for static stability. Results have also confirmed by statistical parameters, Regression plots and residual analysis.