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.

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.