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Article Sustainable Stabilization of Expansive Soils Using Waste Marble Powder and Expanded Polystyrene Beads: Experimental Evaluation and Predictive Modelling(Elsevier, 2026) Akis, Ebru; Citak, Mete; Lotfi, BahramExpansive soils exhibit considerable volume changes with moisture fluctuations leading to serious challenges for civil infrastructure, causing structural instability, pavement distortion, and foundation damage. While lime and cement remain widely used stabilizers, recent research has increasingly focused on waste-derived materials such as marble powder (MP) and expanded polystyrene beads (EPSb) as promising alternatives. These materials provide a practical approach to soil stabilization while contributing to the reuse of industrial by-products. In this study, the engineering behavior of high-plasticity clay was improved through the inclusion of MP and EPSb as additive materials. MP was added at 0%, 5%, 10%, 15%, and 20%, and EPSb at 0%, 0.3%, and 0.9% by dry weight of the high plasticity clay. Both additives were used alone and in combination. Laboratory tests, including Standard Proctor, free swell (FS), and unconfined compressive strength (UCS), were conducted. The results confirmed that the additives effectively reduced the liquid limit (LL) by 20.1% and the plasticity index (PI) by up to 22.4%. Results showed that EPSb effectively reduced FS and UCS, while MP decreased FS and increased UCS up to an optimal content. The most effective mixes achieved a maximum reduction of 54.7% in free swell (FS) (at 20% MP and 0.9% EPSb content) and a maximum increase of 13.1% in unconfined compressive strength (UCS) (at 5% MP content) compared to the untreated soil. The compaction tests further revealed a general decrease in optimum moisture content (OMC) and a slight increase in maximum dry density (MDD) with increasing MP content. Accordingly, the free swell (FS) and unconfined compressive strength (UCS) of the treated soils were predicted using multiple linear regression (MLR) and artificial neural network (ANN) models, developed from both the current experimental dataset and previously published studies. Input variables included untreated FS and UCS values, additive percentages, and one index property. The ANN model demonstrated superior predictive capability, achieving R2 values of 0.955 and 0.874 for FS and UCS, respectively, compared to 0.411 and 0.618 obtained with MLR. These results highlight the robustness of ANN in capturing nonlinear soil behavior and underscore its reliability and accuracy, particularly under limited data conditions.Article Citation - WoS: 2Citation - Scopus: 4Optimum Cost Prediction of Reinforced Concrete Cantilever Retaining Walls(Mdpi, 2023) Akis, EbruReinforced concrete cantilever retaining walls (RCCRWs) are widely used in civil engineering projects as a common type of retaining structure. The design of these structures focuses on ensuring safety against various failure scenarios and compliance with standard building code requirements. This research aims to enhance the design process of RCCRWs by developing a specific code and optimizing it through a metaheuristic-based algorithm. In this study, the cost prediction of RCCRWs is also investigated through a parametric study involving key variables such as wall height, seismic zone, backfill material properties, and backfill inclination angle. To achieve this, non-linear regression analysis is employed to establish an empirical correlation, enabling cost estimation for optimized RCCRWs. The resulting prediction equation is simple to use, requiring only limited inputs. Therefore, it can be applied during the initial stages of a project, making a valuable contribution in determining approximate costs for RCCRW projects.