This study evaluates a novel combination of coffee husk ash (CHA), palm leaf ash (PLA), and silica-enriched filter cake (SEFC) for extrusion-based 3D concrete printing (3DCP), where CHA and PLA serve as waste-derived supplementary cementitious materials (SCMs) and SEFC functions both SCM and as a rheology-modifying additive to enhance fresh and hardened performance. Physicochemical characterization revealed high silica contents in SEFC (80.6
The major problems associated with expansive clay are its high swelling potential, plasticity, moisture-sensitivity, and low load-bearing capacity, all of which challenge the subgrade of pavements. The main challenge is to simultaneously improve the mechanical performance with volumetric instability and minimize the amount of conventional stabilizing materials needed to achieve this. This study is aimed to solve this challenge by systematically using the chemical–mechanical stabilization method with cement kiln dust (CKD), calcium carbide residue (CCR), and polypropylene fibers (PPF). Differential free swell (DFS), Atterberg limits, compaction, unconfined compressive strength (UCS), soaked California Bearing Ratio (CBR), and microstructural analysis using scanning electron microscopy–energy-dispersive X-ray spectroscopy (SEM–EDS) were used to analyze individual, binary, and ternary stabilization systems. The stabilized mixtures were then evaluated for pavement-subgrade performance in terms of resilient modulus determined from CBR and flexible pavement thickness design as per IRC:37–2018. The untreated clay had a DFS of 75.38
The present work provides a mathematical model for unsteady magnetohydrodynamic flow of Maxwell fluid with coupled heat and mass transfer between active parallel plates embedded in a porous medium. The model incorporates wall deformation, time-dependent plate separation, Brownian motion, thermophoresis, activation energy and endothermic-exothermic chemical reactions to describe complex non-Newtonian transfer phenomena. Similarity transformations reduce the governing partial differential equations to nonlinear ordinary differential equations, which are solved using the Meixner–Pollaczek polynomial-based collocation method. The Taguchi method is used to optimize the heat transfer rate, with a residual contribution of 0.04562
It is complicated to comprehend the shear strength behavior of steel fiber-reinforced concrete (FRC) because of various parameters that are involved. The goal of this paper is to develop a framework for precise estimation of the shear strength of steel FRC beams using a novel hybrid machine learning technique. The adaptive neuro fuzzy inference system (ANFIS) was chosen as a predictive model to address this problem, and a new metaheuristic algorithm based on the equilibrium optimizer (EO) was used, for optimal generalization and accurate prediction. In addition, the performance of the newly created predictive model was evaluated by using other popular metaheuristic algorithms, such as genetic algorithm (GA) and particle swarm optimization (PSO). To create the EO-ANFIS model, 241 experimental data sets from the literature were utilized. The input variables consisted of the steel fiber ratio, the concrete design strength, the effective depth, the reinforcement ratio, and the span-to-depth ratio, while the output variable was the shear strength capacity. The accuracy of the ANFIS models based on metaheuristic algorithms was evaluated against four equations from other research and against modern ensemble learners using ten-fold cross-validation. The results indicated that the ANFIS models based on the proposed algorithms outperformed the previous equations, and the EO-ANFIS model had a better performance than the GA-ANFIS and PSO-ANFIS models while remaining competitive with the ensemble methods and offering an interpretable rule structure. The sensitivity analysis revealed that the most influential factors on shear strength are reinforcement ratio and steel fiber ratio.
Reducing research costs has always been a priority for engineers and researchers. The advent of high-performance computers has enabled extensive use of numerical methods, particularly computational fluid dynamics (CFD), in structural wind analysis. In this study, ANSYS Fluent was used to investigate wind load distribution on eleven three-dimensional geometrical configurations with varying apex shapes and diameter distributions. The analyzed geometries include both sharp-apex and flat-apex forms with gradual changes in cross-sectional diameter along the height. A uniform wind direction normal to the front face of the structures (0° incidence angle) was applied, as it represents the most critical loading condition leading to maximum wind pressure on the windward surface. The pressure coefficient variations were evaluated over the entire height of the structures, from the base (0) to the top (h), as well as at different angular positions around the cross-section. Results show that sharp-apex configurations, such as 0–0.5–1 and 0–1–1, exhibit minimum pressure coefficients (maximum suction) of −3.30 and −3.99, respectively, occurring at the apex and at 90° and 270° angular positions. Flat-apex configurations show lower suction values, ranging from −2.15 to −3.02, with the minimum pressure distributed along the structure height rather than at the apex. Maximum pressure coefficients across all configurations ranged from 0.98 to 1 at the windward front, indicating that front-facing pressure is relatively insensitive to geometric variations. These findings highlight the critical influence of apex geometry and cross-sectional variation on wind load distribution, offering key insights for designing aerodynamic and wind-resistant structures.
