Metaheuristic model for the interface shear strength between granular soil and structure considering surface morphology

Application of Novel Machine Learning Techniques for Predicting the Surface Chloride Concentration in Concrete Containing Waste Material

“…GEP, with an R 2 value of 0.88, was the most accurate when compared to the ANN (R 2 equal to 0.84) and DT (R 2 equal to 0.72). This algorithm was also compared with those used in [18], and the results of the comparison accuracy are presented in Figure 9. It can be seen from Figure 9 that the proposed GEP algorithm accurately describes chloride surface concentrations when compared to other algorithms.…”

“…However, there is still no accurate prediction model that is based on only a small number of variables. In contrast, when using machine learning algorithms, prediction models are more accurate and might be successfully used [18]. In this article, 642 data samples obtained from the literature survey [19] were used to predict surface chloride concentrations through the use of machine learning algorithms.…”

Analyzing cyclic shear behavior at the sand–rough concrete interface: An experimental and DEM study across varying displacement amplitudes

“…21,22 Surface roughness serves as a crucial indicator and is typically defined by parameters such as peakvalley distance (𝑅), average roughness (𝑅 a ), or standardized roughness (𝑅 n = 𝑅 max ∕𝑑 50 ). 23,24 However, these parameters are primarily applied to regular surfaces while engineering practice often deals with surfaces that possess irregular shapes. Various methods have been proposed to assess the roughness of irregular surfaces, including the sand cone method, sand replacement method, and sand-pouring method 25,26 But in the preparation of experimental materials, researchers commonly create regular surface test blocks using acquired roughness values.…”

“…Numerous factors influence the properties of the sand–concrete interface, such as stress level, particle shape, particle size, initial relative density, and temperature 21,22 . Surface roughness serves as a crucial indicator and is typically defined by parameters such as peak–valley distance ($$R$$), average roughness ($${R}_{\mathrm{a}}$$), or standardized roughness ($${R}_{\mathrm{n}} = {R}_{\max }/{d}_{50}$$) 23,24 . However, these parameters are primarily applied to regular surfaces while engineering practice often deals with surfaces that possess irregular shapes.…”

Effect of Chemical Composition of Fine Aggregate on the Frictional Behavior of Concrete–Soil Interface under Sulfuric Acid Environment

“…Rmax [28][29][30] was proposed by Yoshimi and Kishida [40] and its definition was the maximum vertical distance between the highest point and the lowest point along the section line on a standard length, while Rn [5,31] was proposed by Uesugi and Kishida [41] and was expressed as Rmax divided by the mean particle diameter (D50). However, Rmax and Rn could not reflect the partial distribution and local changes of the surface profile [42]. The application of fractal dimensions to the eval-uation of the rough surface became more and more popular [43][44][45][46] and several studies concluded that the fractal dimension could be applied successfully in evaluating the resistance of steel corrosion [47,48], pore evolution in concrete [49], and evolution of fracture in rock surface [50,51].…”