Concrete is a prominent construction material globally, owing to its reputed attributes such as robustness, endurance, optimal functionality, and adaptability. Formulating concrete mixtures poses a formidable challenge, mainly when introducing novel materials and additives and evaluating diverse design resistances. Recent methodologies for projecting concrete performance in fundamental aspects, including compressive strength, flexural strength, tensile strength, and durability (encompassing homogeneity, porosity, and internal structure), exist. However, actual approaches need more diversity in the materials and properties considered in their analyses. This dataset outlines the outcomes of an extensive 10-year laboratory investigation into concrete materials involving mechanical tests and non-destructive assessments within a comprehensive dataset denoted as ConcreteXAI. This dataset encompasses evaluations of mechanical performances and non-destructive tests. ConcreteXAI integrates a spectrum of analyzed mixtures comprising twelve distinct concrete formulations incorporating diverse additives and aggregate types. The dataset encompasses 18,480 data points, establishing itself as a cutting-edge resource for concrete analysis. ConcreteXAI acknowledges the influence of artificial intelligence techniques in various science fields. Emphatically, deep learning emerges as a precise methodology for analyzing and constructing predictive models. ConcreteXAI is designed to seamlessly integrate with deep learning models, enabling direct application of these models to predict or estimate desired attributes. Consequently, this dataset offers a resourceful avenue for researchers to develop high-quality prediction models for both mechanical and non-destructive tests on concrete elements, employing advanced deep learning techniques.

Lake Zirahuén is one of the ecologically better preserved and small-sized lakes in Mexico. Observations revealed that Lake Zirahuén is subjected to a consistent diurnal wind because of the presence of a valley–mountain breeze that triggers semidiurnal and 3–8 h variability oscillations, with the latter among the natural frequencies of the lake, whereas the former and the principal forcing are in near resonance with the second vertical mode. The thermodynamic variability was greater in the metalimnion where the analysis of eigenfunctions shows that higher modes (>2) are important at depths below the thermocline. The numerical modeling adequately described the water temperature evolution and surface layer currents in an averaged manner. The daily observations showed drifts in surface currents, acquiring velocities of up to 0.1 m/s, owing to wind stress, which caused an increase in temperature at the northern section of the lake. Data averaged over three months revealed that the surface currents flow northward with an anticyclonic return to the east part and a pair of cyclonic returns to the northwestern and southwestern parts of the lake; whereas at the vertical, the structure showed two circular regions divided by the thermocline located at a depth of 15–20 m.

The accurate description of the flow of water in porous media is of the greatest importance due to its numerous applications in several areas (groundwater, soil mechanics, etc.). The nonlinear Richards equation is often used as the governing equation that describes this phenomenon and a large number of research studies aimed to solve it numerically. However, due to the nonlinearity of the constitutive expressions for permeability, it remains a challenging modeling problem. In this paper, the stationary form of Richards’ equation used in saturated soils is solved by two numerical methods: generalized finite differences, an emerging method that has been successfully applied to the transient case, and a finite element method, for benchmarking. The nonlinearity of the solution in both cases is handled using a Newtonian iteration. The comparative results show that a generalized finite difference iteration yields satisfactory results in a standard test problem with a singularity at the boundary.