Richard’s equation governs the migration of moisture in the soil under unsaturated conditions. Although this differential equation provides a rigorous approach to simulating important infiltration problems, obtaining analytical and numerical solutions to this equation has been a particularly challenging task. This is largely due to the highly nonlinear nature of the soil hydraulic properties, including the moisture retention curve and the hydraulic conductivity function. Whereas analytical solutions of Richard’s equation have been reported for problems involving steady-state conditions and simple hydraulic models, solutions for transient conditions have rarely been obtained. However, such analytical solutions would be particularly valuable, for example, to validate the accuracy of numerical schemes, as well as to facilitate parametric evaluations. A series of analytical solutions of Richard’s equation for unsaturated flow under transient conditions have been developed as part of this study. The solutions involve a variety of initial and boundary conditions. The analytical solutions in this study could be obtained after expressing the governing equation as the addition of advective and diffusive flow components. The solutions consider logarithmic and linear models to represent the soil moisture retention and the hydraulic conductivity functions, respectively. Solutions are also provided for special cases in which either the advective or the diffusive components dominate the flow process, as well as for the steady-state cases. A parametric evaluation was found to provide insight into important characteristics of infiltration problems. In particular, relevant features of an unsaturated flow problem can be explained by evaluating the trends in its advective and diffusive flow components.

This paper explores the potential of machine learning techniques to predict the soil water retention curve based on physical characterization parameters. Results from 794 water retention and suction points obtained from 51 different soils were used in the algorithm. The soil properties used are the percentages of gravel, sand, silt, and clay, the plasticity index, the porosity, and the relation between the volumetric water content and total suction. The data were used as input for machine learning estimators to predict the volumetric water content of a soil with specified physical characterization parameters and suction, the techniques of artificial intelligence were developed in python. Results show that an extremely randomized trees’ estimator can reach a coefficient of determination of 0.99 in the training dataset, with a coefficient of 0.90 in the cross-validation and testing dataset, which measures the generalization capacity. Furthermore, a continuous function can be obtained by fitting a model such as Cavalcante & Zornberg, or van Genuchten, or Costa & Cavalcante (bimodal) to the predictions of the machine learning for use in numerical methods. These results indicate that the proposed machine learning estimator can become an interesting alternative to estimate the soil water retention curve in engineering practice. This work is in progress and the predictions can be improved with the addition of new data. Know how to participate at the end of the paper.

Internal erosion is the most important failure mechanism of earth and rockfill dams. Since this type of erosion develops internally and silently, methodologies of data acquisition and processing for dam monitoring are crucial to guarantee a safe operation during the lifespan of these structures. In this context, artificial intelligence techniques show up as tools that can simplify the analysis and verification process not of the internal erosion itself, but of the effects that this pathology causes in the response of the dam to external stimuli. Therefore, within the scope of this paper, a methodological framework for monitoring internal erosion in the body of earth and rockfill dams will be proposed. For that, artificial intelligence methods, especially deep neural autoencoders, will be used to treat the acoustic data collected by geophones installed on a dam. The sensor data is processed to identify patterns and anomalies as well as to classify the dam’s structural health status. In short, the acoustic dataset is preprocessed to reduce its dimensionality. In this process, for each second of acquired data, three parameters are calculated (Hjorth parameters). For each parameter, the data from all the available sensors are used to calibrate an autoencoder. Then, the reconstruction error of each autoencoder is used to monitor how far from the original (normal) state the acoustic signature of the dam is. The time series of reconstruction errors are combined with a cumulative sum (CUSUM) algorithm, which indicates changes in the sequential data collected. Additionally, the outputs of the CUSUM algorithms are treated by a fuzzy logic framework to predict the status of the structure. A scale model is built and monitored to check the effectiveness of the methodology hereby developed, showing that the existence of anomalies is promptly detected by the algorithm. The framework introduced in the present paper aims to detect internal erosion inside dams by combining different techniques in a novel context and methodological workflow. Therefore, this paper seeks to close gaps in prior studies, which mostly treated just parts of the data acquisition–processing workflow.

Combining tomographical reconstructed volumes and direct pore-scale flow simulations has proven to be an efficient tool in modern soil sciences. The main objective of the present paper is to present an extensive study about the usage of micro–computed tomography to obtain transport properties of porous media. The transport properties are obtained by means of pore-scale simulations whose domains are the pore space of the tomographical images of three different materials: glass spheres, sand, and clay. The main discussion in the present paper regards the definition of the representative elementary volume (REV). To address this issue, statistical analyses were carried out, showing that the REV concept is scale-dependent. Also, some guidelines considering the specification of the numerical domain size are discussed. It is shown that, for the glass spheres and sand, the coefficient of variation (CV) of the permeability decreases as the simulation domain increases, reaching values as low as 20%. In contrast, for clay samples, the CV does not show a clear decreasing tendency, and its values are approximately 75% for the largest domain size considered. Hypothesis tests were used to show that more than one domain size can be considered a REV (i.e., there are different minimum domain sizes for which permeability and porosity can be evaluated), and these values can be used to predict the transport properties of a larger part of the domain at a given scale.

The analysis of the geometry of porous media is an important aspect of modern soil sciences. This comes from the fact that not only experimental studies but also numerical simulations demand a considerable knowledge of the porous matrix involved. Subsequently, the combination of microtomography, three-dimensional (3D) printing, and numerical simulations is studied. The first step in this process is choosing an artificial model for the soil. In the present study, 3D cellular automata were chosen. Following that, pore-scale permeability numerical simulations were considered in such artificial porous media. To bring numerical simulations to real-world situations, artificial porous media were 3D printed. By means of the methodology hereby presented, it is possible to generate specific porous media to isolate and study a given phenomenon of interest. The printings were subjected to a metrological analysis, which revealed that, for all samples analyzed, more than 95% of the linear deviations between the print and the computational model were smaller than the resolution of the printer (0.3 mm). This validates the usage of 3D prints as valuable tools to build artificial porous media. The real permeabilities of the printed porous media were obtained by a permeability experiment. Finally, numerical and real permeability values were compared, and a scale analysis for this property was carried out. It was found that the numerical routines can be used to correctly estimate the real permeability of a given porous medium. For example, the shape of the pore space can be completely known by digitally analyzing the computational medium, and specific parameters (e.g., pore throat size, pore size distribution, hydraulic mean radius, tortuosity) can be explicitly related to its permeability. In contrast, other 3D printing techniques have to be considered. Printing in flexible materials, for example, could provide samples for consolidation analyses.