Upgrading the prediction of jet grouting column diameter using deep learning with an emphasis on high energies

This article proposed a new method to estimate the diameter of jet grouting columns. The method uses the largest data collection of column diameters measured to date and includes a large amount of new data that fills the existing gap of data for high injection energies. The dataset was analysed using a deep neural network that took into account the problem’s key parameters (i.e. type of soil, soil resistance, type of jet and specific energy in the nozzle). As a result, three different neural networks were selected, one for each type of jet, according to the errors and consistency associated with each. Finally, using the trained networks, a number of design charts were developed to determine the diameter of a jet grouting column as a function of the soil properties and the jet system. These charts allow generating an optimal jet grouting design, improving the prediction of the diameter of jet columns especially in the high energy triple fluid.This work was supported by the Spanish Ministry of Economy and Competitiveness, the State Agency of Research and the European Funds for Regional Development under project TEC2017-85244-C2-1-P

Modelling of a generalized thermal conductivity for granular multiphase geomaterial design purposes

Soil thermal conductivity has an important role in geo-energy applications such as high voltage buried power cables, oil and gas pipelines, shallow geo-energy storage systems and heat transfer modelling. Hence, improvement of thermal conductivity of geomaterials is important in many engineering applications. In this thesis, an extensive experimental investigation was performed to enhance the thermal conductivity of geomaterials by modifying particle size distribution into fuller curve gradation, and by adding fine particles in an appropriate ratio as fillers. A significant improvement in the thermal conductivity was achieved with the newly developed geomaterials. An adaptive model based on artificial neural networks (ANNs) was developed to generalize the different conditions and soil types for estimating the thermal conductivity of geomaterials. After a corresponding training phase of the model based on the experimental data, the ANN model was able to predict the thermal conductivity of the independent experimental data very well. In perspective, the model can be supplemented with data of further soil types and conditions, so that a comprehensive representation of the saturation-dependent thermal conductivity of any materials can be prepared. The numerical 'black box' model developed in this way can generalize the relationships between different materials for later added amounts of data and soil types. In addition to the model development, a detailed validation was carried out using different geomaterials and boundary conditions to reinforce the applicability and superiority of the prediction models

Soil Profile Prediction Using Artificial Neural Networks in Sudan

Artificial Neural Networks (ANNs) are a form of Artificial Intelligence, which are mathematical models, inspired from the brains of certain information-processing characteristics, producing meaningful solutions, which fall beyond the reach of conventional digital computers. In recent years, the use of ANNs has increased in many areas of engineering. In particular, ANNs have been applied to many geotechnical engineering problems and have demonstrated some degree of success. In this study,ANNs are used for soil classification prediction in a specified locations at different depths, based on the available site investigation data from a specific area in Sudan. Regarding the large number of the data and considerable variations in soil layers in Sudan, hundred of boreholes were selected for this study . Seven Networks are developed to predict the soil layering in specified locations in Khartoum city.In this study ,area of about 165 square kilometers of Khartoum concentrating on Blue Nile region is considered and the results are then compared with data of actual boreholes to check the ANN model’s validity . The results indicate that Artificial Neural Networks are a useful technique for predicting relationships between the input parameters of the three dimensional coordinates and the resulting soil classification and soil parameters output. So, Artificial Neural Networks can be considered as an effective tool for predicting the soil classification in Khartoum

Modelling of geotechnical problems using soft computing

Correlations are very significant from the earliest days; in some cases, it is essential as it is difficult to measure the amount directly, and in other cases it is desirable to ascertain the results with other tests through correlations. Soft computing techniques are now being used as alternate statistical tool, and new techniques such as artificial neural networks (ANN), support vector machine (SVM), multivariate adaptive regression splines (MARS) has been employed for developing the predictive models to estimate the needed parameters. In this report, four geotechnical problems like compaction parameters of sandy soil, compression index of clay, relative density of clean sand and side resistance of drilled shaft have been modeled. Various error criteria such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient (R) have been considered for the comparison of different models. Finally different sensitivity analysis has been shown to identify the significance of different input parameters that affects the developed models. The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. The use of soft computing may provide new approaches and methodologies to minimize the potential inconsistency of correlations

Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

As a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better

A decision support system for ground improvement method selection

Abstract unavailable please refer to PD

Finite and discrete element modelling of internal erosion in water retention structures

Internal erosion is a process by which particles from a soil mass are transported due to an internal fluid flow. This phenomenon is considered as a serious threat to earthen structures. Internal erosion is the main cause of damage or failure in the body or foundation of embankment dams. Therefore, it is necessary to have an accurate knowledge of fluid-particle interactions in saturated soils during design and operation. The hydrodynamic behaviour of porous media in geotechnical engineering is typically modelled using continuum methods such as the finite element method (FEM). It has become increasingly common to combine the discrete element method (DEM) with continuum methods such as the FEM to provide microscopic insights into the behaviour of granular materials and fluid–solid interactions. This Ph.D. thesis aims to develop a hierarchical FEM-DEM algorithm to analyze the internal erosion process in large scale earthen structures. To achieve this goal, we (i) programmed a versatile interface between two FEM and DEM codes, (ii) implemented a coarse-grid method (CGM) for the coupled FEM-DEM model to minimize the computations associated with drag force calculation, (iii) developed a multiscale algorithm for the interface to limit the number of discrete particles involved in the simulation, (iv) assessed the accuracy of drag force derived from CGM, and (v) trained an Artificial Neural Network (ANN) to improve the prediction of the drag force on particles. The development of multimethod or hybrid models combining continuum analyses and discrete elements is a promising research avenue to combine the advantages associated with both modelling scales. This thesis first introduces ICY, an interface between COMSOL Multiphysics (commercial finite-element engine) and YADE (open-source discrete-element code). Through a series of JAVA classes, the interface combines DEM modelling at the particle scale with large scale modelling with the finite element method. ICY was verified with a simple example based on Stokes’ law. A comparison of results for the coupled model and the analytical solution shows that the interface and its algorithm work properly. The thesis also presents an application example for the interface. The interface used CGM drag force to model an internal erosion test in a permeameter. The number of particles that can be included in the DEM simulation of ICY is limited, thus restricting the volume of soil that can be modelled. The second part of the thesis proposes a multimethod hierarchical approach based on ICY to model the coupled hydro-mechanical behaviour for saturated granular soils. A hierarchical algorithm was specifically developed to limit the number of particles in the DEM simulations and to eventually allow the modelling of internal erosion for large structures. The number of discrete bodies in the simulations was restricted through employing discontinuous subdomains along the sample. This avoids generating the full sample as a DEM model. Particles in these small subdomains were subjected to buoyancy, gravity, drag force and contact forces for small time steps. The small subdomains provide the continuum model with particle flux. The FEM model solves a particle conservation equation to evaluate porosity changes for longer time steps. The multimethod framework was verified by simulating a numerical internal erosion test. The fluid motion in geotechnical applications is typically solved using CGM. With these methods, an average form of the Navier–Stokes equations is solved. The total drag force derived from CGM can be applied to the particles proportionally to their volume (CGM-V) or surface (CGM-S). However, there is some uncertainty regarding the application of the CGM drag models for polydispersed particle. The accuracy of CGM has not been systematically investigated through comparing CGM results with more precise results obtained from solving the Navier-Stokes equations at the pore scale. The last part of this research investigates the accuracy of CGM-V and CGM-S drag forces in comparison with the pore-scale values obtained by FEM. COMSOL Multiphysics was used to simulate the fluid flow in three unit cells with different porosity values (0.477, 0.319 and 0.259). The unit cell involved a monosize skeleton of large particles with fixed positions and a smaller particle with variable sizes and positions. The results showed that the CGM-V and CGM-S could not predict precisely the drag force on the small particle. An ANN was trained to predict the drag force on the smaller particle. A very good correlation was found between the ANN output and the FEM results. The ANN could thus provide drag force values with accuracy similar to that obtained using flow simulations at the pore scale, but with computational resources that are comparable to CGM. This thesis contributes to the literature by improving our understanding of hybrid DEM-continuum methods and drag force computations in DEM simulations. It provides guidelines to researchers and developers who try to model internal erosion in real scale soil systems