A generalised water retention model with soil fabric evolution

This paper introduces a novel soil water retention curve model that considers two different mechanisms of water retention –namely capillarity and adsorption – and their evolution with soil fabric along a generalized stress path. The model is formulated based on a new developed framework, which is justified against experimental evidence and incorporates the following developments: (i) the characteristics of the capillary and adsorption water retention curves are identified, and an ad-hoc water retention model is developed for each mechanism; (ii) the capillary and adsorption water retention curves are related to the experimentally determinable water retention curve thanks to a newly defined parameter named “contribution factor”, which changes with the evolution of the fabric. The proposed model is an elastoplastic water retention model and it allows to describe satisfactorily the water retention capacity of different soils. All simulation results support the statement that the adsorptive mechanism plays a crucial rule in the high suction range while capillarity is important in the low suction range. Moreover, it is shown that the developed capillary water retention curve allows to easily compute, at any state of the soil, a suitable capillary degree of saturation; the latter, adopted within the generalised effective stress concept, makes it possible to interpret shear strength data under different saturation conditions properly.

Numerical modeling and neural networks to identify constitutive parameters from in situ tests

Rafal Filip Obrzud

This study proposes a new advanced algorithm for determining material parameters based on in situ tests. In situ testing gives an opportunity to perform soil characterization in natural stress conditions on a representative soil mass. Most field techniques reduce soil disturbances to minimum, allowing investigating the response of virgin soil. Self-boring pressuremeter tests (SBPT) and standard piezocone tests (CPTU) are widely used to deduce properties of clayey soils through analytical and empirical correlations between soil properties and experimental measurements. Empirical correlations usually require some tuning based on reference laboratory data because first-order estimates for typical correlation coefficients may give unreliable evaluation of soil properties. Analytical correlations are mostly based on cavity expansion methods which are restricted to either fully drained or perfectly undrained problems, so that inverse closed-form solutions for relatively simple constitutive models can be derived. In practice, however, depending on physical and consolidation properties of the soil, partially drained conditions may occur during field testing, leading to an erroneous estimation of clay characteristics. Therefore, elaborating a generic parameter identification framework, which is based on the artificial neural network (NN) technique and which may improve the reliability of soil properties derived from in situ testing, is the main goal of this research. This study explores the possibility of using NNs to solve complex inverse problems including partially drained conditions. In other words, NNs are used to map experimental measurements onto set of soil properties. The development of NN-based inverse models is based on a training data sets which consists of pseudo-experimental measurements derived from numerical simulations of both the SBPT and the CPTU test in normally- and lightly overconsolidated clay type material. The study presents a generic two-level procedure designed for the calibration of constitutive models of soils. It is demonstrated that NN inverse models can be easily integrated into the classical back-analysis. At the first level, the NN approach is applied to achieve the first approximation of parameters. This technique is used to avoid potential pitfalls related to the conventional gradient-based optimization (GBO) technique, considered here as a corrector that improves predicted parameters. Trained NNs as parallel operating systems can provide output variables instantly and without a costly GBO iterative scheme. The proposed framework is verified for the elasto-plastic Modified Cam Clay (MCC) model that can be calibrated based on standard triaxial laboratory tests, i.e. the isotropic consolidation test and the consolidated isotropic drained compression test. The study presents formulations of the input data for the NN predictors, enhanced by a dimensional reduction of experimental data using principal component analysis (PCA). The determination of model characteristics is demonstrated, first on numerical pseudo-experiments and then on the experimental data. Furthermore, the efficiency of the proposed approach in terms of accuracy and computational effort is also discussed. The verified two-level strategy is applied to a numerical procedure of parameter identification for the boundary value problem (BVP) of the SBPT. The coupled hydro-mechanical finite element (FE) formulation allows the generated excess pore water pressure to be dissipated during simulations of the expansion test, followed then by a holding test. Numerical simulations demonstrate that volume changes that may occur in clay during the expansion test due to partial drainage, can cause local soil hardening near the cavity wall and affect parameter interpretation for pressuremeter tests. Therefore, the NN technique is applied to obtain an initial guess for model parameters, taking into account the possible partially drained conditions during the expansion test. Parameter identification based on measurements obtained through the pressuremeter expansion test and two types of holding tests is illustrated on the MCC model. NNs are trained using a set of synthetic test samples, which are generated by means of FE simulations based on constrained random permutations of input model parameters. The measurements obtained through expansion and consolidation tests are normalized by the proposed normalizing formulas so that NN predictors operate independently of the testing depth. Examples of parameter determination are demonstrated on both numerical data and field measurements from the Fucino clay deposit. The efficiency of the combined parameter identification in terms of accuracy, effectiveness and computational effort is also discussed. Finally, an application of NN predictors as a stand-alone support for soil profiling is presented for the piezocone test. By similarity to the SBPT problem, a number of NN inverse models are developed based on the results derived from rigorous FE analyzes. The FE model of piezocone penetration involves numerical formulation for the two-phase material obeying the MCC law and including the large strain theory, as well as the large deformation formulation for contact interface. It is demonstrated that a considerable computational effort related to the generation of the training database can be reduced by optimizing the mesh size and "steady-state" depth in function of soil rigidity index. Due to a severe loss of measurement accuracy observed in the finite elements adhered to the "rough" interface, an equivalent semi-numerical approach is proposed to account for frictional effects in different drainage conditions which are delineated from a number of numerical simulations. The validity of the developed penetration model is verified in detail by means of comparisons with other theoretical solutions and parametric studies synthesized from literature, as well as experimental evidence for both undrained and partially drained scenarios. An extended parametric study including influence analyzes of strength and stress anisotropy, rigidity index and cone roughness on two cone factors provides new insight into the analysis of cone penetration. The shortcomings of the FE model due to the limitations of the applied constitutive model are also discussed. As regards NN models, different configurations of input variables, including standard normalized piezocone metrics and other available soil characteristics are investigated in terms of feasibility of effective NN training. The post-training regression analyzes are performed for numerical data allowing the assessment of the influence of specific input variables on accuracy of parameters predictions. Finally, the developed NN models are applied to predict parameters based on field measurements for a number of characterization sites. Provided examples demonstrate that NN inverse models may constitute an effective complementary support during the first-order quantification of the MCC parameters from piezocone measurements.

EPFL2009

Valuation of a pumped-storage hydro-plant in a multiple markets framework

Nikolaos Iliadis

There has been an evolving electricity market structure starting from the spot market and its derivatives, to several other markets such as the Intraday Market, the Balancing Market and the Reserve Capacity Market. The participation in these markets has changed significantly the operation policy of the plants and therefore their value in the market. The process of valuating a generation plant is closely related to the markets the plant can participate in terms of country and type (Day Ahead Market, Intraday Market, Balancing Market). Increasingly more Renewable Energy Sources plants have been entering into the system, increasing thus the factor of intermittency in the power profile.In addition to the variability in generation, solar photovoltaic plants, where their generation is consistently during the peak hours, together with the wind power plants have driven down the peak Day Ahead Market prices. Furthermore, the decision of countries in Europe to decommission their nuclear power plants, has driven up the off-peak prices. The current research tackles the problem of the long term valuation of a Hydro Pumped-Storage plant participating in the Day Ahead Market and Intraday Market, for thirty-five (35) years ahead, starting from 2015 and ending to 2050. The consideration of hourly time stage was indispensable since the markets, a Hydro Pumped-Storage plant achieves its main revenues are in the short-term, such as Day Ahead Market and Intraday Market. Within the scope of this research was required the application of four (4) modules that assisted in calculating the intermediate results in order to valuate the Hydro Pumped-Storage plant through a set of economic and financial indicators. Among the four (4) modules, three (3) where specifically developed within this research, one for the IM prices simulation, using the R statistics package, one for the operation simulation of an Hydro Pumped-Storage in the Day Ahead Market and Intraday Market, using MatLab and the IBM Cplex algorithm, and one for the financial and economic valuation of the plant, in excel using the financial formulas package. The fourth (4)th module was a commercial software, named AURORAxmp and developed by the USA company EPIS, for the operation simulation of the Day Ahead Market and the calculation of a proxy of the Day Ahead Market prices throughout the horizon. The latter module was fed with four (4) generation expansion capacity scenarios and reciprocal demand evolutions until 2050, based in the official European Union reports. The final purpose of this research was to design, implement and apply a methodology, on a real case study, for the valuation of a Hydro Pumped-Storage plant in a long term horizon. The results for the Hydro Pumped-Storage plant participating in the Day Ahead Market and Intraday Market, showed after corporate tax, an Net Present Value of 653 million EUR, a payback of the in thirty-one (31) years and of the private equity in twenty (20) years, an Internal Rate of Return of the project of 7.9% and of the private equity of 12.81% (for 20% of private equity). Comparing to the results above, a Hydro Pumped-Storage participating in the Day Ahead Market only, showed deteriorated numbers, having an Net Present Value of 243 million EUR, a payback of the project in thirty-four (34) years and of the private equity in twenty-eight (28) years,an Internal Rate of Return of the project of 6.5% and of the private equity of 10.32%.

EPFL2015

Numerical modeling and neural networks to identify constitutive parameters from in situ tests

Rafal Filip Obrzud

EPFL2009

Valuation of a pumped-storage hydro-plant in a multiple markets framework

Nikolaos Iliadis

EPFL2015