Over the past decades simulation techniques, and in particular finite element method, have been used successfully to predict the response of systems across a whole range of industries including aerospace, automotive, chemical processes, geotechnical engineering and many others. In these numerical analyses, the behaviour of the actual material is approximated with that of an idealised material that deforms in accordance with some constitutive relationships. Therefore, the choice of an appropriate constitutive model that adequately describes the behaviour of the material plays an important role in the accuracy and reliability of the numerical predictions. During the past decades several constitutive models have been developed for various materials. In recent years, by rapid and effective developments in computational software and hardware, alternative computer aided pattern recognition techniques have been introduced to constitutive modelling of materials. The main idea behind pattern recognition systems such as neural network, fuzzy logic or genetic programming is that they learn adaptively from experience and extract various discriminants, each appropriate for its purpose. In this thesis a novel approach is presented and employed to develop constitutive models for materials in general and soils in particular based on evolutionary polynomial regression (EPR). EPR is a hybrid data mining technique that searches for symbolic structures (representing the behaviour of a system) using genetic algorithm and estimates the constant values by the least squares method. Stress-strain data from experiments are employed to train and develop EPR-based material models. The developed models are compared with some of the existing conventional constitutive material models and its advantages are highlighted. It is also shown that the developed EPR-based material models can be incorporated in finite element (FE) analysis. Different examples are used to verify the developed EPR-based FE model. The results of the EPR-FEM are compared with those of a standard FEM where conventional constitutive models are used to model the material behaviour. These results show that EPR-FEM can be successfully employed to analyse different structural and geotechnical engineering problems.

Seawater intrusion (SWI) is a widespread environmental problem, particularly in arid and semi-arid coastal areas. Unplanned prolonged over-pumping of groundwater is the most important factor in SWI that could result in severe deterioration of groundwater quality. Therefore, appropriate management strategies should be implemented in coastal aquifers to control SWI with acceptable limits of economic and environmental costs. This PhD project presents the development and application of a simulation-optimization (S/O) model to assess different management methods of controlling saltwater intrusion while satisfying water demands, and with acceptable limits of economic and environmental costs, in confined and unconfined coastal aquifers. The first S/O model (FE-GA) is developed by direct linking of an FE simulation model with a multi-objective Genetic Algorithm (GA) to optimize the efficiency of a wide range of SWI management scenarios. However, in this S/O framework, several multiple calls of the simulation model by the population-based optimization model, evaluating best individual candidate solutions resulted in a considerable computational burden. To solve this problem the numerical simulation model is replaced by an Evolutionary Polynomial Regression (EPR)-based surrogate model in the next S/O model (EPR-GA). Through these S/O approaches (FE-GA and EPR-GA) the optimal coordinates and rates of the both abstraction and recharge barriers are determined in the studied management scenarios. As a result, a new combined methodology, so far called ADRTWW, is proposed to control SWI. The ADRTWW model consists of deep Abstraction of saline water near the coast followed by Desalination of the abstracted water to a potable level for public uses and simultaneously Recharging the aquifer using a more economic source of water such as treated wastewater (TWW). In accordance to the available recharge options (injection through well or infiltration from surface pond), the general performance of ADRTWW is evaluated in different hydro-geological settings of the aquifers indicating that it offers the least cost and least salinity in comparison with other scenarios. The great capabilities of both developed S/O models in identification of the best management solutions and the optimal coordinates and rates of the abstraction well and recharge well/pond are discussed. Both FE-GA and EPR-GA can be successfully employed by a robust decision support system. In the next phase of the study, the general impacts of sea level rise (SLR), associated with its transgression nature along the coastline surface on the saltwater intrusion mechanism are investigated in different hypothetical and real case studies of coastal aquifer systems. The results show that the rate and the amount of SWI are considerably greater in aquifers with flat shoreline slopes compared with those with steep slopes. The SWI process is followed by a significant depletion in quantity of freshwater resources at the end of the century. The situation is exacerbated with combined action of SLR and groundwater withdrawals. This finding is also confirmed by 3D simulation of SWI in a regional coastal aquifer (Wadi Ham aquifer) in the UAE subjected to the coupled actions of SLR and pumping.

[pt] A análise de fluxo de água sob condição transiente não saturada requer o conhecimento das propriedades hidráulicas do solo. Essas relações constitutivas, denominadas curva característica e função de condutividade hidráulica, são descritas através de modelos empíricos que geralmente possuem vários parâmetros que devem ser calibrados com relação a dados coletados. Muitos dos parâmetros nos modelos constitutivos não podem ser medidos diretamente em campo ou laboratório, mas somente podem ser inferidos de forma significativa a partir de dados coletados e da modelagem inversa. Para obter os parâmetros do solo com a análise inversa, um algoritmo de otimização de busca local ou global pode ser aplicado. As otimizações globais são mais capazes de encontrar parâmetros ótimos, no entanto, a solução direta, por meio da modelagem numérica é computacionalmente custosa. Portanto, soluções analíticas (modelo substituto) podem superar essa falha acelerando o processo de otimização. Nesta dissertação, apresentamos a Regressão Polinomial Evolucionária (EPR) como uma ferramenta para desenvolver modelos substitutos do fluxo não saturado. Um rico conjunto de dados de parâmetros hidráulicos do solo é usado para calibrar o nosso modelo, e dados do mundo real são utilizados para validar nossa metodologia. Nossos resultados demonstram que o modelo da EPR prevê com precisão os dados de carga de pressão. As simulações do modelo se mostram concordantes com as simulações do programa Hydrus. / [en] Water flow analyses under transient soil hydraulic conditions require knowledge of the soil hydraulic properties. These constitutive relationships, named soil-water characteristic curve (SWCC) and hydraulic conductivity function (HCF) are described through empirical models which generally have several parameters that must be calibrated against collected data. Many of the parameters in SWCC and HCF models cannot be directly measured in field or laboratory but can only be meaningfully inferred from collected data and inverse modeling. In order to obtain the soil parameters with the inverse process, a local or global optimization algorithm may be applied. Global optimizations are more capable of fiding optimum parameters, however the direct solution through numerical modeling are time consuming. Therefore, analytical solutions (surrogate models) may overcome this shortcomming by accelerating the optimization process. In this work we introduce Evolutionary Polynomial Regression (EPR) as a tool to develop surrogate models of the physically-based unsaturated flow. A rich dataset of soil hydraulic parameters is used to calibrate our surrogate model, and real-world data are then utilized to validate our methodology. Our results demonstrate that the EPR model predicts accurately the observed pressure head data. The model simulations are shown to be in good agreement with the Hydrus software package.