New Methods for Aquifer Parameters from Slug Test Data

An objective method and a diagnostic curve method are developed for estimating the aquifer parameters (storage coefficient and transmissivity) from slug test data on a fully penetrating well. In the objective method, explicit equations are developed for estimating the aquifer parameters. In the diagnostic curve method, a set of unimodal diagnostic curves is developed along with a guiding straight line. The rise or fall in water level of the well is plotted diagnostically on a double logarithmic graph and matched to one of the diagnostic curves plotted on the same scale with a parallel shift of axes, to estimate the aquifer parameters from the dual coordinates of a selected point on the matched portion of the graphs. The unimodal shape of the diagnostic curves and the guiding straight line facilitate the matching and limit the subjectivity. The proposed methods can easily identify a nonideal condition. The estimates of the aquifer parameters obtained using the proposed methods are more accurate than those obtained using the prior curve matching methods. The proposed methods are also able to identify nonideal conditions. It is hoped that the new methods will be of help to field and practicing engineers.     

Well Loss Estimation: Variable Pumping Replacing Step Drawdown Test

An optimization method is presented for simultaneous estimation of aquifer parameters and well loss parameters utilizing all the drawdowns observed during a variable rate pumping or multiple step pumping test. The proposed method does not require any graphical analysis. It is shown that a variable rate pumping test is a better substitute for the conventional step drawdown test to estimate well loss parameters. It suggests that the pumping rate may be changed frequently without waiting for a near steady state to be reached (or a selected duration, say 60 min) in each step of a conventional step drawdown test. This can result in a substantial saving of time and money involved in conducting a step drawdown test with a view to estimate well loss parameters. This gives a greater number of distinct discharges, which improves the estimates of the well loss parameters. Application of the method is demonstrated on published data sets, the results of which show that the parameters estimated using the new method are more reliable as compared to those obtained using prior methods.     

Estimating Aquifer Parameters from Early Drawdowns in Large-Diameter Wells

The existing equation applicable for large diameter wells in confined aquifers is transformed into a convenient form and a set of semilogarithmic diagnostic curves is developed for identifying the aquifer parameters (storage coefficient and transmissivity) from early drawdowns in large diameter wells. A scaled well function is proposed for the diagnostic curves. The aquifer parameters are estimated by matching the diagnostically plotted drawdowns to one of the diagnostic curves by a parallel shift of only one axis. The substantial curvature of the diagnostic curves and shifting of only one axis facilitate matching and reduce subjectivity. The proposed method is an improvement over the existing matching methods. The new method can reliably identify the aquifer parameters from only early drawdowns and would result in a 100-fold saving in time and money. It is hoped that this method would be helpful to field engineers and practitioners.     

Estimating Storage Coefficient and Transmissivity from Slug Test Data

A new accurate approximation for the well function for instantaneous charge of water for large diameter wells is proposed. Using this approximation with an optimization method, storage coefficient and transmissivity are estimated from slug test data on large diameter wells. The proposed method is void of the subjectivity of curve matching procedure.     

Simple Method for Quick Estimation of Leaky-Aquifer Parameters

Simple method and explicit equations are proposed for estimating the parameters of leaky aquifers from drawdown at an observation well, which avoid the curve matching or initial estimate of the parameter. The proposed method is computationally simple and the calculations can be performed even on a handheld calculator. The application of the methods is illustrated, using published data sets. The new method yields quick and accurate estimates of the leaky-aquifer parameters, if observed drawdowns do not contain large errors. The proposed method can also analyze the early drawdowns for accurate characteristics/parameters of a confined aquifer, if the conductance of the aquitard is assigned a zero value. It is hoped that the proposed method would be of help to field engineers and practitioners.     

Use of Genetic Algorithm in Optimization of Irrigation Pumping Stations

Energy costs constitute the largest expenditure for nearly all water utilities worldwide and can consume up to 65% of a water utility’s annual operating budget. One of the greatest potential areas for energy cost savings is in the scheduling of pump operations. This paper presents a new management model, WAPIRRA Scheduler, for the optimal design and operation of water distribution systems. The model makes use of the latest advances in genetic algorithm (GA) optimization to automatically determine annually the least cost of pumping stations while satisfying target hydraulic performance requirements. Optimal design and operation refers to selecting pump type, capacity, and number of units as well as scheduling the operation of irrigation pumps that results in minimum design and operating cost for a given set of demand curves. The optimization process consists of three main steps: (1) generating randomly an initial set of pump combinations to start the optimization process for a given demand-duration curve; (2) minimizing the total annual cost, which consists of operation and maintenance costs and depreciation cost of the initial investment, by changing the set and discharge of pump sets based on the provided model; and (3) achieving the final criterion to stop the optimization process and reporting the optimized results of the model. Computational analysis is based upon one major objective function and solving it by means of a computer program that is developed following the GA approach to find the optimized solution of generated equations. Application of the model to a real-world project shows considerable savings in cost and energy.     

Estimation of the Maximum Consumption of Permanganate by Aquifer Solids Using a Modified Chemical Oxygen Demand Test

Knowledge of the consumption of permanganate by naturally occurring reduced species associated with aquifer materials is required for site screening and design purposes to support permanganate in situ chemical oxidation (ISCO) applications. It has been established that this consumption is not a singled-valued quantity, but rather is kinetically controlled. Current methods to determine this permanganate natural oxidant demand (NOD) involve the use of well-mixed batch tests, which are time consuming and subject to test variables (e.g., concentration, mass of oxidant to solid ratio, reaction duration, and mixing conditions) that significantly affect the results. In this paper, we propose a modified chemical oxygen demand (COD) test using permanganate, which can be used to determine the maximum permanganate NOD of an aquifer material. As an initial point of comparison, we tested aquifer materials collected from eight potential ISCO sites using this modified or permanganate COD method, the traditional dichromate COD method, and a method based on well-mixed batch reactors. The results from this comparison indicated that there was no statistically significant difference (α = 5%) between the results of the permanganate COD test and the maximum NOD from the well-mixed batch reactors, while on average the dichromate COD test overestimated the maximum NOD by 100%. The permanganate COD test results were highly correlated to the batch-test maximum NOD data (r = 0.996), and to the total organic carbon and amorphous Fe content of the aquifer materials (r = 0.91). A limited sensitivity investigation of this proposed permanganate COD test revealed that the suspected formation of manganese oxides, a reaction byproduct, may lead to increased experimental variability. However, in spite of this concern we recommend that this proposed permanganate COD method is a quick and economical approach for estimating the maximum permanganate NOD for aquifer materials to support permanganate ISCO site screening and initial design purposes.     

Approximation of Well Function and Identification of Leaky Aquifer Parameters

The existing equation for leaky aquifers is transformed into a nondimensional form using new parameters and a scaled well function for leaky aquifers is proposed. A computationally simple function is developed for accurately approximating the scaled well function for the practical range of the parameters. Utilizing this function (approximation), an optimization method is proposed for identifying the leaky-aquifer parameters from observed drawdowns. The new function has an enhanced utility when a repetitive numerical evaluation of the well function for leaky aquifers is needed, e.g., while estimating the aquifer parameters using optimization or Kalman filter or artificial neural network methods. The application of the proposed method is illustrated using a few sets of published data. The proposed method outperforms the extended Kalman filter method, based on the reported results in the literature.     

Diagnostic Curves for Identifying Leaky Aquifer Parameters with or without Aquitard Storage

Two sets of unimodal diagnostic curves, one set assumes no aquitard storage and the other set assumes aquitard storage, are developed for identifying the parameter of leaky aquifers from early drawdowns, which yields accurate estimates of the parameters and lessens the subjectivity due to personal errors. The proposed diagnostic curve method is simple, easy to apply, and is based on matching of the diagnostically plotted observed drawdowns to an appropriate diagnostic curve. The new method is simple, easy to apply, does not require either the initial guess for the parameter values or repetitive evaluation of the leaky aquifer well function, and outperforms the conventional curve-matching, optimization, extended Kalman filter, and artificial neural network methods. The proposed set of diagnostic curves has a good diagnostic property and is able to easily identify nonideal conditions. The new method suggests a shorter duration pumping test, which would save time, money, and water. It is hoped that the proposed method would be useful to the field engineers and practitioners.     

Optimization of Open-Pit Mine Depressurization System Using Simulated Annealing Technique

Opencast mines operating in an area with dominant groundwater features may face hydrology-related problems such as heaving and bursting of the mine floor due to excessive uplift pressure. A proper groundwater control system has to be implemented to solve these problems. But the groundwater control system, which includes dewatering and depressurization wells may also create impacts on local groundwater flows. Therefore, an optimization-based development of the groundwater control system is required to ensure that local and regional hydrogeological impacts are within acceptable limits. This note presents a case study where an optimization program based on the simulated annealing technique was developed and applied to a three-dimensional seven-layer groundwater model. The calibrated groundwater flow model, which is based on MODFLOW, was used as the simulation component in the linked simulation-optimization model. The combined model was then used to identify the optimum depressurization strategy. The results show that this combined simulation and optimization methodology is a viable approach for solving large-scale groundwater management problems.     

Determination of Physical Parameters of Stiffened Plates using Genetic Algorithm

An investigation on stiffened isotropic and composite plates has been conducted to determine the geometric and material parameters for the plate, as well as the stiffener from experimental modal data and finite element predictions using a genetic algorithm (GA). The problem is formulated as a global minimization of the error function defined by the difference in undamped eigenvalues and eigenvectors, as predicted from the finite-element modeling to that obtained experimentally. The parameter estimation problem is solved using a GA implementing selection, crossover, and mutation operators to obtain the global minimum solution. Because stiffeners contribute substantially to the overall rigidity of the plate assembly, their position, physical properties, and orientation create considerable variation of the modal properties, as compared to the bare plate with similar construction. This makes each of the stiffened plate identification problems rather unique. GAs have been the subject of considerable interest in providing a robust search procedure for a global optimum solution for such difficult minimization problems. The method is demonstrated on a few simulated examples on stiffened plates to investigate the uniqueness and convergence of results. The methodology, although slow in execution, is found to be very robust, even in the presence of noise, for isolating interesting zones of the search space. Unlike many traditional optimization techniques, it does not get stuck at a particular local minimum due to its parallelism.     

Genetic Algorithms for the Calibration of Constitutive Models for Soils

This paper presents a study that uses an innovative numerical method called the “genetic algorithm” for material parameter optimization in constitutive modeling. The paper introduces a new scheme, a fitness function, to estimate the error of predicted behavior due to a given set of material parameters. Optimization efficiency of the proposed fitness function is analyzed by changing the selected genetic algorithm parameters. The genetic algorithm and the new fitness function were found to be capable of optimizing material parameters of complex constitutive models.     

Estimation of Unsaturated Hydraulic Parameters from Infiltration and Internal Drainage Experiments

Inverse problem of determining unsaturated soil hydraulic properties from transient infiltration and internal drainage events are analyzed. Hydraulic properties are assumed to be described by van Genuchten’s relationships. The inverse problem is solved using Levenberg-Marquardt method while the forward problem is solved using a mass conservative finite difference numerical scheme. The bias induced by different objective functions on the parameter estimates with error free and noisy data are analyzed. Field experiments are conducted at two sites to compare the parameter estimates obtained from the infiltration and internal drainage tests. The results indicate that some objective functions induce undue bias in the estimated parameters in the presence of noise in the data and as such selecting a suitable objective function should be given due importance in the parameter estimation. The comparison of the parameter estimates from infiltration and internal drainage experiments at two sites indicates that the parameter estimates are close to each other. It is concluded that infiltration experiment, which is simpler and of short duration can be an alternative to internal drainage experiment for estimating the unsaturated soil parameters.     

Flow Depletion Induced by Pumping Well from Finite Length of Stream

A procedure for calculating the flow depletion from a finite length of a stream induced by a pumping well in an adjacent aquifer is developed. Four management cases of finite length of the stream including a basic case are considered. A “basic flow depletion factor” is defined, in terms of which the flow depletion factors for all cases are expressed. The basic flow depletion factor is twice the Hantush M function. A computationally simple and accurate practical approximation of the basic flow depletion factor is presented that encompasses the full practical range of the solutions. Using this approximation, an optimization method is proposed for the estimation of the aquifer hydraulic diffusivity and effective distance from the pumping well to the line of recharge from the measured temporal variation of stream flow depletion between two sections. During optimization, repeated computation of stream flow depletion is required; use of the proposed approximation simplifies the computation.     

Global Optimization of Pavement Structural Parameters during Back-Calculation Using Hybrid Shuffled Complex Evolution Algorithm

In this paper, the use of a hybrid evolutionary optimization algorithm is proposed for global optimization of pavement structural parameters through inverse modeling. Shuffled complex evolution (SCE) is a population-based stochastic optimization technique combining the competitive complex evolution with the controlled random search, the implicit clustering, and the complex shuffling. Back-calculation of pavement layer moduli is an ill-posed inverse engineering problem, which involves searching for the optimal combination of pavement layer stiffness solutions in an unsmooth, multimodal, complex search space. SCE is especially considered a robust and efficient approach for global optimization of multimodal functions. A desirable characteristic of the SCE algorithm is that it uses information about the nature of the response surface, extracted using the deterministic Simplex geometric shape, to direct the search into regions with higher posterior probability. The hybrid back-calculation system described in this paper combines the robustness of the SCE in global optimization with the computational efficiency of neural networks and advanced pavement system characterization offered by employing finite-element models. This is the first time the SCE approach is applied to real-time nondestructive evaluation of pavement systems required in the routine maintenance and rehabilitation activities for sustainable transportation infrastructure.     

Effects of Local Search Algorithms on Groundwater Remediation Optimization Using a Self-Adaptive Hybrid Genetic Algorithm

Genetic algorithms allow solution of more complex, nonlinear civil, and environmental engineering problems than traditional gradient-based approaches, but they are more computationally intensive. One way to improve algorithm performance is through inclusion of local search, creating a hybrid genetic algorithm (HGA). The inclusion of local search helps to speed up the solution process and to make the solution technique more robust. This paper focuses on the effects of different local search algorithms on the performance of two different HGAs developed in previous phases of this research, the self-adaptive hybrid genetic algorithm (SAHGA) and the enhanced SAHGA. The algorithms are tested on eight test functions from the genetic and evolutionary computation literature and a groundwater remediation design case study. The results show that the selection of the local search algorithm to be combined with the simple genetic algorithm is critical to algorithm performance. The best local search algorithm varies for different problems, but can be selected prior to solving the problem by examining the reduction in fitness standard deviation associated with each local search algorithm, and the time distribution associated to the local search algorithm.