Data-worth analysis for multiobjective optimal design of pump-and-treat remediation systems

The design and the management of pump-and-treat (PAT) remediation systems for contaminated aquifers under uncertain hydrogeological settings and parameters often involve decisions that trade off cost optimality against reliability. Both design objectives can be improved by planning site characterization programs that reduce subsurface parameter uncertainty. However, the cost for subsurface investigation often weighs heavily upon the budget of the remedial action and must thus be taken into account in the trade-off analysis. In this paper, we develop a stochastic data-worth framework with the purpose of estimating the economic opportunity of subsurface investigation programs. Since the spatial distribution of hydraulic conductivity is most often the major source of uncertainty, we focus on the direct sampling of hydraulic conductivity at prescribed locations of the aquifer. The data worth of hydraulic conductivity measurements is estimated from the reduction of the overall management cost ensuing from the reduction in parameter uncertainty obtained from sampling. The overall cost is estimated as the expected value of the cost of installing and operating the PAT system plus penalties incurred due to violations of cleanup goals and constraints. The crucial point of the data-worth framework is represented by the so-called pre-posterior analysis. Here, the tradeoff between decreasing overall costs and increasing site-investigation budgets is assessed to determine a management strategy proposed on the basis of the information available at the start of remediation. The goal of the pre-posterior analysis is to indicate whether the proposed management strategy should be implemented as is, or re-designed on the basis of additional data collected with a particular site-investigation program. The study indicates that the value of information is ultimately related to the estimates of cleanup target violations and decision makers’ degree of risk-aversion.     

Worth of head data in well-capture zone design: deterministic and stochastic analysis

In this study, we examine the effects of conditioning spatially variable transmissivity fields using head and/or transmissivity measurements on well-capture zones. In order to address the challenge posed by conditioning a flow model with spatially varying parameters, an innovative inverse algorithm, the Representers method, is employed. The method explicitly considers this spatial variability.

A number of uniform measurement grids with different densities are used to condition transmissivity fields using the Representers method. Deterministic and stochastic analysis of well-capture zones are then examined. The deterministic study focuses on comparison between reference well-capture zones and their estimated mean conditioned on head data. It shows that model performance due to head conditioning on well-capture zone estimation is related to pumping rate. At moderate pumping rates transmissivity observations are more crucial to identify effects arising from small-scale variations in pore water velocity. However, with more aggressive pumping these effects are reduced, consequently model performance, through incorporating head observations, markedly improves. In the stochastic study, the effect of conditioning using head and/or transmissivity data on well-capture zone uncertainty is examined. The Representers method is coupled with the Monte Carlo method to propagate uncertainty in transmissivity fields to well-capture zones. For the scenario studied, the results showed that a combination of 48 head and transmissivity data could reduce the area of uncertainty (95% confidence interval) in well-capture zone location by over 50%, compared to a 40% reduction using either head or transmissivity data. This performance was comparable to that obtained through calibrating on three and a half times the number of head observations alone.     

Bioelectricity generation and remediation of sulfide contaminated tidal flat sediment

A dramatic decrease in the catch of shellfish has been observed due to the high amount of Acid Volatile Sulfide(AVS) in the tidal flats in Japan.In the current study,an evaluation of simultaneous bioelectricity generation and remediation of sulfide contaminated tidal flat sediment has been done.The sediment samples collected from Tokyo Bay and Yamaguchi Bay,Japan,have been used in the laboratory test.A 2 L cylindrical shaped Sediment Microbial Fuel Cell(SMFC) has been used to evaluate the remediation of both sediment samples in the laboratory.Three different electrode materials carbon felt,carbon fiber and bamboo charcoal were used in the experiments to compare their efficiency to reduce the AVS from the sediment and generate bioelectricity.It was observed that the AVS reduction was higher at 5 cm depths for the Tokyo Bay sediment(100%) compared to the Yamaguchi Bay sediment(60%).The larger grain size for the Tokyo Bay sediment was the probable reason for this.The maximum voltage was around 100 and80 mV for Tokyo Bay and Yamaguchi Bay,respectively.     

Simulation-based risk assessment of contaminated sites under remediation scenarios, planning periods, and land-use patterns—a Canadian case study

Risk assessment of contaminated sites is crucial for quantifying adverse impacts on human health and the environment. It also provides effective decision support for remediation and management of such sites. This study presents an integrated approach for environmental and health risk assessment of subsurface contamination through the incorporation of a multiphase multicomponent modeling system within a general risk assessment framework. The method is applied to a petroleum-contaminated site in western Canada. Three remediation scenarios with different efficiencies (0, 60, and 90%) and planning periods (10, 20, 40, 60, and 80 years later) are examined for each of the five potential land-use plans of the study site. Then three risky zones with different temporal and spatial distributions are identified based on the local environmental guidelines and the excess lifetime cancer risk criteria. The obtained results are useful for assessing potential human health effects when the groundwater is used for drinking water supply. They are also critical for evaluating environmental impacts when the groundwater is used for irrigation, stockbreeding, fish culture, or when the site remains the status quo. Moreover, the results indicate that the proposed method can effectively identify risky zones with different risk levels under various remediation actions, planning periods, and land-use patterns.     

Optimal design of soil dynamic compaction using genetic algorithm and fuzzy system

Computational intelligent techniques, such as fuzzy and genetic algorithm, have proven to be useful in modeling of complex nonlinear phenomena such as dynamic compaction. Dynamic compaction method is used to improve the mechanical behavior of underlying soil layers especially loose granular materials. The method involves the repeated impart of high-energy impacts to the soil surface using steel or concrete tampers with weights ranging 10–40 ton and with drop heights ranging 10–40 m. A relatively exact estimation of dynamic compaction level is of major concern to geotechnical engineers. This paper develops a fuzzy set base method for the analysis of dynamic compaction phenomenon. In this model, the input variables are tamper weight, height of tamper drop, print spacing, tamper radius, number of impact and soil layer geotechnical properties. The main shortcoming of this technique is uncertainty to locate the best sketch of dynamic compaction to gain maximum effect of this method of soil improvement. Therefore, this paper describes the incorporation of genetic algorithm methodology using fuzzy system for determining the optimum design of dynamic compaction. Subsequently, it will be shown that the genetic algorithm has some abilities in the optimization of dynamic compaction design. Also different manners of this algorithm are compared and then the optimized structure of genetic algorithm will be suggested for dynamic compaction.     

Flood-plain mapping: a critical discussion of deterministic and probabilistic approaches

Abstract

Different methodologies for flood-plain mapping are analysed and discussed by comparing deterministic and probabilistic approaches using hydrodynamic numerical solutions. In order to facilitate the critical discussion, state-of-art techniques in the field of flood inundation modelling are applied to a specific test site (River Dee, UK). Specifically, different flood-plain maps are derived for this test site. A first map is built by applying an advanced deterministic approach: use of a fully two-dimensional finite element model (TELEMAC-2D), calibrated against a historical flood extent, to derive a 1-in-100 year flood inundation map. A second map is derived by using a probabilistic approach: use of a simple raster-based inundation model (LISFLOOD-FP) to derive an uncertain flood extent map predicting the 1-in-100 year event conditioned on the extent of the 2006 flood. The flood-plain maps are then compared and the advantages and disadvantages of the two different approaches are critically discussed.

Citation Di Baldassarre, G., Schumann, G., Bates, P. D., Freer, J. E. & Beven, K. J. (2010) Flood-plain mapping: a critical discussion of deterministic and probabilistic approaches. Hydrol. Sci. J. 55(3), 364–376.     

Impact of offsite sediment transport and toxicity on remediation of a contaminated estuarine bay

Sediment of Ostrich Bay, an arm of Dyes Inlet on Puget Sound, was historically contaminated with ordnance compounds from an onshore US Navy facility. An initial recommendation for a sediment cover to mitigate benthic risks was followed by studies of sediment transport and deposition to determine whether contaminated sediment from Dyes Inlet or other offsite sources in Puget Sound may contribute to Ostrich Bay impacts. A Sediment Trend Analysis (STA) identified net sediment transport pathways throughout the bay and inlet by examining changes in grain size distributions in multiple adjacent samples. Results indicated that fine-grained sedimentary material transports into and deposits throughout the Dyes Inlet system, with no erosion or transport out of Ostrich Bay. Echinoderm larvae mortality bioassay results were elevated in fine-grained sediments of both Ostrich Bay and Dyes Inlet. Ordnance compounds were undetected, and although sediment mercury concentrations were elevated at 0.48-1.4 mg/kg in both waterbodies, the relationship with toxicity was weak. Results of the studies and sedimentation modeling indicate that impacted sedimentary material deposits throughout the Dyes Inlet/Ostrich Bay system from unknown sources and will prevent natural recovery of Ostrich Bay as well as negate long-term effectiveness of active remedial measures. Stakeholders have recognized that remediation of the bay can be achieved only after the toxicity of depositing sediment decreases.     

结构分灾抗震设计:概念和应用

详细论述了结构分灾抗震设计的产生背景、设计思想、优化模型和基本原则,指出结构分灾设计是在分析基于投资—效益准则的结构抗震设计模型的基础上,对工程实践中一些成功经验的提炼和概括而形成的设计方法,工程领域中一些现行设计方法和措施就是分灾设计的具体应用。当工程师们待处理的问题必须考虑高度不确定性因素时,将分灾设计作为一种可能选用的设计理念,将有助于工程师们实现设计创新。分灾设计符合基于性能的抗震设计思想,可以方便地实现基于性能的设计。     

量子遗传算法在大地电磁反演中的应用

量子遗传算法（QGA）以量子理论为基础,通过利用量子位编码代替经典遗传算法的二进制位编码,利用量子旋转门定向更新种群来代替传统方法中种群的选择、交叉和变异过程,使得算法具有一定的内在并行运算能力和量子的隧道效应,从而加快了搜索速度,改善了收敛速度,并具有更强的全局寻优能力.本文针对地球物理反演问题的非线性、多极值特点提出一套实现方案,通过理论模型和实测数据试验对比研究,表明量子遗传方法在大地电磁反演中的寻优质量和效果明显优于传统遗传算法.     

Rainfall stochastic disaggregation models: Calibration and validation of a multiplicative cascade model

The simulation of long time series of rainfall rates at short time steps remains an important issue for various applications in hydrology. Among the various types of simulation models, random multiplicative cascade models (RMC models) appear as an appealing solution which displays the advantages to be parameter parsimonious and linked to the multifractal theory. This paper deals with the calibration and validation of RMC models. More precisely, it discusses the limits of the scaling exponent function method often used to calibrate RMC models, and presents an hydrological validation of calibrated RMC models. A 8-year time series of 1-min rainfall rates is used for the calibration and the validation of the tested models. The paper is organized in three parts. In the first part, the scaling invariance properties of the studied rainfall series is shown using various methods (q-moments, PDMS, autocovariance structure) and a RMC model is calibrated on the basis of the rainfall data scaling exponent function. A detailed analysis of the obtained results reveals that the shape of the scaling exponent function, and hence the values of the calibrated parameters of the RMC model, are highly sensitive to sampling fluctuation and may also be biased. In the second part, the origin of the sensivity to sampling fluctuation and of the bias is studied in detail and a modified Jackknife estimator is tested to reduce the bias. Finally, two hydrological applications are proposed to validate two candidate RMC models: a canonical model based on a log-Poisson random generator, and a basic micro-canonical model based on a uniform random generator. It is tested in this third part if the models reproduce faithfully the statistical distribution of rainfall characteristics on which they have not been calibrated. The results obtained for two validation tests are relatively satisfactory but also show that the temporal structure of the measured rainfall time series at small time steps is not well reproduced by the two selected simple random cascade models.     

Optimal and sustainable extraction of groundwater in coastal aquifers

Four examples are investigated for the optimal and sustainable extraction of groundwater from a coastal aquifer under the threat of seawater intrusion. The objectives and constraints of these management scenarios include maximizing the total volume of water pumped, maximizing the profit of selling water, minimizing the operational and water treatment costs, minimizing the salt concentration of the pumped water, and controlling the drawdown limits. The physical model is based on the density-dependent advective-dispersive solute transport model. Genetic algorithm is used as the optimization tool. The models are tested on a hypothetical confined aquifer with four pumping wells located at various depths. These solutions establish the feasibility of simulating various management scenarios under complex three-dimensional flow and transport processes in coastal aquifers for the optimal and sustainable use of groundwater.     

Analysis of biodegradation in a 3-D heterogeneous porous medium using nonlinear stochastic finite element method

Probabilistic analysis by Monte Carlo Simulation method (MCSM) is a computationally prohibitive task for a reactive solute transport involving coupled PDEs with nonlinear source/sink terms in 3-D heterogeneous porous media. The perturbation based stochastic finite element method (SFEM) is an attractive alternative method to MCSM as it is computationally efficient and accurate. In the present study SFEM is developed for solving nonlinear reactive solute transport problem in a 3-D heterogeneous medium. Here the solution of the biodegradation problem involving a single solute by a single class of microorganisms coupled with dynamic microbial growth is attempted using this method. The SFEM here produces a second-order accurate solution for the mean and a first-order accurate solution for the standard deviation of concentrations. In this study both the physical parameters (hydraulic conductivity, porosity, dispersivity and diffusion coefficient) and the biological parameters (maximum substrate utilization rate and the coefficient of cell decay) are considered as spatially varying random fields. A comparison between the MCSM and SFEM for the mean and standard deviation of concentration is made for 1-D and 3-D problem. The effects of heterogeneity on the degradation of substrate and growth of biomass concentrations for a range of variances of input parameters are discussed for both 1-D and 3-D problems.     

混合被动控制优化设计及性能研究

本文将调谐液体阻尼器(TLD)和黏弹性阻尼器(VED)同时作用于结构,构成混合被动控制系统。通过对两类阻尼器分别进行优化设计并考虑两者间的相互影响,在充分发挥两者各自优良控制性能的同时,克服了VED大量使用导致控制系统整体造价过高的问题。算例分析表明,混合控制可以得到令人满意的整体减震效果,同时大大节约了VED用量,提高了控制系统的综合经济性能。     

Sensitivity analysis of two-dimensional steady-state aquifer flow equations. Implications for groundwater flow model calibration and validation

This paper presents the analytical properties of the sensitivity of the two-dimensional, steady-state groundwater flow equation to the flow parameters and to the boundary conditions, based on the perturbation approach. These analytical properties are used to provide guidelines for model design, model calibration and monitoring network design. The sensitivity patterns are shown to depend on the nature of both the perturbed parameter and the variable investigated. Indeed, the sensitivity of the hydraulic head to the hydraulic conductivity extends mainly in the flow direction, while the sensitivity to the recharge spreads radially. Besides, the sensitivity of the flow longitudinal velocity to the hydraulic conductivity propagates in both the longitudinal and transverse directions, whereas the sensitivity of the flow transverse velocity propagates in the diagonal directions to the flow. The analytical results are confirmed by application examples on idealized and real-world simulations. These analytical findings allow some general rules to be established for model design, model calibration and monitoring network design. In particular, the optimal location of measurement points depends on the nature of the variable of interest. Measurement network design thus proves to be problem-dependent. Moreover, adequate monitoring well network design may allow to discriminate between the possible sources of error.     

Application of an intelligent model developed from experimental data to building design for fire safety

Computational fluid dynamics (CFD) techniques are widely adopted to simulate the behavior of fire. However, CFD suffers from the shortcoming of requiring extensive computer storage and a lengthy computational time. In practical applications, although comprehensive field information on velocities, temperatures, pressure, and the fractions of different constitutes can be obtained from CFD simulations, the user may only be interested in few important parameters that index the performance of a compartment design in the event of a fire. The height of the thermal interface (HTI) is one such key index, and refers to the average height above floor level inside a fire compartment at which the temperature gradient is highest. In practice, a fire compartment is considered untenable when the HTI drops below the respiratory level of the occupants, and in optimizing the design of a fire system, another set of design parameters (e.g., the width of the door opening) must be examined if the HTI of a fire compartment design is evaluated by CFD as being too low. This trial and error exercise then continues until a close to optimum set of design parameters is achieved. This approach is theoretically feasible, but requires lengthy computational time. This paper proposes the application of an Artificial Neural Network (ANN) approach as a fast alternative to CFD models to simulate the behavior of a compartment fire. A novel ANN model named GRNNFA has been specially developed for fire studies. It is a hybrid ANN model that combines the General Regression Neural Network (GRNN) and Fuzzy ART (FA). The GRNNFA model features a network structure that grows incrementally, stable learning, and the absence of the noise embedded in experimental fire data. It has been employed to establish a system response surface based on the training samples collected from a full-scale experiment on compartment fire. However, as the available training samples may not be sufficient to describe the behavior of all systems, and especially those involving fire data, this paper proposes that extra knowledge be acquired from human experts. Human expert network training has thus been developed to remedy established system response surface problems. After transforming the remedied system response surface to the problem domain, a Genetic Algorithm (GA) is applied to evaluate the close to optimum set of design parameters.     

基于单台加速度记录的混合全局优化HVSR反演场地浅层速度结构

利用水平与竖向谱比（HVSR）方法反演场地速度结构是国际上迅速发展的研究领域.HVSR反演计算实质是一个土层场地模型空间搜索的全局优化问题,当模型搜索空间的复杂程度增大时,目前常用的搜索算法收敛速度慢,计算效率较低.本文实现了一种结合遗传和模拟退火方法优点的混合全局优化HVSR反演算法,通过理论模型和竖向台阵实测数据的检验,表明该算法能获得很好的反演效果,较好地解决了蒙特卡罗方法收敛速度慢,遗传算法收敛早熟和模拟退火算法搜索效率低的问题.本文在此基础上讨论了单台加速度S波记录用于场地速度结构HVSR反演的适用性,为基于单个地震台的地震观测记录反演浅层速度结构提供了一种高效且较为准确的反演方法.

     

The development of stochastic space transformation and diagrammatic perturbation techniques in subsurface hydrology

This paper develops concepts and methods to study stochastic hydrologic models. Problems regarding the application of the existing stochastic approaches in the study of groundwater flow are acknowledged, and an attempt is made to develop efficient means for their solution. These problems include: the spatial multi-dimensionality of the differential equation models governing transport-type phenomena; physically unrealistic assumptions and approximations and the inadequacy of the ordinary perturbation techniques. Multi-dimensionality creates serious mathematical and technical difficulties in the stochastic analysis of groundwater flow, due to the need for large mesh sizes and the poorly conditioned matrices arising from numerical approximations. An alternative to the purely computational approach is to simplify the complex partial differential equations analytically. This can be achieved efficiently by means of a space transformation approach, which transforms the original multi-dimensional problem to a much simpler unidimensional space. The space transformation method is applied to stochastic partial differential equations whose coefficients are random functions of space and/or time. Such equations constitute an integral part of groundwater flow and solute transport. Ordinary perturbation methods for studying stochastic flow equations are in many cases physically inadequate and may lead to questionable approximations of the actual flow. To address these problems, a perturbation analysis based on Feynman-diagram expansions is proposed in this paper. This approach incorporates important information on spatial variability and fulfills essential physical requirements, both important advantages over ordinary hydrologic perturbation techniques. Moreover, the diagram-expansion approach reduces the original stochastic flow problem to a closed set of equations for the mean and the covariance function.     

The development of stochastic space transformation and diagrammatic perturbation techniques in subsurface hydrology

This paper develops concepts and methods to study stochastic hydrologic models. Problems regarding the application of the existing stochastic approaches in the study of groundwater flow are acknowledged, and an attempt is made to develop efficient means for their solution. These problems include: the spatial multi-dimensionality of the differential equation models governing transport-type phenomena; physically unrealistic assumptions and approximations and the inadequacy of the ordinary perturbation techniques. Multi-dimensionality creates serious mathematical and technical difficulties in the stochastic analysis of groundwater flow, due to the need for large mesh sizes and the poorly conditioned matrices arising from numerical approximations. An alternative to the purely computational approach is to simplify the complex partial differential equations analytically. This can be achieved efficiently by means of a space transformation approach, which transforms the original multi-dimensional problem to a much simpler unidimensional space. The space transformation method is applied to stochastic partial differential equations whose coefficients are random functions of space and/or time. Such equations constitute an integral part of groundwater flow and solute transport. Ordinary perturbation methods for studying stochastic flow equations are in many cases physically inadequate and may lead to questionable approximations of the actual flow. To address these problems, a perturbation analysis based on Feynman-diagram expansions is proposed in this paper. This approach incorporates important information on spatial variability and fulfills essential physical requirements, both important advantages over ordinary hydrologic perturbation techniques. Moreover, the diagram-expansion approach reduces the original stochastic flow problem to a closed set of equations for the mean and the covariance function.     

Assessment of a capacity spectrum seismic design approach against cyclic and seismic experiments on full‐scale precast RC structures

Within the last decades, simplified methods alternative to dynamic nonlinear analysis have been developed to estimate the seismic performance of structures toward a performance‐oriented design. Considering drift as the main parameter correlated with structural damage, its estimation is of main importance to assess the structural performance. While traditional force‐based design deals with calibrated force reduction factors based on the expected structural ductility, other methods are based on the definition of a viscous damping factor defined as a function of the expected energy dissipated by the structure. An example is the capacity spectrum method. This method can be applied even without any a priori calibration or designer arbitrariness. This allows considering several peculiarities of the seismic behavior of precast structures, which may be influenced by nontraditional hysteresis of connections and members, interaction with the cladding panels, P‐δ effects, etc. The paper aims at verifying the soundness and accuracy of this method through the comparison of its predictions against the results of cyclic and pseudodynamic tests on precast structures, including single‐ and multistory buildings either stiff or flexible, obtained on full‐scale building prototypes tested within the framework of recent research projects (namely, “Precast Structures EC8,” “Safecast,” and “Safecladding”). Two simple methodologies of determination of the equivalent viscous damping from a force‐displacement cycle, based on the dissipated energy in relation to 2 different estimates of the elastic strain energy, are addressed and compared. Comments on the possible use of this procedure for the estimation of the seismic performance of precast structures are provided.