Probabilistic Approach to Estimation of Urban Storm-Water TMDLs: Regulated Catchment

The present work is concerned with the development of a set of tools for the incorporation of various control measures—best management practices into an analytical probabilistic modeling approach for urban storm-water total maximum daily load (TMDL) estimation. Control measures are divided into two major groups—upstream and downstream, each requiring application of separate modeling principles elaborated in this paper. Applying Monte Carlo simulation to the developed set of expressions allows modeling the “end-of-pipe” parameters of urban storm-water discharges (runoff volume, discharge rate, and pollutant load) on an event average basis, as well as the stream parameters downstream of a storm-water discharge outlet. Model application is illustrated for a catchment regulated with an extended detention dry pond. Representative model results are presented, and a range of potential model applications is discussed. The capability to model the behavior of an urban storm-water system with the application of various control measures is the key precondition for the design of an optimal configuration of a water-protective strategy.     

Transitional Markov Chain Monte Carlo Method for Bayesian Model Updating, Model Class Selection, and Model Averaging

This paper presents a newly developed simulation-based approach for Bayesian model updating, model class selection, and model averaging called the transitional Markov chain Monte Carlo (TMCMC) approach. The idea behind TMCMC is to avoid the problem of sampling from difficult target probability density functions (PDFs) but sampling from a series of intermediate PDFs that converge to the target PDF and are easier to sample. The TMCMC approach is motivated by the adaptive Metropolis–Hastings method developed by Beck and Au in 2002 and is based on Markov chain Monte Carlo. It is shown that TMCMC is able to draw samples from some difficult PDFs (e.g., multimodal PDFs, very peaked PDFs, and PDFs with flat manifold). The TMCMC approach can also estimate evidence of the chosen probabilistic model class conditioning on the measured data, a key component for Bayesian model class selection and model averaging. Three examples are used to demonstrate the effectiveness of the TMCMC approach in Bayesian model updating, model class selection, and model averaging.     

Technical Performance Assessment of Urban Sewer Systems

Performance assessment in urban water infrastructures is an increasingly important field of knowledge. Performance has traditionally been expressed in a variety of ways relating mostly to local design practice, with hardly any consensus on how it should be measured or compared. The efficient technical management of these systems deserves a specific approach, suited to the methodologies regularly employed while planning, designing, constructing, operating, and maintaining the systems. At the engineering level, decisions are based on operational, physical, and resources data and on analyses deploying simulation models, geographic information systems, or other information systems. However, such tools tend to produce vast amounts of insufficiently aggregated or performance-oriented information. This paper presents a performance assessment system that is based on the decisional concept of utility functions and designed as a technical analysis tool with the purpose of shifting the focus of technical management of urban drainage systems to a performance-oriented view.     

Reliability-Based Performance Objectives and Probabilistic Robustness in Structural Control Applications

A reliability-based structural control design approach is presented that optimizes a control system explicitly to minimize the probability of structural failure. Failure is interpreted as the system’s state trajectory exiting a safe region within a given time duration. This safe region is bounded by hyperplanes in the system state space, each of them corresponding to an important response quantity. An efficient approximation is discussed for the analytical evaluation of this probability, and for its optimization through feedback control. This analytical approximation facilitates theoretical discussions regarding the characteristics of reliability-optimal controllers. Versions of the controller design are described for the case using a nominal model of the system, as well as for the case with uncertain model parameters. For the latter case, knowledge about the relative plausibility of the different possible values of the uncertain parameters is quantified through the use of probability distributions on the uncertain parameter space. The influence of the excitation time duration on feedback control design is discussed and a probabilistic treatment of this time duration is suggested. The relationship to H2 (i.e., minimum variance) controller synthesis is also examined.     

Assessment of Variable Uncertainties for Reliability-Based Design of Foundations

LRFD shows promise as a viable alternative to the present working stress design (WSD) approach to shallow foundation design. The key improvements of LRFD over the traditional WSD are the ability to provide a more consistent level of reliability and the possibility of accounting for load and resistance uncertainties separately. For LRFD to gain acceptance in geotechnical engineering, a framework for the objective assessment of resistance factors is needed. Such a framework, based on reliability analysis, is proposed in this paper. Probability density functions (PDFs), representing design variable uncertainties, are required for analysis. A systematic approach to the selection of PDFs is presented. A procedure such as that proposed provides a rational probabilistic basis for the development of LRFD methods in geotechnical engineering.     

Stochastic Analysis of a Single-Degree-of-Freedom Nonlinear Experimental Moored System Using an Independent-Flow-Field Model

Stochastic characteristics of the surge response of a nonlinear single-degree-of-freedom moored structure subjected to random wave excitations are examined in this paper. Sources of nonlinearity of the system include a complex geometric configuration and wave-induced quadratic drag. A Morison-type model with an independent-flow-field formulation and a three-term-polynomial approximation of the nonlinear restoring force is employed for its proven excellent prediction capability for the experimental results investigated. Wave excitations considered in this study include nearly periodic waves, which take into account the presence of tank noise, noisy periodic waves that have predominant periodic components with designed additive random perturbations, and narrow-band random waves. A unified wave excitation model is used to describe all the wave conditions. A modulating factor governing the degree of randomness in the wave excitations is introduced. The corresponding Fokker–Planck formulation is applied and numerically solved for the response probability density functions (PDFs). Experimental results and simulations are compared in detail via the PDFs in phase space. The PDFs portray coexisting multiple response attractors and indicate their relative strengths, and experimental response behaviors, including transitions and interactions, are accordingly interpreted from the ensemble perspective. Using time-averaged probability density functions as an invariant measure, probability distributions of large excursions in experimental and simulated responses to various random wave excitations are demonstrated and compared. Asymptotic long-term behaviors of the experimental responses are then inferred.     

Modeling Municipal Solid Waste Collection Systems Using Derived Probability Distributions.?I: Model Development

This paper presents a derived probability model that can be used to estimate vehicle and labor requirements for municipal solid waste collection systems. The model consists of closed-form solutions for important performance parameters such as the mean and variance of the total collection time for a route of given size. The results of the model agree well with the results of Monte Carlo–simulation models of curbside collection, but the derived probability model has several advantages over simulation techniques. The analytical nature of the model allows it to be easily and directly implemented on a spreadsheet. The proposed model will allow solid waste managers to examine a wide range of collection alternatives without the time and expense of simulation modeling.     

Probabilistic Seismic Demand Model for California Highway Bridges

A performance-based seismic design method enables designers to evaluate a graduated suite of performance levels for a structure in a given hazard environment. The Pacific Earthquake Engineering Research Center is developing a framework for performance-based seismic design. One component of this framework is a probabilistic seismic demand model for a class of structures in an urban region with a well-defined seismic hazard exposure. A probabilistic seismic demand model relates ground motion intensity measures to structural demand measures. It is formulated by statistically analyzing the results of a suite of nonlinear time-history analyses of typical structures under expected earthquakes in the urban region. An example of a probabilistic seismic demand model for typical highway bridges in California is presented. It was formulated using a portfolio of 80 recorded ground motions and a portfolio of 108 bridges generated by varying bridge design parameters. The sensitivity of the demand models to variation of bridge design parameters is also discussed. Trends derived from this sensitivity study provide designers with a unique tool to assess the effect of seismicity and design parameters on bridge performance.     

Proposed guidelines for the internal quality control of analytical results in the medical laboratory

The factors involved in analytical quality relate to definition of quality, creation of quality, and control of quality, and errors arise from external and internal sources as well as from permanent and variable factors. Further, the two main types of error are classified as systematic and random errors. Internal quality control (IQC) systems can only operate on the variable factors which are related to batch-to-batch variations (external factors) and to the performance in the laboratory (internal factors). In creating an adequate internal control system, several problems are faced: (i) quality of control materials, (ii) types and frequency of possible errors, (iii) number and types of control materials, (iv) number of replicates of the control, (v) probability of error detection, (vi) probability of false rejection, (vii) consequences of reject signals, (viii) trouble-shooting systems, and (ix) prevention of errors among many other conditions. Gaussian distributions of control results are assumed and the statistical control rules are evaluated in relation to probability of false rejections, Pfr, and probability of error detection, Ped, for the different rules. Combinations of low Pfr and high Ped are obtained by combining results from e.g. four measurements of the same control sample by use of mean and range rules. Further, it is not possible to establish a common control system which can be used for all quantities and analytical procedures; on the contrary, each procedure should have its particular efficient IQC system. These aspects are discussed and a number of guidelines for statistical control rules and problem related internal quality control are presented.     

Efficient strategies for genome scanning using maximum-likelihood affected-sib-pair analysis

Detection of linkage with a systematic genome scan in nuclear families including an affected sibling pair is an important initial step on the path to cloning susceptibility genes for complex genetic disorders, and it is desirable to optimize the efficiency of such studies. The aim is to maximize power while simultaneously minimizing the total number of genotypings and probability of type I error. One approach to increase efficiency, which has been investigated by other workers, is grid tightening: a sample is initially typed using a coarse grid of markers, and promising results are followed up by use of a finer grid. Another approach, not previously considered in detail in the context of an affected-sib-pair genome scan for linkage, is sample splitting: a portion of the sample is typed in the screening stage, and promising results are followed up in the whole sample. In the current study, we have used computer simulation to investigate the relative efficiency of two-stage strategies involving combinations of both grid tightening and sample splitting and found that the optimal strategy incorporates both approaches. In general, typing half the sample of affected pairs with a coarse grid of markers in the screening stage is an efficient strategy under a variety of conditions. If Hardy-Weinberg equilibrium holds, it is most efficient not to type parents in the screening stage. If Hardy-Weinberg equilibrium does not hold (e.g., because of stratification) failure to type parents in the first stage increases the amount of genotyping required, although the overall probability of type I error is not greatly increased, provided the parents are used in the final analysis.     

Community alternatives to psychotherapy.

Increasingly, individuals are turning to and being confronted by alternatives to dynamically-oriented therapy. Among these alternative change systems are alternative therapies such as Alcoholics Anonymous, and alternatives to therapy, such as the spiritual approaches based on various meditations. There are important theoretical conflicts between dynamic therapy and these alternatives. However, combinations of dynamic therapy and these alternatives are being forged by consumers of mental health services. In their effort to maximize the delivery of help, consumers make pragmatic decisions to construct such combinations, ignoring or deemphasizing theoretical conflicts or viewing them within a framework of complementarity. Research on these combinations is needed to learn more about which are effective with whom and for what purpose. (26 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Performance Assessment of a Precast-Concrete, Buried, Small Arch Bridge

Experimental field load-test and finite-element analysis were carried out for the performance assessment of a precast-concrete, modular, three-sided, low-profile, buried, arch bridge system. Finite-element analysis incorporated soil modeling and soil–structure interaction at service and limit load levels. The analytical study simulates step-by-step incremental phases of construction and service loads. The finite-element model was calibrated based on the experimental field assessment, to provide a better correlation between the analytically predicted behavior and the actual response of the structure. The study validates the incorporation of various soil models and soil–structure interaction characteristics, to allow a more cost-effective bridge design.     

Optimal Design in the Presence of Modeling Uncertainties

The integration of modeling and simulation tools with robust and efficient methods of optimal design offers a rational approach to explore new concepts and designs. However, a widespread adaptation of these tools in the industry design environment will require that they incorporate a systematic analysis of uncertainty in all aspects of the design process. A lack of confidence in designs generated in a simulation-based approach is the result of uncertainties in the predictive capabilities of physics-based models used in the simulations, and poor representation of uncertainties and their propagation in a coupled systems engineering design problem. A data- and knowledge-lean environment, typical of a design process involving novel concepts, further exacerbates the situation; design engineers often make gross assumptions about distributional information of random variables and parameters, thereby adding to the uncertainty associated with the design results. The paper focuses on numerical and analytical tools by which to model uncertainty and risk in a simulation-based design environment, including cases where the uncertainty does not conform to standard probabilistic distributions. A specific focus of the modeling effort is an approach to establish confidence intervals for response predictions available from analytical and numerical models, as well as surrogate approximations used in the design process. Innovative adaptations of formal optimization methods in a nondeterministic design setting are discussed, including design problem formulations that examine the nondeterministic design problem in a multicriteria optimization framework. Simple design problems are used to illustrate the concepts and to underscore the deficiencies in a purely deterministic approach to the design problem.     

Probabilistic Approach to Estimation of Urban Storm-Water Total Maximum Daily Loads: Unregulated Catchment

This work examines the basic processes and functions behind urban storm-water pollution delivery into surface waters and develops a set of tools that allow the estimation of pollutant load dynamics on receiving waters. In particular, the group of expressions developed in this paper allows the calculation of runoff parameters (volume, discharge rate and pollutant load) on an event average basis for an unregulated catchment. Using Monte Carlo simulation techniques, the runoff pollutant concentration probability distribution (as event averages) are obtained. Merging these runoff statistics with the stream parameters allows the receiving water pollutant concentration characteristics to be obtained as well as the probability of exceeding threshold pollutant concentrations in the mixing zone of a stream. The simulation can be performed with allowance for different levels of complexity with respect to catchment hydrologic representation and pollutant load functions. As a result, the magnitude of influence of urban runoff on a surface water body can be determined, pollutants of concern can be identified, and certain remedial measures recommended.     

Decision Making in Flexible Mine Production System Design Using Real Options

Large multifaceted capital projects, such as those in the mineral resource industry, are often associated with diverse sources of both internal and external risks and uncertainties. Risks can cause delays to the planned schedule of a project, add a significant cost, and greatly influence its profitability. Uncertainties can be associated with project risks, as well as with opportunities that can develop throughout the project’s lifecycle. Having the ability to plan for these uncertainties, by incorporating flexible alternatives into the system design, is increasingly recognized as critical to long-term corporate success. This paper advances the knowledge needed to incorporate flexibility in systems engineering and management for both practitioners and researchers. Flexibility is defined in this paper as the ability of a system to sustain performance, preserve a particular cost structure, adapt to internal or external changes in operating conditions, or take advantage of new opportunities that develop during a mine’s life cycle by modifying operational parameters. By engaging in planning for flexible production systems, the effects of risk on a particular project value can be examined, project volatility can be calculated, and potential flexible mining alternatives can be evaluated. Once identified, a real options valuation provides a strategic decision-making tool for mine planners to determine the value of incorporating flexible alternatives into the mine plan. This paper demonstrates that flexibility can become an equal partner among the parameters controlling the decision-making process for underground engineering construction systems, followed by industry practitioners. It presents a methodology in mine production system design by introducing flexibility into design through the application of real options valuation techniques. Real world case studies related to flexible planning and design of construction and production systems in underground hard rock mines are presented.     

Hidden neural networks

A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear performance gains compared to standard HMMs tested on the same task.     

Probabilistic Seismic Demand Models and Fragility Estimates for Reinforced Concrete Bridges with Two-Column Bents

Probabilistic models are developed to predict the deformation and shear demands due to seismic excitation on reinforced concrete (RC) columns in bridges with two-column bents. A Bayesian methodology is used to develop the models. The models are unbiased and properly account for the predominant uncertainties, including model errors, arising from a potentially inaccurate model form or missing variables, measurement errors, and statistical uncertainty. The probabilistic models developed are akin to deterministic demand models and procedures commonly used in practice, but they have additional correction terms that explicitly describe the inherent systematic and random errors. Through the use of a set of “explanatory” functions, terms that correct the bias in the existing deterministic demand models are identified. These explanatory functions provide insight into the underlying behavioral phenomena and provide a means to select ground motion parameters that are most relevant to the seismic demands. The approach takes into account information gained from scientific/engineering laws, observational data from laboratory experiments, and simulated data from numerical dynamic responses. The demand models are combined with previously developed probabilistic capacity models for RC bridge columns to objectively estimate the seismic vulnerability of bridge components and systems. The vulnerability is expressed in terms of the conditional probability (or fragility) that a demand quantity (deformation or shear) will be greater than or equal to the corresponding capacity. Fragility estimates are developed for an example RC bridge with two-column bents, designed based on the current specifications for California. Fragility estimates are computed at the individual column, bent, and bridge system levels, as a function of the spectral acceleration and the ratio between the peak ground velocity and the peak ground acceleration.     

湿地与城市湿地公园规划设计初探

详细阐述了湿地与城市湿地公园的基本概念、类型等,并介绍了城市湿地公园与各学科之间的联系,同时结合杭州西溪湿地公园规划的实例,对城市湿地公园规划要点、原则和规划进行了分析研究,为城市湿地公园规划提供了理论指导。     

Statistical Analysis on the Cost and Duration of Public Building Projects

A probabilistic model is proposed to predict the risk effects on time and cost of public building projects. The research goal is to utilize a real history data in estimating project cost and duration. The model results can be used to adjust floats and budgets of the planning schedule before project commencement. Statistical regression models and sample tests are developed using real data of 113 public projects. The model outputs can be used by project managers in the planning phase to validate the schedule critical path time and project budget. The comparison of means analysis for project cost and time performance indicated that the sample projects tend to finish over budget and almost on schedule. Regression models were developed to model project cost and time. The regression analysis showed that the project budgeted cost and planned project duration provide a good basis for estimating the cost and duration. The regression model results were validated by estimating the prediction error in percent and through conducting out-of-sample tests. In conclusion, the models were validated at a probability of 95%, at which the proposed models predict the project cost and duration at an error margin of ±0.035% of the actual cost and time.     

Reliability-Based Assessment of Suspension Bridges: Application to the Innoshima Bridge

Many suspension and cable-stayed bridges were designed and constructed between Honshu Island and Shikoku Island in Japan. All these bridges were designed according to the allowable stress design method. In the allowable stress design method, it is not possible to quantify the reliabilities of both bridge components and the entire bridge system. Therefore, in light of current reliability-based design philosophy, there is an urgent need to assess the safety of suspension bridges from a probabilistic viewpoint. To develop cost-effective design and maintenance strategies, it is necessary to assess the condition of suspension bridges using a reliability-based approach. This is accomplished by a probabilistic finite-element geometrically nonlinear analysis. This study describes an investigation into the reliability assessment of suspension bridges. The combination of reliability analysis and geometrically nonlinear elastic analysis allows the determination of reliabilities of suspension bridges. A probabilistic finite-element geometrically nonlinear elastic code, created by interfacing a system reliability analysis program with a finite-element program, is used for reliability assessment of suspension bridges. An existing suspension bridge in Japan, the Innoshima Bridge, is assessed using the proposed code. The assessment is based on static load effects. Reliabilities of the bridge are obtained by using 2D and 3D geometrically nonlinear models. Furthermore, damage scenarios are considered to assess the effects of failure of various elements on the reliability of undamaged components and on the reliability of the bridge. Finally, sensitivity information is obtained to evaluate the dominant effects on bridge reliability.