Hybrid Approach for Addressing Uncertainty in Risk Assessments

Parameter uncertainty is a major aspect of the model-based estimation of the risk of human exposure to pollutants. The Monte Carlo method, which applies probability theory to address model parameter uncertainty, relies on a statistical representation of available information. In recent years, other uncertainty theories have been proposed as alternative approaches to address model parameter uncertainty in situations where available information is insufficient to identify statistically representative probability distributions, due in particular to data scarcity. The simplest such theory is possibility theory, which uses so-called fuzzy numbers to represent model parameter uncertainty. In practice, it may occur that certain model parameters can be reasonably represented by probability distributions, because there are sufficient data available to substantiate such distributions by statistical analysis, while others are better represented by fuzzy numbers (due to data scarcity). The question then arises as to how these two modes of representation of model parameter uncertainty can be combined for the purpose of estimating the risk of exposure. This paper proposes an approach (termed a hybrid approach) which combines Monte Carlo random sampling of probability distribution functions with fuzzy calculus. The approach is applied to a real case of estimation of human exposure, via vegetable consumption, to cadmium present in the surficial soils of an industrial site located in the north of France. The application illustrates the potential of the proposed approach, which allows the uncertainty affecting model parameters to be represented in a way that is consistent with the information at hand. Also, because the hybrid approach takes advantage of the “rich” information provided by probability distributions, while retaining the conservative character of fuzzy calculus, it is believed to hold value in terms of a “reasonable” application of the precautionary principle.     

初轧机滑块式万向接轴叉头应力应变分析及计算机图形显示

本文采用ＡＬＧＯＲＦＥＡＳ微机有限元分析软件包对滑块万向联轴器中的关键受力部件－叉头进行了有限元分析。采用了切线矢量加载和边界单元的约束方法建立分析模型。由于采用了有限元分析的前后处理技术，计算模型和分析结果实现了自动生成和计算机图形显示。     

Predicting the Endpoint Phosphorus Content of Molten Steel in BOF by Two-stage Hybrid Method

A two-stage hybrid method is proposed to predict the phosphorus content of molten steel at the endpoint of steelmaking in BOF (Basic Oxygen Furnace). At the first clustering stage, the weighted K-means is performed to produce clusters with homogeneous data. At the second predicting stage, each fuzzy neural network is carried out on each cluster and the results from all fuzzy neural networks are combined to be the final result of the hybrid method. The hybrid method and single fuzzy neural network are compared and the results show that the hybrid method outperforms single fuzzy neural network.     

Quantifying Qualitative Information on Risks: Development of the QQIR Method

The method for quantifying qualitative information on risks (QQIR) bridges the gap between qualitative and quantitative risk assessment methods. It employs fuzzy set theory and results in deriving customized probability density functions (PDFs) for stochastic applications in risk assessment and financial modeling. The QQIR method uses fuzzy sets for capturing expert opinions on uncertain information and it uses the fuzzy weighted average method for aggregating that information. The aggregated opinion then is converted proportionally into a PDF with respect to the possibility–probability consistency principle and the uncertainty–invariance principle. This paper describes the construction of the proposed QQIR method and explains the underlying operations and principles used. The different competing possible methods and principles that exist in fuzzy set theory and could have been chosen for designing the QQIR method will be introduced and numerically tested in detail to determine which method best fits the purposes of making the QQIR method work. The paper refers to possible applications of the method that have been published by the writers and concludes with a summary and limitations of the QQIR method. The QQIR method is generic and has been successfully validated and applied to the impact of political risks on infrastructure projects.     

Jet Integral–Particle Tracking Hybrid Model for Single Buoyant Jets

The mixing processes of a buoyant jet discharged from a submerged single port were analyzed using a three-dimensional hybrid model. In the proposed hybrid model, the initial mixing was simulated by a jet integral method, and the advection-diffusion process was simulated using a particle tracking method. Laboratory experiments were conducted for various flow conditions in order to verify the proposed model. The simulated horizontal concentration distributions and minimum dilutions at the water surface were generally in agreement with the observations. The vertical concentration distributions for coflowing jets were well simulated by both the jet integral and the particle tracking methods of the hybrid model. Trajectories simulated by the jet integral module of the hybrid model were in agreement with the measured trajectories when the velocity ratio was low. For cases where the velocity ratio was high, the hybrid model in which the vortex-pair distribution was used gave better results than the hybrid model with only Gaussian velocity distribution.     

Stream Surface Strip Element Method and Simulation of Three-Dimensional Deformation of Continuous Hot Rolled Strip

A new method, the stream surface strip element method, for simulating the three-dimensional deformation of plate and strip rolling process was proposed. The rolling deformation zone was divided into a number of stream surface (curved surface) strip elements along metal flow traces, and the stream surface strip elements were mapped into the corresponding plane strip elements for analysis and computation. The longitudinal distributions of the lateral displacement and the altitudinal displacement of metal were respectively constructed to be a quartic curve and a quadratic curve, of which the lateral distributions were expressed as the third-power spline function, and the altitudinal distributions were fitted in the quadratic curve. From the flow theory of plastic mechanics, the mathematical models of the three-dimensional deformations and stresses of the deformation zone were constructed. Compared with the streamline strip element method proposed hy the first author of this paper, the stream surface strip element method takes into account the uneven distributions of stresses and deformations along altitudinal direction, and realizes the precise three-dimensional analysis and computation. The simulation example of continuous hot rolled strip indicates that the method and the model accord with facts and provide a new reliable engineering-computation method for the three-dimensional mechanics simulation of plate and strip rolling process.     

Modeling Blockage Failures in Sewer Systems to Support Maintenance Decision Making

The objective of this research is to develop and implement a stochastic method that can be applied to characterize random failures in critical infrastructure systems. We particularly focus on blockage failures in sewer systems that are nonmechanistic and result from combination of external factors, including deterioration in condition. The method was implemented using a data set consisting of sewer blockage failure records from a small municipality. Statistical tests were conducted to: (1) ensure that available data set is representative and (2) estimate parameters of distributions that appropriately characterize failure event arrival pattern. Failure trends were also analyzed to identify the influence of local factors and justify the choice of the distributions used to characterize interarrival times. Based on the analysis, we explored the challenges in developing a reliability model across the life cycle of a sewer system. In addition, specific examples were also presented to illustrate how the method can be applied to support system maintenance decisions. The results of this study illustrate how the memoryless property can be assumed in analyzing failure events, while explicitly considering context specific influences. Finally, the methods described in this paper are extensible and can be applied generally to analyzing random failures in other infrastructure systems as well.     

考虑采掘随机因素的采金船斗桥内力计算法

针对现行计算法的缺点和不足，详细分析斗桥的荷载，指出采掘力的随机有界变化特性，提出考虑最不刊荷载组合的内力计算法．配合使用推导出的受力分析计算公式，可较大减少运算工作量和出错的可能性，并给出某300L船斗桥的计算结果．得出的方法和实例可供采金部设计及现场生产工程师们参考．     

Bootstrap Technique for Risk Analysis with Interval Numbers in Bridge Construction Projects

Bridges are principal and vital transportation structures. If risk management is not considered in bridge construction projects, objectives cannot be delivered on time, on budget, or with suitable quality results. Risk data set sizes and experts’ judgments are not usually sufficient for analyzing significant risks in bridge construction projects; moreover, the statistical distributions for risk parameter estimates are usually unknown. Standard parametric statistical techniques cannot provide appropriate solutions for cases with small data sets or unknown distributions. This paper proposes a new hybrid approach by using a nonparametric resampling technique and interval computations for risk analysis, in particular, for bridge construction projects. Bootstrap techniques produce more accurate inferences for comparing parametric techniques and are an alternative when the underlying parametric assumptions are not considered. Increasingly, because of the complexity and uncertainty in decision making at bridge projects, it is easier or more natural to provide interval values for parts or all of decision-making judgments. Furthermore, the goal of reducing standard deviations for both risk probability and risk impact compared with the conventional approach is another conclusion of this paper. The proposed approach is applied to a case in Iran to show the validity of the approach.     

基于遗传算法的连续碳酸化分解过程模糊优化

提出了基于遗传算法的连续碳酸化分解过程操作模式模糊优化方法.首先建立基于模糊规则和模糊推理的模型,然后以稳态误差为优化目标,应用混合编码的遗传算法同时优化模糊控制隶属度和推理规则,将获得的模糊规则应用于末槽分解率的控制,获得了良好的应用效果.     

Hybrid Soft Computing Approach for Mining of Complex Construction Databases

The paper presents a hybrid soft computing system for mining of complex construction databases. The proposed approach hybridizes soft computing techniques, such as fuzzy logic, artificial neural networks (ANNs), and messy genetic algorithms (mGAs), to form a novel computational method for mining of human understandable knowledge from historical databases. The hybridization combines the merits of explicit knowledge representation of fuzzy logic decision-making systems, learning abilities of ANNs, and global search of mGAs. A hybrid soft computing system (HSCS) is developed for mining complex databases in construction with three characteristics: scarcity, incompleteness, and uncertainty. Real-world construction data repositories are selected to test the capabilities of the proposed HSCS for data-mining under the above-mentioned complex conditions. The testing results show the promising potential of the proposed HSCS for mining of complex databases in construction.     

基于周期势系统随机共振的轴承故障诊断

提出基于普通变尺度和周期势自适应随机共振理论,检测噪声背景下轴承滚动体的故障特征.在具体实施过程中,首先用普通变尺度的方法满足随机共振中小参数的条件,然后用随机权重粒子群优化算法作为自适应随机共振参数寻优的优化算法,同时用改进的信噪比作为评价指标.噪声背景下含轴承滚动体故障的实验信号经过普通变尺度下的自适应随机共振处理和优化后,微弱的故障特征可以有效的提取出来.将普通变尺度下的双稳态自适应随机共振和周期势自适应随机共振进行了对比,结果表明周期势自适应随机共振比双稳态自适应随机共振能进一步提高信噪比,并且比双稳态自适应随机共振迭代次数少,用时短.这说明提出的基于普通变尺度和周期势系统自适应随机共振的轴承滚动体故障诊断方法具有优越性,尤其是在工程实际中,故障监测所需的数据量大,计算时间长,如能较早的预警,可以提高诊断效率并减少不必要的损失.因此,这种轴承滚动体故障诊断方法对提高机械设备故障诊断效率具有参考价值.      

Quality control of dose volume histogram computation characteristics of 3D treatment planning systems

Detailed quality control (QC) protocols are a necessity for modern radiotherapy departments. The established QC protocols for treatment planning systems (TPS) do not include recommendations on the advanced features of three-dimensional (3D) treatment planning, like the dose volume histograms (DVH). In this study, a test protocol for DVH characteristics was developed. The protocol assesses the consistency of the DVH computation to the dose distribution calculated by the same TPS by comparing DVH parameters with values obtained by the isodose distributions. The computation parameters (such as the dimension of the computation grid) that are applied to the TPS during the tests are not fixed but set by the user as if the test represents a typical clinical case. Six commercial TPS were examined with this protocol within the frame of the EC project Dynarad (Biomed I). The results of the intercomparison prove the consistency of the DVH results to the isodose values for most of the examined TPS. However, special attention should be paid when working with cases of adverse conditions such as high dose gradient regions. In these cases, higher errors are derived, especially when an insufficient number of dose calculation points are used for the DVH computation.     

Integrated Modeling for Optimal Municipal Solid Waste Management Strategies under Uncertainty

In this study, an inexact fuzzy-probabilistic programming (IFPP) method is advanced for developing optimal municipal solid waste (MSW) management strategy with uncertain information. The IFPP can support the assessment of risk of violating constraints associated with fuzzy and random features. The developed method is applied to a case study of long-term MSW management planning in the city of Changchun, China. Violations for transfer-station capacity constraints are allowed under a range of probability and possibility levels, which are related to trade-offs between the system cost and the constraint-violation risk. The results indicate that useful solutions for planning the MSW management practices have been generated. They are valuable for supporting the identification of efficient waste-flow allocation patterns, the long-term capacity planning of the city’s waste management system, and the formulation of local policies regarding waste management under uncertainty.     

Overview of the Application of “Fuzzy Techniques” in Construction Management Research

During the last decade, “fuzzy techniques” have been increasingly applied to the research area of construction management discipline. To date, however, no paper has attempted to summarize and present a critique of the existing “fuzzy” literature. This paper, therefore, aims to comprehensively review the fuzzy literature that has been published in eight selected top quality journals from 1996 to 2005, these being Journal of Construction Engineering and Management, ASCE; Journal of Management in Engineering, ASCE; Construction Management and Economics; Engineering, Construction and Architectural Management; International Journal of Project Management; Building Research and Information; Building and Environment; and Benchmarking: An International Journal. It has been found that fuzzy research, as applied in construction management discipline in the past decade, can be divided into two broad fields, encompassing: (1) fuzzy set/fuzzy logic; and (2) hybrid fuzzy techniques, with the applications in four main categories, including: (1) decision making; (2) performance; (3) evaluation/assessment; and (4) modeling. The comprehensive review provided in this paper offers new directions for fuzzy research and its application in construction management. Based on a comprehensive literature review on the applications of fuzzy set/fuzzy logic, and hybrid fuzzy techniques in construction management research, an increasing trend of applying these techniques in construction management research is observed. Therefore, it is suggested that future research studies related to fuzzy techniques can be continuously applied to these four major categories. Fuzzy membership functions and linguistic variables in particular can be used to suit applications to solving problems encountered in the construction industry based on the nature of construction, which are widely regarded as complicated, full of uncertainties, and contingent on changing environments. Moreover, hybrid fuzzy techniques, such as neurofuzzy and fuzzy neural networks, can be more widely applied because they can better tackle some problems in construction that fuzzy set/fuzzy logic alone may not best suit. For example, neural networks are strong in pattern recognition and automatic learning while fuzzy set and fuzzy logic are strong in modeling certain uncertainties. Their combination can assist in developing models with uncertainty under some forms of pattern. Finally, an increasing trend of applying fuzzy techniques in the building science and environmental disciplines is also observed; it is believed that the application of fuzzy techniques will go beyond the construction management area into these disciplines as well.     

Multiobjective Optimal Fuzzy Logic Controller Driven Active and Hybrid Control Systems for Seismically Excited Nonlinear Buildings

The third generation benchmark control problem for seismically excited nonlinear buildings is an effort to evaluate the developed control strategies in order to apply them in field applications. As the fuzzy logic control systems have been applied effectively in various fields, including vibration control of structures, a multiobjective optimal fuzzy logic control system has been proposed in this paper. Two types of control devices, namely, active and hybrid, driven by a fuzzy logic controller (FLC) have been considered in the present study. Nondimensionalized peak interstory drift ratio and peak floor acceleration have been used as the two objective functions for the multiobjective optimal design problem. A two-branch tournament genetic algorithm has been used to find a set of Pareto-optimal solutions, as the optimization problem is not necessarily continuous or convex. Performance of the FLC driven active and hybrid control systems have been evaluated for all three third generation benchmark problems for seismically excited nonlinear buildings (3-, 9- and 20-story). Acceleration and velocity information of different floors have been used as feedback to the FLC. This approach provides a set of Pareto optimal designs, from which a controller design can be selected for the required performance. The FLC driven active control system performs better than the sample controller given in the benchmark problem. Though the number of sensors and control devices are far less, the performance of the hybrid is close to the active control system.     

New Point Estimates for Probability Moments

There are many areas of structural safety and structural dynamics in which it is often desirable to compute the first few statistical moments of a function of random variables. The usual approximation is by the Taylor expansion method. This approach requires the computation of derivatives. In order to avoid the computation of derivatives, point estimates for probability moments have been proposed. However, the accuracy is quite low, and sometimes, the estimating points may be outside the region in which the random variable is defined. In the present paper, new point estimates for probability moments are proposed, in which increasing the number of estimating points is easier because the estimating points are independent of the random variable in its original space and the use of high-order moments of the random variables is not required. By using this approximation, the practicability and accuracy of point estimates can be much improved.     

基于混合编码策略的电弧炉冶炼钢种的碳含量预报模型

提出一种混合编码策略的遗传算法(GA)训练电炉钢碳含量神经网络预报模型。先采用二进制编码策略,再采用十进制编码策略继续优化预报模型的权阈值,这种混合编码策略综合了二进制编码搜索能力强和十进制编码变异量可任意小的优点。仿真结果表明,混合编码策略的遗传算法(GA)具有更快的收敛速度和更好的寻优性能。对100 t电弧炉冶炼0.85%～1.00%C的钢种,预报碳含量的精度为±0.04%时混合编码GA的命中率为96%,二进制编码GA的命中率为90%。     

A robust sequential detection algorithm for cardiac arrhythmia classification

In [1] qnd [2] Thakor et al. describe a sequential probability ratio test (SPRT) based on threshold crossing intervals (TCI) for the discrimination of ventricular fibrillation (VF) from ventricular tachycardia (VT). However, in applying their algorithm to data from the MIT-BIH malignant arrhythmia database, we observed some overlap in the distributions of TCI for VF and VT resulting in 16% overall error rate for the discrimination. In this communication, we describe a modified SPRT algorithm, using a new feature dubbed blanking variability (BV) as the basis for discrimination. Using the MIT-BIH database, the preliminary results showed that the proposed method decreases the overall error rate to 5%.     

模糊C均值聚类算法在高炉料面分类中的应用

通过对多雷达扫描得到的高炉料面进行数据处理,根据数据的特征,分别采用模糊C均值聚类和特征加权模糊C均值聚类算法对料面数据进行分类,建立标准料面模型库.再通过模糊模式识别中贴近度的方法把待分类的目标料面与模型库相匹配,为后续的布料控制提供依据.该算法在某2500 m3高炉上进行了实验,取得良好的效果.仿真结果表明了其有效性.