Maximising the net social benefit of the construction of post‐disaster alternative housing projects

The widespread destruction that follows large‐scale natural disasters, such as Hurricane Katrina in August 2005, challenges the efficacy of traditional temporary housing methods in providing adequate solutions to housing needs. Recognising these housing challenges, the Congress of the United States allocated, in 2006, USD 400 million to the Department of Homeland Security to support Alternative Housing Pilot Programs, which are intended to explore the possibilities of providing permanent and affordable housing to displaced families instead of traditional temporary housing. This paper presents a new methodology and optimisation model to identify the optimal configurations of post‐shelter housing arrangements to maximise the overall net socioeconomic benefit. The model is capable of quantifying and optimising the impacts of substituting temporary housing with alternative housing on the social and economic welfare of displaced families as well as the required additional costs of doing so. An application example is presented to illustrate the use of the model and its capabilities.     

A fuzzy risk assessment approach for occupational hazards in the construction industry

The techniques in the construction industry have been improved due to the rapid development of science and technology. However, the constructional hazards are not decreased as expected. To reduce or prevent occupational hazards in the construction industry, a fuzzy risk assessment method was proposed to provide a prevention and improvement technique against occupational hazards. This method used two-stage quality function deployment (QFD) tables to represent the relationships among construction items, hazard types and hazard causes. A fuzzy analytic network process (ANP) method was developed to identify important hazard types and hazard causes. Failure modes and effect analysis (FMEA) was performed to assess the risk value of hazard causes based on the fuzzy inference approach. The proposed method was applied to a telecom engineering company in southern Taiwan. The performance evaluation result indicated that this method can provide satisfactory risk assessment values of hazard causes and relevant improvement strategies.     

Handling and updating uncertain information in bow-tie analysis

Bow-tie analysis is a fairly new concept in risk assessment that can describe the relationships among different risk control parameters, such as causes, hazards and consequences to mitigate the likelihood of occurrence of unwanted events in an industrial system. It also facilitates the performance of quantitative risk analysis for an unwanted event providing a detailed investigation starting from basic causes to final consequences. The credibility of quantitative evaluation of the bow-tie is still a major concern since uncertainty, due to limited or missing data, often restricts the performance of analysis. The utilization of expert knowledge often provides an alternative for such a situation. However, it comes at the cost of possible uncertainties related to incompleteness (partial ignorance), imprecision (subjectivity), and lack of consensus (if multiple expert judgments are used). Further, if the bow-tie analysis is not flexible enough to incorporate new knowledge or evidence, it may undermine the purpose of risk assessment.Fuzzy set and evidence theory are capable of characterizing the uncertainty associated with expert knowledge. To minimize the overall uncertainty, fusing the knowledge of multiple experts and updating prior knowledge with new evidence are equally important in addition to addressing the uncertainties in the knowledge. This paper proposes a methodology to characterize the uncertainties, aggregate knowledge and update prior knowledge or evidence, if new data become available for the bow-tie analysis. A case study comprising a bow-tie for a typical offshore process facility has also been developed to describe the utility of this methodology in an industrial environment.     

Prevalence-adjusted optimisation of fuzzy models for species distribution

Most performance criteria which have been applied to train ecological models focus on the accuracy of the model predictions. However, these criteria depend on the prevalence of the training set and often do not take into account ecological issues such as the distinction between omission and commission errors. Moreover, a previous study indicated that model training based on different performance criteria results in different optimised models. Therefore, model developers should train models based on different performance criteria and select the most appropriate model depending on the modelling objective. This paper presents a new approach to train fuzzy models based on an adjustable performance criterion, called the adjusted average deviation (aAD). This criterion was applied to develop a species distribution model for spawning grayling in the Aare River near Thun, Switzerland. To analyse the strengths and weaknesses of this approach, it was compared to model training based on other performance criteria. The results suggest that model training based on accuracy-based performance criteria may produce unrealistic models at extreme prevalences of the training set, whereas the aAD allows for the identification of more accurate and more reliable models. Moreover, the adjustable parameter in this criterion enables modellers to situate the optimised models in the search space and thus provides an indication of the ecological model relevance. Consequently, it may support modellers and river managers in the decision making process by improving model reliability and insight into the modelling process. Due to the universality and the flexibility of the approach, it could be applied to any other ecosystem or species, and may therefore be valuable to ecological modelling and ecosystem management in general.     

Embedding the human factor in road tunnel risk analysis

The paper is focusing on road tunnel safety and builds upon the Directive 2004/54/EC launched by the European Commission; the latter sets basic requirements and suggests the implementation of risk assessment in several tunnel cases apart from technical measures imposed on the basis of tunnel structural and operational characteristics. Since the EU Directive does not indicate the method for performing risk assessment, a wide range of methods have been proposed, most of them based on quantitative risk assessment (QRA). Although the majority of current road tunnel QRAs assess physical aspects of the tunnel system and consider several hazards concerning the transportation of dangerous goods through a tunnel, they do not take into account, sufficiently, several organizational and human-related factors that can greatly affect the overall safety level of these critical infrastructures. To cope with this limitation this paper proposes a fuzzy logic system based on CREAM method for human reliability analysis (Hollnagel, 1998) in order to provide more sophisticated estimations of the tunnel operator's performance in safety critical situations. It is deduced that a human reliability analysis component to analyze operator performance, like the fuzzy system proposed here, is important for risk analysts. Consideration of organizational and human factors will enhance risk analysts’ studies and highlight the uncertainty related to human performance variability.     

A discrete model for estimating the development time from egg to infecting larva of Ostertagia ostertagi parametrized using a fuzzy rule-based system

Ostertagia ostertagi is a nematode, predominantly affecting cattle in the Pampean region of Argentina. A mathematical model parametrized using fuzzy rule-based systems of the Takagi-Sugeno-Kant type (FTSK) for estimating the development time from egg to infecting larval stage L3 of the gastrointestinal parasite O. ostertagi is here proposed. The estimation of development time of O. ostertagi is essential for the generation of appropriate control mechanisms, since this provides information about the time when parasites are ready to migrate to pastures. For the purpose of reflecting the natural environmental conditions, the mean daily temperature is taken as the main and only regulator of the development time. Humidity conditions are considered to be sufficient for the normal development of the larvae. Hence the individual's daily growth is a function of its length and the mean temperature recorded on the previous day. It is expressed in terms of a difference equation with fuzzy parameters, which are defined using laboratory data. Model outputs are tested against results of field experiments. Simulation results are very satisfactory, yielding a mean estimation error (MEE) of 0.64 weeks, with variance 0.34, and a determination coefficient R² = 0.74. The model clearly exhibits an inverse relationship between development time and temperature both in controlled and in field conditions. It also exhibits a very sensitive response both to the order in which the temperature sequence occurs, - reproducing the differences observed between spring and autumn - and to the amplitude of the temperature range.     

Optimal feed profile for a second order reaction in a semi-batch reactor under safety constraints: Experimental study

Maximising the yield of the second order reaction (2-butanol+propionic anhydride) by manipulating the inlet flow rate is considered for an isothermal semi-batch process. First a procedure for the determination of the kinetic parameters using coupled spectroscopic and calorimetric methods is presented. Then an optimisation of the reaction system is performed numerically and verified experimentally. Constraints on the amount of heat produced and on the temperature attainable in the case of cooling failure are imposed for safety consideration.     

区域农业生态环境质量综合评价方法与模型研究

为了客观准确的评价区域农业生态环境质量,引入灰色系统理论中的关联度分析法,提出了区域生态环境质量的多级模糊综合评价——灰色关联优势分析复合模型,并以安徽省芜湖市区域农业生态环境质量的综合评价为例,根据芜湖市区域农业生态环境调查与监测资料,进行了实例研究。结果表明:安徽省芜湖市区域农业生态环境已经受到了明显的污染,并且各区域污染水平具有一定的差异;芜湖市区域农业生态环境质量优劣的关联序依次为:南陵县、芜湖县、繁昌县和城市郊区。      

A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider

Return of used products is becoming an important logistics activity due to government legislation and increasing awareness among the people to protect the environment and reduce waste. For industries, the management of return flow usually requires a specialized infrastructure with special information systems for tracking and dedicated equipment for the processing of returns. Therefore, industries are turning to third-party reverse logistics providers (3PRLPs). In this study, a multi-criteria group decision-making (MCGDM) model in fuzzy environment is developed to guide the selection process of best 3PRLP. The interactions among the criteria are also analyzed before arriving at a decision for the selection of 3PRLP from among 15 alternatives. The analysis is done through Interpretive Structural Modeling (ISM) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS). Finally the effectiveness of the model is illustrated using a case study on battery manufacturing industry in India.