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The fuzzy logic technique is one of the effective approaches for evaluating flexible and rigid pavement distress. The process of classifying pavement distress is usually performed by visual inspection of the pavement surface or using data collected by automated distress measurement equipment. Fuzzy mathematics provides a convenient tool for incorporating subjective analysis, uncertainty in pavement condition index, and maintenance-needs assessment, and can greatly improve consistency and reduce subjectivity in this process. This paper aims to develop a fuzzy logic-based system of pavement condition index and maintenance-needs evaluation for a pavement road network by utilizing pavement distress data from the U.S. and Canada. Considering rutting, fatigue cracking, block cracking, longitudinal cracking, transverse cracking, potholes, patching, bleeding, and raveling as input variables, the variables were fuzzified into fuzzy subsets. The fuzzy subsets of the variables were considered to have triangular membership functions. The relationships between nine pavement distress parameters and PCI were represented by a set of fuzzy rules. The fuzzy rules relating input variables to the output variable of sediment discharge were laid out in the IF–THEN format. The commonly used weighted average method was employed for the defuzzification procedure. The coefficient of determination ( R 2 ), root mean squared error (RMSE), and mean absolute error (MAE) were used as the performance indicator metrics to evaluate the performance of analytical models. [ABSTRACT FROM AUTHOR]

Condition assessment of aging infrastructure is a growing concern all over the world. A systematic performance assessment is a pre-requisite for effective asset and infrastructure management. Accordingly, this article develops an integrated approach for the performance assessment of the municipal infrastructure. The main objectives are to develop an approach to assess, measure, and combine the performance levels of water, sewer, and road infrastructure and then integrate those values into a performance index. The methodology involves developing assessment criteria from literature and using interviews with professionals. A series of workshops was conducted with professionals in Canada. Throughout the workshops, questionnaires were collected based on the analytic hierarchy process (AHP) technique. Using AHP and fuzzy set theory, data was aggregated, and the results were developed. The analysis proved that the road roughness rating has the highest impact on the performance level followed by the number of breaks that occurred within the water network. Furthermore, the developed integration model was validated by professional engineers working in the field and has shown potential in terms of providing a reliable assessment of the overall state of the infrastructure. [ABSTRACT FROM AUTHOR]

Climate change affects natural streamflow regimes globally. To assess alterations in streamflow regimes, typically temporal variations in one or a few streamflow characteristics are taken into account. This approach, however, cannot see simultaneous changes in multiple streamflow characteristics, does not utilize all the available information contained in a streamflow hydrograph, and cannot describe how and to what extent streamflow regimes evolve from one to another. To address these gaps, we conceptualize streamflow regimes as intersecting spectrums that are formed by multiple streamflow characteristics. Accordingly, the changes in a streamflow regime should be diagnosed through gradual, yet continuous changes in an ensemble of streamflow characteristics. To incorporate these key considerations, we propose a generic algorithm to first classify streams into a finite set of intersecting fuzzy clusters. Accordingly, by analyzing how the degrees of membership to each cluster change in a given stream, we quantify shifts from one regime to another. We apply this approach to the data, obtained from 105 natural Canadian streams, during the period of 1966 to 2010. We show that natural streamflow in Canada can be categorized into six regime types, with clear hydrological and geographical distinctions. Analyses of trends in membership values show that alterations in natural streamflow regimes vary among different regions. Having said that, we show that in more than 80 % of considered streams, there is a dominant regime shift that can be attributed to simultaneous changes in streamflow characteristics, some of which have remained previously unknown. Our study not only introduces a new globally relevant algorithm for identifying changing streamflow regimes but also provides a fresh look at streamflow alterations in Canada, highlighting complex and multifaceted impacts of climate change on streamflow regimes in cold regions. [ABSTRACT FROM AUTHOR]

Knowledge generation is often viewed as a direct outcome of spatial proximity or other social affinities between firms. In rejecting structural interpretations, this article emphasizes the crucial role of agency in orchestrating knowledge transfer and generation over space. We explore how firms strategically leverage the uneven geography of knowledge in international investments and identify four spatial knowledge strategies according to the direction of knowledge flows and mode of connection: knowledge replicating, scouting, connecting, and integrating. Drawing from a relational perspective, we develop four propositions to investigate how these strategies are configured in specific spatial settings. It is argued that replicating and scouting strategies occur from clusters to nonclusters and from nonclusters to clusters, respectively, while connecting and integrating strategies take place in cluster-to-cluster contexts. Adopting fuzzy set Qualitative Comparative Analysis (fsQCA), an investigation of forty-nine headquarters-subsidiary linkages between Canada and China substantiates the four knowledge strategies and their spatial configurations, and shows how spatial structure and agency are fundamentally intertwined and influence each other. [ABSTRACT FROM AUTHOR]

The concern for the relationship between demographic changes and asset markets has increased from beginning of 2000. Many researchers analyze the relationship between demographic changes and asset prices through regression models. Most of these studies apply linguistic terms for each different phase of the life cycle (e.g. late working-aged, elderly, adult, and middle-aged) and then define a specific behaviour for each of these cohorts. Although these terms are vague, all the researchers define them as a crisp set with crisp partitions. Additionally, fuzzy regression methods have attracted growing interest from researchers in various scientific, engineering, and humanities area due to the ambiguity in real data. The motivation of this research is that it is rational to consider and apply fuzzy sets to interpret these linguistic terms instead of the crisp partitions. In this study, we propose and apply a new approach in order to calculate the fuzzy frequency for the linguistic term, which can be useful in any other demographic study. Moreover, new fuzzy regression models are developed. These regression models, that are able to consider both fuzzy and crisp regression coefficients are developed based on applying a fuzzy distance concept in which the distance between two triangular fuzzy numbers (TFNs) or between a TFN and a crisp number is a TFN. Multi-objective optimization helps us to find the results without any compromise. The models are solved using the mathematical programming solver LINGO-16 to derive the fuzzy regression coefficients. We apply these models in a numerical example also in a real case study (fuzzy input, crisp output) in which an investigation on the relationship between fuzzy demographic dynamics and monetary aggregates is made. [ABSTRACT FROM AUTHOR]

The biological indicators are rarely used in CCME WQI, for the objective thresholds of most biological indicators are ambiguous. To solve this problem, this study establishes an improved CCME WQI model based on variable fuzzy set theory. The water quality of Wuli Lake is assessed as an illustration; and the result shows that its water quality condition is “fair”, and more measures should be adopted to control the internal phosphorus releasing and the reproduction of cyanobacteria in summer and autumn. Moreover, compared with the conventional CCME CQI, the improved CCME WQI is more comprehensive, for it not only takes the aquatic physic-chemical condition into consideration, but also introduces the biological indicators into evaluation. [ABSTRACT FROM AUTHOR]

Highlights: [•] We modeled three Atlantic salmon (Salmo salar) life stages habitat, using fuzzy logic. [•] Twenty-seven experts, from both sides of the Atlantic Ocean, defined fuzzy sets and fuzzy rules. [•] Median curves of standardized Weighted Usable Area (WUA) were calculated from the experts’ curves. [•] Confidence intervals around WUA curves were calculated by bootstrap resampling. [•] Predicted presence and absence HSI were tested by Student's t-test and RM-ANOVA. [Copyright &y& Elsevier]

Abstract: A fuzzy set theoretic approach for handling preference uncertainty within the paradigm of the Graph Model for Conflict Resolution is employed for systematically carrying out the strategic investigation of a conflict over the proposed export of water in bulk quantities. Following an overview of the literature regarding fuzzy preferences and their applications in decision making, the graph model is restructured to incorporate fuzzy preferences into calculations of stability. Nash and sequential stability definitions, which reflect human behavior in conflict, are modified to accommodate fuzzy preferences. The conflict over the potential large-scale export of water from Lake Gisborne, located in Canada’s Newfoundland and Labrador province, is modeled, assuming that one of the four Decision Makers (DMs) in the dispute has fuzzy or uncertain preferences, while the preferences of the remaining DMs are crisp. The strategic insights gained by varying the satisficing behavior of the DM with fuzzy preferences are discussed. [Copyright &y& Elsevier]

This paper presents a methodology for assessing spatial and temporal uncertainty associated with flood risk management. Traditional modelling approaches focus on either temporal or spatial variability, but not both. There is a need to understand the dynamic characteristics of flood risk and its spatial variability. The traditional two-dimensional (2D) fuzzy set, with one dimension for the universe of discourse and the other dimension for its membership degree, is not sufficient to handle both, spatial and temporal variation of flood risk. The theoretical foundation of this study is based on the development of a three-dimensional (3D) fuzzy set that includes flood risk variability in space and time. The proposed methodology extends the acceptance level of partial flood damage concept to a 3D representation and allows capturing change of decision makers' preferences in time and space. The main objective of the paper is to present an original methodology for flood risk management that is capable of (a) addressing uncertainty caused by spatial and temporal variability and ambiguity; (b) integrating objective and subjective risks; and (c) assisting flood management decision making based on a more detailed understanding of temporal and spatial variability of risk. Presented methodology is illustrated using the Red River flood of 1997 (Manitoba, Canada) as a case study. [ABSTRACT FROM AUTHOR]

This paper presents an effective Fuzzy Multi-Criteria Decision Analysis (FMCDA) approach for contaminated site management. The development was based on: selecting eight criteria for remedial alternative evaluation and determining the weight of criteria importance under uncertainty; addressing various uncertainty issues in remedial alternative assessment process using a fuzzy-set approach based on the questionnaire survey results; evaluating and ranking remedial alternatives by establishing a fuzzy evaluation matrix of remediation alternatives through comprehensively considering different stakeholder opinions and uncertainties within a general decision analysis framework. A case study on the remedial alternative selection for a contaminated site in western Canada is conducted to illustrate the efficiency and applicability of the developed approach. [ABSTRACT FROM AUTHOR]

The discharge and stage measurements in a river system are characterized by a number of sources of uncertainty, which affects the accuracy of a rating curve established from measurements. This paper presents a fuzzy set theory based methodology for consideration of different sources of uncertainty in the stage and discharge measurements and their aggregation into a combined uncertainty. The uncertainty in individual measurements of stage and discharge is represented using triangular fuzzy numbers, and their spread is determined according to the International Organization for Standardization (ISO) standard 748 guidelines. The extension principle based fuzzy arithmetic is used for the aggregation of various uncertainties into overall stage-discharge measurement uncertainty. In addition, a fuzzified form of ISO 748 formulation is used for the calculation of combined uncertainty and comparison with the fuzzy aggregation method. The methodology developed in this paper is illustrated with a case study of the Thompson River near Spences Bridge in British Columbia, Canada. The results of the case study show that the selection of number of velocity measurement points on a vertical is the largest source of uncertainty in discharge measurement. An increase in the number of velocity measurement points provides the most effective reduction in the overall uncertainty. The next most important source of uncertainty for the case study location is the number of verticals used for velocity measurements. The study also shows that fuzzy set theory provides a suitable methodology for the uncertainty analysis of stage-discharge measurements. [ABSTRACT FROM AUTHOR]

In this study, an inexact fuzzy chance-constrained two-stage mixed-integer linear programming (IFCTIP) approach is proposed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing inexact two-stage programming and mixed-integer linear programming techniques by incorporating uncertainties expressed as multiple uncertainties of intervals and dual probability distributions within a general optimization framework. The developed method can provide an effective linkage between the predefined environmental policies and the associated economic implications. Four special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it provides a linkage to predefined policies that have to be respected when a modeling effort is undertaken; secondly, it is useful for tackling uncertainties presented as intervals, probabilities, fuzzy sets and their incorporation; thirdly, it facilitates dynamic analysis for decisions of facility-expansion planning and waste-flow allocation within a multi-facility, multi-period, multi-level, and multi-option context; fourthly, the penalties are exercised with recourse against any infeasibility, which permits in-depth analyses of various policy scenarios that are associated with different levels of economic consequences when the promised solid waste-generation rates are violated. In a companion paper, the developed method is applied to a real case for the long-term planning of waste management in the City of Regina, Canada. [Copyright &y& Elsevier]

Since the use of pervious concrete pavement structures (PCPSs) is essentially still in the trial stage in Canada, long-term and quantitative pavement condition data are not available. The existing approaches applied to assess pervious concrete pavement structure (PCPS) conditions are ad hoc and suffer from methodological limitations. A fuzzy set technique is proposed herein as an efficient tool for dealing with qualitative and incomplete pavement condition data on distress types, severities, densities, and weighting factors. Using this method, a comprehensive fuzzy condition index was developed based on Ministry of Transportation of Ontario (MTO) methodology and using fuzzy pavement condition data. This fuzzy condition index was converted to a single value that allowed for comparisons of pavement conditions using several ranking techniques. A case study of 24 PCPS sites was utilized to demonstrate how the fuzzy representations of the condition index compared with associated single values. It is shown that this approach can effectively provide extensive condition indices for PCPSs and rank them accordingly, using only limited and imprecise pavement condition data. [ABSTRACT FROM AUTHOR]

The Canadian Forest Fire Danger Rating System (CFFDRS) is used daily across Canada for evaluating forest fire danger. Fuel-type information is one of the inputs required by the models used in the CFFDRS. In this project, three fuel-type maps with a 25 m resolution were produced for a pilot study area located in Alberta using land cover only; land cover and biomass; and, land cover, biomass and leaf area index data derived from satellite imagery. The relationships between inputs and fuel types were determined mainly by the opinions of forest fire scientists and incorporated into a computer program using fuzzy set methodology. Not all the CFFDRS fuel types could be distinguished using these inputs; three of the coniferous types had to be grouped into one common fuel type. Overall accuracy was between 74 and 83% based on ground-truth comparisons. The most accurate map resulted from land cover and biomass data. Detailed accuracy assessment indicated that the overall accuracy increased up to 86% if ambiguous fuel type identification was considered. No combination of inputs was able to define a fuel type with absolute certainty, which is a reflection of differing expert opinions and the small number of inputs used to produce the maps. [ABSTRACT FROM AUTHOR]

Predictive ecosite mapping involves developing computer models that consistently identify and map ecosystems. This method of predicting ecosystem occurrence on the landscape uses basic inventory information and expert knowledge, and is an effective integrated planning tool for providing a record of the location and spatial distribution of ecosystems within a management area. Fuzzy logic technology can be used to computerize essential elements of ecosystem identification, and the outputs can be linked to a Geographic Information System for map production. A pilot study was undertaken on the application of this technology to the Alberta Ecological Land Classification database and the resulting ecosite map for a township located in central Alberta (Tp42R9W5). The range of attributes used in the program was constrained by the attributes recorded on mapped polygons. Three maps with suitable attributes were available for the township studied: a Digitized Elevation Model map, an Alberta Vegetation Inventory map, and a reconnaissance soil survey map. Attributes of all polygons from all three maps were compiled and seven attributes (humus form, Ah thickness, surface texture, aspect, organic thickness, slope angle, and Alberta Vegetation Inventory moisture regime) were chosen to produce a computerized program for ecosite identification. Four sets of data were used to calibrate the program, as well as a small-plot data set collected from the township studied. The computer program was used to analyze the polygon data corresponding to two sets of data collected in the field and resulted in 72% and 70% similarity between the choices of experts and of the computer program. The quality of the original polygon attributes contributed to errors in identification. In addition, the reconnaissance soil survey map gave only an estimate of four attributes (Ah horizon thickness, organic thickness, humus form, and surface texture). [ABSTRACT FROM AUTHOR]

Proposes the use of fuzzy cluster membership weights for ranking species important in a classification of the sampling unit, using crustacean-zooplankton data from Lake Simcoe in Ontario. Identification of elements of interannual and spatial variability in the crustacean zooplankton; Other aspects of crustacean zooplankton community structure.

Discusses the use of the weights of evidence method based on fuzzy sets and fuzzy probabilities for mineral potential mapping. How the ordinary weights of evidence method is based on binary or ternary patterns of evidence; Use of geographic information systems for mineral potential mapping; Consideration of the probability of a unit area containing mineral deposits on the basis of the fuzzy evidence for the unit area; Application of the method to gold deposits in Meguma Terrane, Nova Scotia, Canada.

Occupant behavior in residential buildings has a direct impact on the effectiveness of energy-saving measures. In order to realize a buildings' carbon mitigation targets, the impact of individual occupancy profiles needs to be integrated with building simulation models. This paper introduces a decision support framework as a potential solution to make energy performance upgrade choices based on different occupancy profiles. This framework has been demonstrated through a case study of two single-family detached homes in Canada, which were highly instrumented with sensors for monitoring energy input and output. The case studies represented two common occupancy profiles-(1) a family of four (consisting of 2 working adults and 2 teenagers); and (2) a retired couple. Firstly, calibrated energy models were developed by using one-year energy use data collected through an intrusive load monitoring technique. Secondly, energy upgrade combinations were considered for each profile and tested for additional investment, payback period and greenhouse gas (GHG) emissions. Lastly, the most suitable combination of energy upgrade for each profile was ranked using a multi-criteria decision-making method (e.g., TOPSIS). Results indicated that the retired couple used more energy than the family of four and required energy upgrades with usually higher payback periods to achieve the same level of GHG emission reduction. The results of this research are timely for energy policymaking and developing best management practices, which need to be implemented along with the deployment of more stringent building standards and codes. • A scenario-based framework was developed for residential homes energy upgrades. • It considers occupant behavior and preference in selecting energy upgrades. • Field data is used to represent energy use of a family of four and a stay-at-home retired couple. • Energy upgrades are same under pro-economic choices for two profiles. • Energy upgrades vary under pro-environmental scenarios for two profiles. [ABSTRACT FROM AUTHOR]

Risk maturity evaluation is an efficient tool which can assist construction organizations in the identification of their strengths and weaknesses in risk management processes and in taking necessary actions for the improvement of these processes. The accuracy of its results relies heavily on the quality of responses provided by participants specialized in these processes across the organization. Risk maturity models reported in the literature gave equal importance to participants' responses during the model development, neglecting their level of authority in the organization as well as their level of expertise in risk management processes. Unlike the existing models, this paper presents a new risk maturity model that considers the relative importance of the responses provided by the participants in the model development. It considered their authority in the organization and their level of involvement in the risk management processes for calculating the relative weights associated with the risk maturity attributes. It employed an analytic network process (ANP) to model the interdependencies among the risk maturity attributes and utilizes the fuzzy set theory to incorporate the uncertainty associated with the ambiguity of the responses used in the model development. The developed model allows the construction organizations to have a more accurate and realistic view of their current performance in risk management processes. The application of the developed model was investigated by measuring the risk maturity level of an industrial partner working on civil infrastructure projects in Canada. [ABSTRACT FROM AUTHOR]