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Construction is a highly dynamic environment with numerous interacting factors that affect construction processes and decisions. Uncertainty is inherent in most aspects of construction engineering and management, and traditionally, it has been treated as a random phenomenon. However, there are many types of uncertainty that are not naturally modelled by probability theory, such as subjectivity, ambiguity and vagueness. Fuzzy logic provides an approach for handling such uncertainties. However, fuzzy logic alone has some limitations, including its inability to learn from data and its extensive reliance on expert knowledge. To address these limitations, fuzzy logic has been combined with other techniques to create fuzzy hybrid techniques, which have helped solve complex problems in construction. In this chapter, a background on fuzzy logic in the context of construction engineering and management applications is presented. The chapter provides an introduction to uncertainty in construction and illustrates how fuzzy logic can improve construction modelling and decision-making. The role of fuzzy logic in representing uncertainty is contrasted with that of probability theory. Introductory material is presented on key definitions, properties and methods of fuzzy logic, including the definition and representation of fuzzy sets and membership functions, basic operations on fuzzy sets, fuzzy relations and compositions, defuzzification methods, entropy for fuzzy sets, fuzzy numbers, methods for the specification of membership functions and fuzzy rule-based systems. Finally, a discussion on the need for fuzzy hybrid modelling in construction applications is presented, and future research directions are proposed.

Examines the growth of a new technology called fuzzy logic and its significance for microcontroller‐based embedded control solutions. Outlines the reasons for the emergence of fuzzy logic and explains the mathematic principles behind fuzzy set theory. Using the example of an oven temperature control system, describes how fuzzy logic is applied to the practical solution of a control problem rather than a conventional solution. Concludes that fuzzy logic has been used primarily in embedded control application as a software‐based methodology in closed‐loop control systems whilst a dedicated fuzzy hardware processor would optimally be based on a parallel architecture, allowing the entire rule base to be evaluated in a parallel fashion.

Sets out to provide an understanding of the theory of fuzzy logic by supplying background details concerning its evolution in mathematics and computer science. Once a basic understanding of the theory is obtained, then it is easier to understand the implications for computer applications. Fuzzy logic processors and compilers have facilitated the development of expert systems that typically use a lot of imprecise data. These expert systems have been used successfully as control units in industrial settings and as decision support systems in hospital settings. Fuzzy logic has been found to be a practical and viable form of artificial intelligence that mitigates the current drawbacks of other forms of artificial intelligence. But the really exciting development that is poised to emerge is the introduction of fuzzy logic appliances. These appliances employ an expert system on a chip that is able to mimic the range of flexibility of the human mind, while utilizing resources more efficiently.

The purpose of this paper is to explore the application of fuzzy logic in real estate investment in Hong Kong. There have been sufficient debates on the literature, providing the theoretical background on real estate investment decisions but there has been a lack of empirical support in this regard. This paper attempts to fill the gap between theorem and application.

The fuzzy logic system is adopted to evaluate the situation of a real estate market with imprecise and vague information. An indicator‐portfolio, rather than a specific indicator/index usually employed by practitioners, is explored to assist investors in risk management. The result derived from this framework is then compared to the property price index. This approach provides a framework in understanding the market without statistical and mathematical models. It tries to stimulate the complex human cognitive process involving decision making.

The housing‐indicator portfolio composition produces an outcome value which is able to reflect the complexities of both the real estate market and investors' expectations. An increase of this value implies that the investment condition is becoming more positive.

The paper reveals that fuzzy logic can provide some insights in an intuitive manner and is capable of obtaining information not found in market data. It is particularly useful to investors without experience in mathematical modeling.

This paper establishes a basic framework of fuzzy logic for real estate investment on which a base is formed as a reference for practitioners and investors. However, they should make references to the specific housing‐indicator portfolio composition in their own regions.

This paper has used a fuzzy logic system to assist practitioners as well as investors on decision making in real estate investment with imperfect market information. With the aid of the system, practitioners and investors are able to enhance their investment decision‐making quality by reducing the risk incurred by such uncertainties.

A plethora of studies focused on the cause and solutions for the bullwhip effect, and consequently many have successfully experimented to dampen the effect. However, the feasibility of such studies and the actual contribution for supply chain performance are yet up for debate. This paper aims to fill this gap by providing a holistic system-based perspective and proposes a fuzzy logic decision-making implementation for a single-product, three-echelon and multi-period supply chain system to mitigate such effect.

This study uses system dynamics (SD) as the central modeling method for which Vensim® is used as a tool for hybrid simulation. Further, the authors used MATLAB for undertaking fuzzy logic modeling and constructing a fuzzy inference system that is later on incorporated into SD model for interaction with the main supply chain structure.

This research illustrated the usefulness of fuzzy estimations based on experts’ linguistically and logically defined parameters instead of relying merely on the traditional demand forecasting based on time series. Despite the increased complexity of the calculations and structure of the fuzzy model, the bullwhip effect has been considerably decreased resulting in an improved supply chain performance.

This dynamic modeling approach is not only useful in supply chain management but also the model developed for this study can be integrated into a corporate financial planning model. Further, this model enables optimization for an automated system in a company, where decision-makers can adjust the fuzzy variables according to various situations and inventory policies.

This study presents a systemic approach to deal with uncertainty and vagueness in dynamic models, which might be a major cause in generating the bullwhip effect. For this purpose, the combination between fuzzy set theory and system dynamics is a significant step forward.

Auditors generally describe risk in terms of probabilities. Risk arises from lack of information which in turn leads to uncertainty. Since uncertainty exists when information is deficient and information can be deficient in different ways, it follows that auditors deal with different types of uncertainty. This article describes different types of uncertainty and a relatively new method of dealing with uncertainty referred to as fuzzy logic. Fuzzy logic and fuzzy set theory have contributed greatly to the development of artificial intelligence and have the potential to facilitate internal auditors’ measurement and management of risk and uncertainty in the audit environment.

Spaciousness is defined as “the feeling of openness or room to wander” that has been affected by various physical factors. The purpose of this paper is to assess the spaciousness of space to determine how spacious the space is. Furthermore, the study intends to propose a fuzzy-based model to assess the degree of spaciousness in terms of physical parameters such as area, proportion, the ratio of window area to floor area and color value.

Fuzzy logic is the most appropriate mathematical model to assess uncertainty using nonhomogeneous variables. In contrast to conventional methods, fuzzy logic depends on partial truth theory. MATLAB Fuzzy Logic Toolbox was used as a computational model including a fuzzy inference system (FIS) using linguistic variables called membership functions to define parameters. As a result, fuzzy logic was used in this study to assess the spaciousness degree of design studios in universities in the Iraqi Kurdistan region.

The findings of the presented fuzzy model show the degree to which the input variables affect a space perceived as larger and more spacious. The relationship between parameters has been represented in three-dimensional surface diagrams. The positive relationship of spaciousness with the area, window-to-floor area ratio and color value has been determined. In contrast, the negative relationship between spaciousness and space proportion is described. Moreover, the three-dimensional surface diagram illustrates how the changes in the input values affect the spaciousness degree. Besides, the improvement in the spaciousness degree of the design studio increases the quality learning environment.

This study attempted to assess the degree of spaciousness in design studios. There has been no attempt carried out to combine educational space learning environments and computational methods. This study focused on the assessment of spaciousness using the MATLAB Fuzzy Logic toolbox that has not been integrated so far.

The purpose of this paper is to present the flight control law based on the fuzzy logic control methods for Mars airplane, and the research emphasis is placed on the attitude hold and the command track using the fuzzy control.

The aircraft model is established with the combination of atmospheric environment, aerodynamic force and propulsive action. Then, the dynamic characteristics are analyzed in response to the different flight points for Mars airplane. Afterward, the flight control law is designed by applying the fuzzy logic theory to realize the attitude hold and the command track for Mars airplane.

The simulation results demonstrate that the proposed control law based on the fuzzy logic control methods is effective to guarantee system stability and relieve coupling dynamics. In addition, this control system can provide strong robustness and good tracking performance for Mars airplane.

The current work offers a new approach for the control law design of Mars airplane. The presented fuzzy control system can be applied to the other unconventional airplanes which will fly under unknown and uncertain environment to implement the complicated tasks such as deep space exploration.

This paper provides the new methods for Mars airplane to design the fuzzy control system which consists of three implementation steps: the fuzzy quantization control step, the fuzzy decoupling control step and the fuzzy attitude control step. Through the progressive design, this presented control system of Mars airplane has strongly nonlinear and robust control ability due to the application of the fuzzy expert concepts.

Companies deal with many decision‐making processes whose impact on the global performance can be very strong. As a consequence, the role of the decision support systems (DSSs) within the organization is critical. Considering the imprecise or fuzzy nature of the data in real‐world problems, it becomes obvious that the ability to manage uncertainty turns out to be a crucial issue for a DSS. In this framework, this paper discusses the key role of fuzzy logic (FL) in the DSSs, presents new applications of FL in DSSs in various sectors and identifies new challenges and new directions for further research.

This paper aims to provide a tool for decision makers to help them with selection of the appropriate supplier.

Companies often depend on their suppliers to meet customers' demands. Thus, the key to the success of these companies is selection of the appropriate supplier. A methodology is proposed to address this issue by first identifying the appropriate selection criteria and then developing a mechanism for their inclusion and measurement in the evaluation process. Such an evaluation process requires decision maker's preferences on the importance of these criteria as inputs.

Human assessments contain some degree of subjectivity that often cannot be expressed in pure numeric scales and requires linguistic expressions. To capture this subjectivity the authors have applied fuzzy logic that allows the decision makers to express their preferences/opinions in linguistic terms. Decision maker's preferences on appropriate criteria as well as his/her perception of the supplier performance with respect to these criteria are elicited. Fuzzy membership functions are used to convert these preferences expressed in linguistic terms into fuzzy numbers. Fuzzy mathematical operators are then applied to determine a fuzzy score for each supplier. These fuzzy scores are in turn translated into crisp scores to allow the ranking of the suppliers. The proposed methodology is multidisciplinary across several diverse disciplines like mathematics, psychology, and operations management.

The procedure proposed here can help companies to identify the best supplier.

The paper describes a decision model that incorporates decision maker's subjective assessments and applies fuzzy arithmetic operators to manipulate and quantify these assessments.