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Most decision-making problems in construction are complex and difficult to solve, as they involve multiple criteria and multiple decision makers in addition to subjective uncertainties, imprecisions and vagueness surrounding the decision-making process. In many instances, the decision-making process is based on linguistic terms rather than numerical values. Hence, structured fuzzy consensus-reaching processes and fuzzy aggregation methods are instrumental in multi-criteria group decision-making (MCGDM) problems for capturing the point of view of a group of experts. This chapter outlines different fuzzy consensus-reaching processes and fuzzy aggregation methods. It presents the background of the basic theory and formulation of these processes and methods, as well as numerical examples that illustrate their theory and formulation. Application areas of fuzzy consensus reaching and fuzzy aggregation in the construction domain are identified, and an overview of previously developed frameworks for fuzzy consensus reaching and fuzzy aggregation is provided. Finally, areas for future work are presented that highlight emerging trends and the imminent needs of fuzzy consensus reaching and fuzzy aggregation in the construction domain.

Simple expressions for upper and lower limits to the shear modulus of honeycomb sandwich cores are obtained by application of the Unit Displacement and Unit Load methods in conjunction with simplifying assumptions as to the strain and stress systems respectively in the core. The theory is given for cores built up from foil ribbons to form cells of general honeycomb form. Test methods for the experimental determination of the shear modulus are also discussed. Of these, the three‐point bending test on sandwich beams is considered most satisfactory and results of such tests on steel and aluminium foil honeycombs show good agreement with the theory.

Several different simulation techniques, such as discrete event simulation (DES), system dynamics (SD) and agent-based modelling (ABM), have been used to model complex construction systems such as construction processes and project management practices; however, these techniques do not take into account the subjective uncertainties that exist in many construction systems. Integrating fuzzy logic with simulation techniques enhances the capabilities of those simulation techniques, and the resultant fuzzy simulation models are then capable of handling subjective uncertainties in complex construction systems. The objectives of this chapter are to show how to integrate fuzzy logic and simulation techniques in construction modelling and to provide methodologies for the development of fuzzy simulation models in construction. In this chapter, an overview of simulation techniques that are used in construction is presented. Next, the advancements that have been made by integrating fuzzy logic and simulation techniques are introduced. Methodologies for developing fuzzy simulation models are then proposed. Finally, the process of selecting a suitable simulation technique for each particular aspect of construction modelling is discussed.

This chapter reviews recent theoretical developments and empirical research, to examine the causes and consequences of identity processes in relation to collaboration in work groups and group performance. Our central proposition is that identification in work groups can have beneficial as well as detrimental effects, depending on the nature of the shared identity, and the content of distinctive group norms. First, we examine some of the complications stemming from the fact that identification in work settings typically involves groups that can be defined at different levels of inclusiveness and where people can be seen as having multiple cross-cutting identities. Then, we move on to show that processes of identification affect the way people view their co-workers and supervisors, causing the same objective behavior to be interpreted and responded to in a fundamentally different way. Finally, we examine how normative expectations about prototypical group behavior determine group processes and group outcomes, with the consequence that identification and commitment can affect work motivation and collective performance in different ways, depending on the content of distinctive group norms.

AT Schiphol Airport the first Dutch airliner to be built since the war is nearing completion. The Royal Netherlands Aircraft Company Fokker, which was responsible for a large number of successful airliner designs in the period between the wars, has designed this latest aircraft as a medium size ‘Dakota replacement’. Called the Fokker F.27 Friendship, it carries 28–36 passengers and a liberal amount of luggage on stage lengths up to 1,000 miles. It is powered by two Rolls‐Royce Dart propeller turbine engines installed in the high‐aspect‐ratio high wing. The advanced design incorporates a number of interesting structural features which contribute to efficient production and economy of operation.

The computational drawbacks of existing numerical methods have forced researchers to rely on heuristic algorithms. Heuristic methods are powerful in obtaining the solution of optimization problems. Although they are approximate methods (i.e. their solution are good, but not provably optimal), they do not require the derivatives of the objective function and constraints. Also, they use probabilistic transition rules instead of deterministic rules. The purpose of this paper is to present an improved ant colony optimization (IACO) for constrained engineering design problems.

IACO has the capacity to handle continuous and discrete problems by using sub‐optimization mechanism (SOM). SOM is based on the principles of finite element method working as a search‐space updating technique. Also, SOM can reduce the size of pheromone matrices, decision vectors and the number of evaluations. Though IACO decreases pheromone updating operations as well as optimization time, the probability of finding an optimum solution is not reduced.

Utilizing SOM in the ACO algorithm causes a decrease in the size of the pheromone vectors, size of the decision vector, size of the search space, the number of function evaluations, and finally the required optimization time. SOM performs as a search‐space‐updating rule, and it can exchange discrete‐continuous search domain to each other.

The suitability of using ACO for constrained engineering design problems is presented, and applied to optimal design of different engineering problems.

BECAUSE of the apparently low moduli obtained from it, the double block shear test has recently been the subject of some criticism. The purpose of this paper is to re‐examine the double block shear test as a means of measuring the shear stiffness of foil honeycomb cores because of the advantages in simplicity it has over other methods. The two most widely used methods of testing are shown in FIG. 1, they are fundamentally different and illustrate the two approaches to honeycomb foil testing.

The purpose of this paper is to identify and prioritize the causes of delay in development projects and present strategies to reduce the delay.

The discussion includes the failure mode and effects analysis (FMEA) method for converting survey data, fuzzy risk priority number (FRPN) for prioritizing the delay factors, and the proposed solutions to reduce the delay of projects.

The research provided prioritized delay factors to show the importance of each delay factor in the projects by analyzing the survey data. Results show that lack of proper contract price to win the tender has the most impact on the delay of development project between delay factors.

This study offered some guidelines for people who are involved with contracts and development projects, so that they can use these suggestions to prevent possible factors that may cause delay in projects. This paper also demonstrated a process of analyzing the data which was provided by FMEA and FRPN methods.

Knowledge sharing has become necessary for organizations as it is a source of competitive advantage. Therefore, this study aims to investigate how empowering leadership through psychological empowerment encourages employees’ knowledge-sharing (KS) behavior. This study further explores the moderating role of learning goal orientation (LGO) between psychological empowerment and KS behavior.

This study collected data from 423 employees working in manufacturing and service organizations in two waves on convenience basis to tackle common method variance. The data were analyzed through structural equation modeling.

The results revealed that empowering leadership helps employees modify their KS behavior both directly and indirectly in the presence of psychological empowerment. Moreover, individuals high in LGO are more likely to involve in KS behavior when psychologically empowered.

This study used a cross-sectional design and suggests management focus on their working environment to enhance knowledge sharing among employees, which is possible through empowering leaders. The study further suggests management not ignore individual attributes during recruitment.

Drawing upon job characteristics model and social exchange theory, this study explores the mediating role of psychological empowerment between empowering leadership and KS behavior and the moderating role of LGO on the association between psychological empowerment and KS behavior.

The purpose of this paper is to propose variants of an adaptive penalty scheme for steady-state genetic algorithms applied to constrained engineering optimization problems.

For each constraint a penalty parameter is adaptively computed along the evolution according to information extracted from the current population such as the existence of feasible individuals and the level of violation of each constraint. The adaptive penalty method (APM), as originally proposed, computes the constraint violations of the initial population, and updates the penalty coefficient of each constraint after a given number of new individuals are inserted in the population. A second variant, called sporadic APM with constraint violation accumulation, works by accumulating the constraint violations during a given insertion of new offspring into the population, updating the penalty coefficients, and fixing the penalty coefficients for the next generations. The APM with monotonic penalty coefficients is the third variation, where the penalty coefficients are calculated as in the original method, but no penalty coefficient is allowed to have its value reduced along the evolutionary process. Finally, the penalty coefficients are defined by using a weighted average between the current value of a coefficient and the new value predicted by the method. This variant is called the APM with damping.

The paper checks new variants of an APM for evolutionary algorithms; variants of an APM, for a steady-state genetic algorithm based on an APM for a generational genetic algorithm, largely used in the literature previously proposed by two co-authors of this manuscript; good performance of the proposed APM in comparison with other techniques found in the literature; innovative and general strategies to handle constraints in the field of evolutionary computation.

The proposed algorithm has no limitations and can be applied in a large number of evolutionary algorithms used to solve constrained optimization problems.

The proposed algorithm can be used to solve real world problems in engineering as can be viewed in the references, presented in this manuscript, that use the original (APM) strategy. The performance of these variants is examined using benchmark problems of mechanical and structural engineering frequently discussed in the literature.

It is the first extended analysis of the variants of the APM submitted for possible publication in the literature, applied to real world engineering optimization problems.