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This paper aims to cover the development of a methodology for hybrid fuzzy Monte Carlo agent-based simulation (FMCABS) and its implementation on a parametric study of construction crew performance.

The developed methodology uses fuzzy logic, Monte Carlo simulation and agent-based modeling to simulate the behavior of construction crews and predict their performance. Both random and subjective uncertainties are considered in model variables.

The developed methodology was implemented on a real case involving the parametric study of construction crew performance to assess its applicability and suitability for this context.

This parametric study demonstrates a practical application for the hybrid FMCABS methodology. Though findings from this study are limited to the context of construction crew motivation and performance, the applicability of the developed methodology extends beyond the construction domain.

This paper will help construction practitioners to predict and improve crew performance by taking into account both random and subjective uncertainties.

This paper will advance construction modeling by allowing for the assessment of social interactions among crews and their effects on crew performance.

The developed hybrid FMCABS methodology represents an original contribution, as it allows agent-based models to simultaneously process all types of variables (i.e. deterministic, random and subjective) in the same simulation experiment while accounting for interactions among different agents. In addition, the developed methodology is implemented in a novel and extensive parametric study of construction crew performance.

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.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

Both system dynamics (SD) and agent-based modeling (ABM) have been used in simulation-based group dynamics research. To combine the advantages of both simulation approaches, the concept of SD-ABM hybrid simulation has been proposed. However, research efforts to compare the effectiveness of modeling approaches between the hybrid and non-hybrid models in the context of group dynamics study are rare. Against this background, this study aims to propose an agent-embedded SD (aeSD) modeling approach and demonstrate its advantages when compared to pure SD or ABM modeling approaches, based on a research case on construction workers’ social absenteeism.

The authors introduce an aeSD modeling approach to incorporate individual attributes and interactions among individuals in an SD model. An aeSD model is developed to replicate the behavior of an agent-based model previously developed by the authors to study construction workers’ group behavior regarding absenteeism. Then, the characteristics of the aeSD model in comparison with a pure ABM or SD model are demonstrated through various simulation experiments.

It is demonstrated that an aeSD model can capture the diversity of individuals and simulate emergent system behaviors arising from interactions among heterogeneous agents while holding the strengths of an SD model in identifying causal feedback loops and policy testing. Specifically, the effectiveness of the aeSD approach in policy testing is demonstrated through examples of simulation experiments designed to test various group-level and individual-level interventions to control social absence behavior of workers (e.g. changing work groupings, influencing workgroup networks and communication channels) under the consideration of the context of construction projects.

The proposed aeSD modeling method is a novel approach to how individual attributes of agents can be modeled into an SD model. Such an embedding-based approach is distinguished from the previous communication-based hybrid simulation approaches. The demonstration example presented in the paper shows that the aeSD modeling approach has advantages in studying group dynamic behavior, especially when the modeling of the interactions and networks between individuals is needed within an SD structure. The simulation experiments conducted in this study demonstrate the characteristics of the aeSD approach distinguishable from both ABM and SD. Based on the results, it is argued that the aeSD modeling approach would be useful in studying construction workers’ social behavior and investigating worker policies through computer simulation.

The deviant behavior (DB) of construction workers has always been a troubling event in project management. Although scholars continue to search for the main causes of this behavior to curb it at the source, the authors know less about the role and contribution of the team. This study aims to uncover the mechanisms and conditions under which collective moral judgment focus on self (CMJS) effectively enhances DB.

Adopting Chinese construction enterprises as samples, a hierarchical linear model (HLM) is used to test the results of the hypothesis. Moderated mediating effects are used to analyze the potential mechanisms and boundary conditions of DB.

The results of the HLM analysis show that CMJS could directly and significantly induce DB, and moral disengagement (MD) plays a mediator role in this association. In addition, the positive relationship between MD and DB is stronger when performance-avoidance goal orientation (PaGO) or overqualification (Overq) is higher.

The conditions and mechanisms that influence DB are not unique. Future study could examine the explanatory and weighting mechanisms of DB from other perspectives or to construct a framework and summarize the factors that may influence DB.

This study provides a rich theoretical basis for the prevention and correction of construction workers' DB in Chinese construction firms from the perspective of CMJS. In addition, objective moral judgments contribute to guiding employees' moral cognitive processes and positive work.

This study extends existing research on DB and advances the practical outcomes of construction project governance. It not only illustrates that CMJS has a direct impact on DB but also clarifies the mechanisms and conditions that predispose to the generation of DB, filling the research gap on construction workers' DB from cross-level mechanisms and also enriching the theoretical system for preventing this behavior.

Previous studies have highlighted that communication barrier was one of the major safety problems faced by ethnic minority (EM) workers. This study aims to model the predominant safety communication networks of EM crews and explore the relationships among safety communication networks, individual attributes, safety climate, near misses and injuries of EM crews.

Case studies were conducted with EM crews in the Hong Kong construction industry. Demographic attribute, network, safety climate and accidents data were collected through questionnaires and analyzed by a combination of social network analysis (SNA), cross-case comparison and nonparametric tests.

The results revealed that language proficiency, network density and level of reciprocity were contributing factors of distinguishing high and low safety performing EM crews. EM management received more safety information from EM workers than local management. The centrality of EM workers was significantly related to their age, the perceived priority of safety and language ability.

The research findings regarding the impact of safety communication network characteristics on the safety performance of EM crews provides insights to employers on how to cultivate effective safety communication patterns within EM crews that can lead to better safety performance. The connections between personal attributes and their positions in safety communication networks could help the employers identify the EM workers who are positioned on edges of networks and need more attention.

This study contributes to knowledge by enriching the limited research on analyzing safety communication of small construction crews using SNA and expanding the research object to EM construction crews in the literature, who are more vulnerable to construction accidents. This research also extends the existing body of knowledge from studies mainly carried out in Western culture to Eastern culture. Although safety communication has been regarded as important for EM workers, there is a lack of quantitative analysis on this at a crew level. The present study provides empirical research to reveal authentic safety communication networks and their connections with safety performance and personal attributes.

The purpose of this paper is to focus on productivity as it unfolds during the execution of a particular task, i.e., reinforced concrete operations. The main aim is understanding whether the learning effect explaining the improvement of productivity in subsequent cycles of a given repetitive construction process is mainly attributable to a pure worker learning (independent on the specific site) or to the experience developed by the crew on the site conditions.

The authors conduct a research that empirically investigates and compares the change in productivity data of a single worker during his/her working life and that of a crew involved in specific repetitive work, such as the concreting activities of a multi-storey building.

The findings suggest differentiating between productivity gain as a result of the learning effect of the individual worker throughout his/her working life (which is independent of the specific project and site) and that of a crew composed by more workers which repeat reinforced concrete operations in a given specific project.

Despite the great attention reserved to learning in construction, few researchers discuss on the real applicability of the learning curve (LC) theory in the construction industry. The authors contribute to this literature by empirically investigating the contributions that the learning effect of the individual worker and that of a crew repeating a given task (i.e. reinforced concrete operations) in a given project have on the productivity improvement for subsequent cycles of the repetitive construction process.

The findings of this study have important managerial implications. The shape of the LC of the individual worker implies that learning increases relatively slowly in his/her working life (particularly after one to two years), while the effects of the crew experience are immediately significant in a time range of few weeks. This means that a single “one-off” multi-storey building project will show in the first storey the “historical,” individual productivity of the individual workers (i.e. not going to vary significantly in the next few weeks). The productivity improvement in the further storeys will only depend on the project-specific (and collective, for the crew) “learning” due, for example, to better coordination or to other issues that are progressively solved moving from the first storey to the following ones. So, the project-specific LC increases in a faster way than the individual one, and the overall productivity can be improved by accelerating the project-specific learning rate with more accurate project-specific design and management.

This paper enhances the understanding of the contributions that the learning effect of the individual worker and that of a crew repeating a given task (i.e. reinforced concrete operations) in a given project have on the productivity improvement for subsequent cycles of the repetitive construction process. This will contribute to improve the planning and control of site work activities, avoiding time and money wastefulness.

This paper aims to present a highly accessible and affordable tracking model for earthmoving operations in an attempt to overcome some of the limitations of current tracking models.

The proposed methodology involves four main processes: acquiring onsite terrestrial images, processing the images into 3D scaled cloud data, extracting volumetric measurements and crew productivity estimations from multiple point clouds using Delaunay triangulation and conducting earned value/schedule analysis and forecasting the remaining scope of work based on the estimated performance. For validation, the tracking model was compared with an observation-based tracking approach for a backfilling site. It was also used for tracking a coarse base aggregate inventory for a road construction project.

The presented model has proved to be a practical and accurate tracking approach that algorithmically estimates and forecasts all performance parameters from the captured data.

The proposed model is unique in extracting accurate volumetric measurements directly from multiple point clouds in a developed code using Delaunay triangulation instead of extracting them from textured models in modelling software which is neither automated nor time-effective. Furthermore, the presented model uses a self-calibration approach aiming to eliminate the pre-calibration procedure required before image capturing for each camera intended to be used. Thus, any worker onsite can directly capture the required images with an easily accessible camera (e.g. handheld camera or a smartphone) and can be sent to any processing device via e-mail, cloud-based storage or any communication application (e.g. WhatsApp).

For parts of the time on a typical construction site concrete pour, the site placing crew is idle waiting for the arrival of the next truckmixer delivery, whereas for other periods, truckmixers are idle on site waiting to be unloaded. Ideally, the work of the crew should be continuous, with successive truckmixers arriving on site just as the preceding truckmixer has been emptied, to provide perfect matching between site and concrete plant resources. However, in reality, sample benchmark data, representing 118 concrete pours of 69 m³ average volume, illustrate that significant wastage occurs of both crew and truckmixer time. The purpose of this paper is to present and explain the characteristics of the wastage pattern observed and provide further understanding of the effects of the factors affecting the productivity of this everyday routine site concreting system.

Analytical algebraic models have been developed applicable to both serial and circulating truckmixer dispatch policies. The models connect crew idle time, truckmixer waiting time, truckmixer round trip time, truckmixer unloading time and truckmixer numbers. The truckmixer dispatch interval is another parameter included in the serial dispatch model. The models illustrate that perfect resource matching cannot be expected in general, such is the sensitivity of the system to the values applying to those parameters. The models are directly derived from theoretical truckmixer and crew placing time-based flow charts, which graphically depict crew and truckmixer idle times as affected by truckmixer emptying times and other relevant parameters.

The models successfully represent the magnitudes of the resource wastage seen in real life but fail to mirror the wastage distribution of crew and truckmixer time for the 118 pour benchmark. When augmented to include the simulation of stochastic activity durations, however, the models produce pour combinations of crew and truckmixer wastage that do mirror those of the benchmark.

The basic contribution of the paper consists of the proposed analytical models themselves, and their augmented versions, which describe the site and truckmixer resource wastage characteristics actually observed in practice. A further contribution is the step this makes towards understanding why such an everyday construction process is so apparently wasteful of resources.

The main objective of this research is to identify the most important human resource management (HRM) practices, which have the potential to enhance labour productivity using fuzzy synthetic evaluation approach.

The study used a mixed-methods research design in which qualitative data were collected and analysed during Phase I and quantitative data were analysed during Phase II. Nineteen experts who have experience in building construction projects were involved in interviews conducted in Phase I. During Phase II, quantitative data were collected from contractors that were involved in the delivery of building projects using questionnaires and the data were analysed using FSE technique.

Clear delegation of responsibility, stability of organisational structure and crew composition are found to be the three most important HRM practices that can enhance productivity in building construction projects. The findings of the study showed that the overall importance index computed using the FSE model is 3.65 (≈ 4) with an equivalent linguistic term of “very important”. The study also suggested that the top three HRM practices should be implemented conjointly as there is no significant difference among their weights.

The output of this research can provide important information regarding the HRM practices in the Australian construction industry. Thus, international developers or contractors who want to do construction business in Australia can implement the essential HRM practices so that the productivity of their construction projects will not be affected negatively.