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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.

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.

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.

The purpose of this paper shows the effect of hot and humid weather conditions (HHWCs) on workers that has resulted in considerable loss in the construction industry, especially during the hottest periods due to decline in worker productivity (WP). Until the last few decades, there is very limited research on construction WP in HHWCs. Nevertheless, these studies have sparked interests on seeking for the most appropriate methods to assess the impact of HHWCs on construction workers.

This paper begins by reviewing the current measuring methods on WP in HHWCs, follows by presenting the potential impact of HHWCs on WP. The paper highlights the methodological deficiencies, which consequently provides a platform for scholars and practitioners to direct future research to resolve the significant productivity loss due to global warming. This paper highlights the need to identify the limitations and advantages of the current methods to formulate a framework of new approaches to measure the WP in HHWCs.

Results show that the methods used in providing real-time response on the effects of HHWCs on WP in construction at project, task and crew levels are limited. An integration of nonintrusive real-time monitoring system and local weather measurement with real-time data synchronisation and analysis is required to produce suitable information to determine worker health- and safety-related decisions in HHWCs.

The comprehensive literature review makes an original contribution to WP measurements filed in HHWCs in the construction industry. Results of this review provide researchers and practitioners with an insight into challenges associated with the measurements methods and solving practical site measurements issues. The findings will also enable the researchers and practitioners to bridge the identified research gaps in this research field and enhance the ability to provide accurate measures in HHWCs. The proposed research framework may promote potential improvements in the productivity measurements methods, which support researchers and practitioners in developing new innovative methods in HHWCs with the integration of the most recent monitoring technologies.

Due to its key role in the successful delivery of construction projects, construction productivity is one of the most researched topics in construction domain. While the majority of previous research is focused on the productivity of labor-intensive activities, there is a lack of research on the productivity of equipment-intensive activities. The purpose of this paper is to address this research gap by developing a comprehensive list of factors influencing the productivity of equipment-intensive activities and determining the most influential factors through interview surveys.

A list of 201 factors influencing the productivity of equipment-intensive activities was developed through the review of 287 articles, selected from the ten top-ranked construction journals, by searching for construction productivity in the articles’ titles, abstracts or keywords. Next, the most influential factors were determined by conducting interview surveys with 35 construction experts. To ensure that the interviewees were aware of the research objectives and the distinction between labor- and equipment-intensive activities, an information session was held prior to conducting the surveys, and the surveys were conducted in interview format to allow for clarification and discussion throughout the process.

Project management respondents identified foreman-, safety- and crew-related factors as the categories with the most influence on productivity; tradespeople respondents identified foreman-, equipment- and crew-related factors as the most influential categories. In total, 14 factors were identified, for which there was a significant difference between the perspectives of project management and tradespeople regarding the factors’ influence on productivity.

This paper provides a comprehensive list of factors influencing the productivity of equipment-intensive activities. It identifies the most influential factors through an interview survey of 35 construction experts, who are familiar with the challenges of equipment-intensive activities based on their experience with such activities in the industrial construction sector of Alberta, Canada. Additionally, the differences between the factors that influence the productivity of labor- and equipment-intensive activities are discussed by comparing the findings of this paper with previous research focused on labor intensive activities.

The purpose of this paper is to develop a systematic strategic approach to handle corrective maintenance onto the failures/breakdowns of construction equipment. For the maintenance crew/team, a breakdown code management is proposed, which will provide focused and unambiguous approach to manage any kind of breakdowns in construction equipments.

The past breakdown records of a construction organization in the UAE are considered for analysis. From the failure data, through cause effect analysis (CEA) tools, the components and the breakdown codes namely breakdown main codes (BMC) and breakdown sub-codes (BSC) are formulated. With Pareto analysis, the critical codes are identified and validated through failure modes and effects analyses (FMEA) tools for the critical effect on the affected components. From this identified BSC's further closer failure identification codes namely breakdown symptom codes (BSyC) and breakdown reason codes (BRC) are identified through fault tree analysis (FTA) tools. The approach to modified breakdown maintenance management (MB2M) with breakdown maintenance protocol (BMP) is envisaged.

The study was conducted on four different types of heavy lifting/earth moving/material handling system of equipment and further focused with two earth moving equipment namely dumpers and wheel loaders. Failure analysis is performed and the failure ratio and the component contribution to the failures are identified. Based on the information, the preliminary codes namely BMC and BSC are identified through CEA tools and the BMC and BSC are identified to find the most contributing codes to the maximum number of failures through Pareto analysis. Further the critical sub-codes are further verified through FMEA tools on the severity levels of the sub components due to these codes. The FTA methods are used to identify the closer reasoning and relations of these codes and the further codes namely BSyC and BRC are identified which are the exact cause of the failures. The management of breakdowns is further proposed through MB2M which includes BMP which provides all resources for the breakdowns.

The failure data collected are only pertaining to the Middle East region and applicable to similar regions for similar plant mix in construction companies. The sample equipment is only part representative of the construction equipment. A more robust model can be suggested in the future covering all aspects and for other regions as well.

The proposed methodology and model approach is highly adaptable to similar industries operating in the Middle East countries.

Many authors have studied the preventive maintenance models and procedures and proposals have been proposed. On the breakdown maintenance management of construction equipment, very few studies have been proposed mostly on the cost analysis. This model attempts to provide a code management solution to manage the unpredictable failures in construction equipment through failure data analysis on a construction organization.