Construction productivity is a major research interest within the construction domain. Because construction is a labor-intensive industry, previous research has often focused on construction labor productivity (CLP). However, equipment is the main driver of productivity for some construction activities, so-called equipment-intensive activities. Existing models of activity-level productivity often predict a single-factor productivity measure—namely CLP—yet determining multifactor productivity, including labor, material, and equipment, provides more comprehensive predictions of productivity. Construction productivity models are often static in nature, or incapable of capturing the subjective uncertainty of some factors influencing productivity. Fuzzy system dynamics is an appropriate technique for modeling construction productivity because it captures the dynamism of construction projects and addresses the subjective and probabilistic uncertainty of factors influencing productivity. The contributions of this paper are threefold: identifying the key factors influencing the productivity of equipment-intensive activities, developing a predictive model of multifactor productivity for equipment-intensive activities using fuzzy system dynamics technique, and developing an approach to reduce uncertainty overestimation in the simulation results of fuzzy system dynamics models.

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Originality/value: 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.

Construction projects are highly risk-prone due to both internal factors (e.g., organizational, contractual, project, etc.) and external factors (e.g., environmental, economic, political, etc.). Construction risks can thus have a direct or indirect impact on project objectives, such as cost, time, safety, and quality. Identification of these risks is crucial in order to fulfill project objectives. Many tools and techniques have been proposed for risk identification, including literature review, questionnaire surveys, and expert interviews. However, the majority of these approaches are highly reliant on expert knowledge or prior knowledge of the project. Therefore, the application of such tools and techniques in risk identification for renewable energy projects (e.g., wind farm and solar power plant projects) is challenging due to their novelty and the limited availability of historical data or literature. This paper addresses these challenges by introducing a new risk identification framework for renewable energy projects, which combines case-based reasoning (CBR) with fuzzy logic. CBR helps to solve problems related to novel projects (e.g., renewable energy projects) based on their similarities to existing, well-studied projects (e.g., conventional energy projects). CBR addresses the issue of data scarcity by comparing novel types of construction projects to other well-studied project types and using the similarities between these two sets of projects to solve the different problems associated with novel types of construction projects, such as risk identification of renewable energy projects. Moreover, the integration of fuzzy logic with CBR, to develop fuzzy case-based reasoning (FCBR), increases the applicability of CBR in construction by capturing the subjective uncertainty that exists in construction-related problems. The applicability of the proposed framework was tested on a case study of an onshore wind farm project. The objectives of this paper are to introduce a novel framework for risk identification of renewable energy projects and to identify the risks associated with the construction of onshore wind farm projects at the work package level. The results of this paper will help to improve the risk management of renewable energy projects during the construction phase.