In recent years, the shortage in skilled labor was one of the most significant concerns for the construction industry. Previous studies explored the causes and effects of such shortages; however, these efforts featured limited documentation and implemented subjective methods for analysis. This phenomenon is very complex and dynamically responds to the labor market's behavior and its influence on construction projects. The main goal of this study is to provide a simulation-based method for analyzing the causes and impacts of skilled labor shortages. By adopting a system dynamics (SD) approach, an integrated SD model was developed to explain the dynamic interrelationships between the causes and effects of the skilled labor shortage in construction projects. A total of five scenarios were established to simulate shortages and construction project behaviors. Based on statistical regression and sensitivity analysis, this study contributes to a better understanding of the patterns among the causes of shortages, the shortages themselves, and their impacts on labor wages, cost overruns, and scheduling concerns in construction projects.

Recent studies in construction waste and management (CWM) have mainly investigated the waste management chain from a static perspective and failed to take into account the dynamic nature of parameters and their correlation. In addition, the current studies of building information modeling (BIM)-based CWM failed to analyze the cost–benefits due to the lack of numerical economic benchmarks. To address the gap, this study developed a system dynamic (SD) model to analyze the economic aspects of construction and demolition (C&D) waste from using BIM. Causal loop and stock-flow diagrams are modeled based on the determined variables and their interrelationships. Standard sensitivity tests were then performed to establish the validity of the model under real system conditions. Different scenarios were applied to simulate and compare the model results in response to various policies. A case study was conducted to quantify the costs and the profits. Based on the comparison with the conventional approach and BIM-based method, BIM can reduce CWM cost by up to 57%. The findings also indicated that higher landfill charges will not be able to motivate managers to use sustainable CWM; conversely, increasing the modularity of design and earlier realization of net benefits will incentivize project managers to employ BIM-based CWM.

The construction industry has been trying to enhance the level of digitalization and autonomy by adopting various communication and information technologies (ICT), e.g., augmented reality (AR), virtual reality (VR), robotics, drones, or building information modeling (BIM). However, improvement of the safety and productivity in their domains is still a struggle. One of the main reasons for failing to accelerate their digital transformation is ignoring the deep understanding of the concept of digital twin, its usage, and the potential benefits of digital twins in the construction industry. Therefore, this paper investigated the impacts and potentials of digital twins on the construction industry through a quantitative systematic review assisted by the text mining method. The study presented the potential usability of digital twins, leading and core technologies, and applications, revealing their benefits and potential for optimizing project planning, execution, and management process. Through this comprehensive literature review, this study elucidated the distinctive features, advantages, and immense potential that digital twins bring to the construction field. The findings highlight the transformative impact of digital twins, providing critical insights for their broader adoption and groundbreaking applications in the industry. By addressing the challenges of adopting this technology, the article provided valuable insights for advancing research and the broad implementation of digital twins in the sector.

Many studies have discussed unmanned aircraft system (UAS) applications for infrastructure conditions and construction project inspections. However, the industry currently faces significant challenges that hinder the use of this technology. UAS operational procedure is disjointed with regard to the inspection decision-making process. Moreover, training those responsible for UAS operations requires a great deal of time and cost. This study documents the job responsibilities and establishes goals for UAS operators in the construction and infrastructure domains through the use of goal-directed cognitive task analysis (GCTA). GCTA has been proven essential to enhancing training efficiency by revealing necessary areas of augmentation of information requirements related to UAS operation, such as situation awareness and decision criteria. The main goal of this study is to provide a better understanding of the task responsibilities and multilevel goals for operating a UAS. The findings of this study include three key personnel identified, their task domains, goals, and decision criteria, and situation awareness (SA) requirements. In the end, this study also instigates extensive discussion of the conceptual goal-directed decision-making process to support the realization of the goals of UAS applications. This study will help to enhance training performance and align UAS operational procedures and decision-making related to inspection tasks in the construction and infrastructure project environments.