Construction and demolition waste account for a significant part of the solid waste taking up landfills on a global scale. A considerable portion of the waste generated by the construction industry has substantial residual value, and therefore waste management and sustainability principles and techniques should be applied. Buildings consume a lot of energy during the operations phase, but decisions made during design and construction impact building operations. This study reviews sustainable building practices to explore strategies that ensure minimal effects on economy, society, and the environment through efficient resource and waste management at different phases of a building life cycle. These practices include pollution reduction, reuse and recycling, energy consumption, embodied carbon, and water resource management.

PurposeConstruction companies are increasingly adopting sensing technologies like laser scanners, making it necessary to upskill the future workforce in this area. However, limited jobsite access hinders experiential learning of laser scanning, necessitating the need for an alternative learning environment. Previously, the authors explored mixed reality (MR) as an alternative learning environment for laser scanning, but to promote seamless learning, such learning environments must be proactive and intelligent. Toward this, the potentials of classification models for detecting user difficulties and learning stages in the MR environment were investigated in this study.Design/methodology/approachThe study adopted machine learning classifiers on eye-tracking data and think-aloud data for detecting learning stages and interaction difficulties during the usability study of laser scanning in the MR environment.FindingsThe classification models demonstrated high performance, with neural network classifier showing superior performance (accuracy of 99.9%) during the detection of learning stages and an ensemble showing the highest accuracy of 84.6% for detecting interaction difficulty during laser scanning.Research limitations/implicationsThe findings of this study revealed that eye movement data possess significant information about learning stages and interaction difficulties and provide evidence of the potentials of smart MR environments for improved learning experiences in construction education. The research implication further lies in the potential of an intelligent learning environment for providing personalized learning experiences that often culminate in improved learning outcomes. This study further highlights the potential of such an intelligent learning environment in promoting inclusive learning, whereby students with different cognitive capabilities can experience learning tailored to their specific needs irrespective of their individual differences.Originality/valueThe classification models will help detect learners requiring additional support to acquire the necessary technical skills for deploying laser scanners in the construction industry and inform the specific training needs of users to enhance seamless interaction with the learning environment.

The COVID-19 pandemic severely impacted many industries on a global scale. Expectedly, the construction industry was not left out as non-essential construction was halted, strict health and safety protocols were introduced, and businesses were disrupted. New York City was the epicenter of the pandemic at its onset in the United States, and the pandemic had different impacts on workers based on their work location and role. This study utilized a survey including twenty-five statements to explore the initial impacts of the COVID-19 pandemic on the construction industry in New York State, analyzing its effects on sixty-one construction industry professionals, their projects, and firms, also considering their work location and role in the construction process. The most severe impacts were on construction schedules and in-person meetings. Those who worked in New York City had more difficulty complying with the increased health and safety regulations than those who worked outside the city. Those categorized as builders indicated significantly more contract performance issues. Furthermore, a set of recommendations were highlighted to strengthen the industry’s response to future similar disruptions. This study is significant in helping researchers and businesses build more resilient operations to address current and future pandemic-related challenges facing the construction industry.

She teaches courses in construction management at RIT including construction scheduling, planning and control and sustainable building construction and design. Her research is in sustainable built environments, occupant comfort and behavior, indoor environmental quality, and building energy consumption.

Construction materials play a crucial role in the project delivery process. However, the process of requisition for the materials on construction site if not handled appropriately can adversely affect the performance of quality, cost, and time. Therefore, the study was aimed at developing an automated materials requisition system which will make the supply chain on construction sites seamless and more effective. A use case diagram and an activity block diagram helped to understand the users and functionalities of the material requisition platform. In addition, using different user interface and a database system including a programming language to connect them, the study developed an automated Web-based material requisition system for construction firms using the model view controller (MVC) model. The MVC model comprised of using MySQL, HTML, and PHP, respectively, in the design. The automated materials requisition system was tested by sending material requisition through the supply chain of a construction firm. Result of the automated system was presented via screenshots of the Web-based platform. In conclusion, any construction firm can register on the platform and make use of the automated materials requisition system in order to maximize the productivity and optimize the use of ICT in their materials' supply chain process.