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.

The emergence of construction 5.0 marks a shift toward a human-centric approach to digitization within the construction industry. Along with diverse digital innovations related to this shift, blockchain technology offers vast opportunities for the construction industry, including streamlining project management processes, enhancing transparency in payment processes, and improving contract administration. This paper systematically reviews 109 articles using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to examine the applications of blockchain in construction, identifying twenty-three topics across eight thematic areas. These areas were further mapped using VOSviewer Online version 1.2.3 to identify interrelationships among the themes and highlight their broad impact. Key features like immutability, security, transparency, and traceability show promise in contract administration, supply chain logistics, facilities management, and sustainability. However, the study also describes the challenges of adopting blockchain in construction, emphasizing the need for enhanced stakeholder education, improved regulatory frameworks, and the creation of industry-specific blockchain platforms to support its acceptance in the construction industry. Emphasizing emerging blockchain applications and the adoption challenges equips researchers and practitioners with the knowledge of these applications and their significance to construction practices.

Infrastructure plays a pivotal role in a nation’s economic stability, security, and long-term sustainability. For decades, United States infrastructure has been rated as inadequate and the resources required for its restoration continue to grow. Despite the transformative potential of Building Information Modeling (BIM) coupled with digital technologies in revolutionizing the building industry, its widespread adoption within infrastructure projects remains limited. This study explores the application of BIM and emerging technologies in transportation projects’ lifecycle, identifies research gaps, and discusses the use of technology to address current infrastructure challenges. Employing a systematic review approach, this research synthesizes BIM application studies based on well-defined criteria. The synthesis of the literature underscores the comprehensive integration of BIM throughout the lifecycle of transportation assets, catalyzed by collaborative, information-driven processes. However, challenges pertaining to interoperability, role delineation, standards enforcement, and cost persist. This study advocates for a deeper exploration of the synergy between BIM, digital technologies, and infrastructure, envisioning an enriched transportation lifecycle in the future.

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.

Purpose: Construction 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/approach: The 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.Findings: The 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/implications: The 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/value: The 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.