The world is progressing towards sustainable, eco-friendly, recyclable materials to enhance the circular economy and mitigate the issues of carbon footprint, overburdened landfills, and waste of natural resources. As increasing greenhouse gas (GHG) emissions are a major contributor towards climate change and given that the construction industry is one of the major producers of GHG emissions, it is crucial to meticulously quantify and lower its emissions, especially in the context of developing countries. This research presents a novel framework by combining advanced tools i.e., building information modeling (BIM), life-cycle assessment (LCA), geographic information systems (GISs), and quantification of embodied emissions to optimize construction’s design, material-selection, operations, maintenance, and waste-management processes. The effectiveness of the proposed approach has been demonstrated with the help of a real-world case study in Islamabad, Pakistan. A building model has been generated using BIM, and a comprehensive LCA has been conducted. Additionally, GIS tools have been utilized to identify the locations and accessibility of available-waste-management facilities. Based on this data, embodied emissions related to handling and transportation of waste material to disposal facilities have been computed using mathematical analyses. Furthermore, targeted mitigation strategies have been proposed and an optimized route has been designed using GIS-based route-optimization tools along the suggested facility centers in the Islamabad region. The case study has been reassessed with alleviation strategies, and the results show that 29.35% of the materialization stage, 16.04% of the operational stage, and 21.14% of the end-of-life-phase GHG emissions can be effectively reduced. Hence, pre-evaluating the environmental degradation caused by construction projects throughout their life cycle might offer an opportunity to comprehend and reduce prospective environmental impacts.

With rapid urbanization, the utilization of underground space has become an important part of infrastructure. However, the stability of underground spaces such as large caverns remains a key challenge in civil engineering throughout the lifecycle of a project. Traditional methods of stability assessment rely on static models and periodic monitoring and often fail to capture real-time changes in rock behavior, leading to potential safety risks and, in severe cases, even the collapse of underground infrastructure. To address this challenge, this study introduces a digital twin (DT) framework to improve stability assessments and monitor deformations in underground structures. The framework enables the continuous monitoring and adaptive optimization of rock support systems by combining real-time sensor data with virtual simulations. A five-dimensional DT framework comprises physical objects, virtual objects, service systems, DT data, and their interconnections. It incorporates six key modules, which are structure, geology, material, behavior, performance, and environment, to enhance the understanding of cavern stability. The framework is based on Industry Foundation Classes standards to ensure seamless data exchange, interoperability, and the standardized representation of geotechnical and structural data. A seven-step methodology is developed for this framework, encompassing geological assessment, virtual modeling, Building Information Modeling (BIM)-based design, construction processes, real-time monitoring, and optimization strategies. To evaluate its effectiveness, the framework is applied to a case study, demonstrating improvements in deformation monitoring and rock support efficiency. The findings highlight the potential of integrating DT with BIM to enhance safety, reliability, and long-term stability in underground construction projects.

One in every five occupational deaths occurs in the construction sector. A proactive approach for improving on-site safety is identifying and analyzing accident precursors, such as near-misses, that provide early warnings of accidents. Despite the importance of near-misses, they are frequently left unreported and unrecorded in the construction sector. The adoption of modern technologies can prevent accidents by automated data collection and analysis. This study aims to develop near-miss detection metrics to facilitate the automated detection of near-misses through sensors. The study adopted a mixed method approach including both qualitative and quantitative approaches. First, a quantifiable definition of near-misses was developed from the literature. Hazards, accidents, and the causes of accidents were identified. Through empirical and statistical analyses of accidents from the OSHA repository, combinations of unsafe acts and conditions responsible for a near-miss were identified. The identified factors were analyzed using a frequency analysis, correlation, and a lambda analysis. The results revealed twelve significant near-misses, such as A1—approach to restricted areas and C2—unguarded floor/roof openings, A5—equipment and tool inspection was incomplete and C8—unsafely positioned ladders and scaffolds, A2—no or improper use of PPE and C2—unguarded floor or roof openings, etc. Lastly, measurable data required by sensors for autonomous detection of near-misses were determined. The developed metric set the basis for automating near-miss reporting and documentation using modern sensing technology to improve construction safety. This study contributes to improving construction safety by addressing the underreporting of near-miss events. Overall, the developed metrics lay the groundwork for enhancing construction safety through automated near-miss reporting and documentation. Furthermore, it helps for the establishment of safety management schemes in the construction industry, specifically in identifying near-misses. This research offers valuable insight into developing guidelines for safety managers to improve near-miss reporting and detection on construction sites. In sum, the findings can be valuable for other industries also looking to establish or assess their own safety management systems.

Designing and modeling tunnels are a complex and labor‐intensive process, exacerbated by the involvement of multiple designers and software applications to accommodate diverse geometric and subsurface conditions. This often results in reduced efficiency, productivity, and increased potential for errors. To address these challenges, the authors implemented a strategy integrating building information modeling and mathematical relationships governing tunnel geometries and rock support elements. Using Autodesk Revit, they developed a prototype that allows designers to model tunnels aligned with ground conditions without reliance on multiple software applications. This streamlined approach enhances productivity, simplifies complexities, and improves accuracy in tunnel design and review processes, including quantity take‐offs. A case study comparing the prototype with conventional methods demonstrated significant improvements in efficiency, productivity, and design accuracy. This research offers practical benefits to stakeholders in tunnel projects, optimizing work quality, and outcomes in tunnel design and construction.