The urban landscape is undergoing significant transformations due to economic shifts, urban planning initiatives, technological advancements, and sociopolitical changes. Almost 89% of the U.S. population is projected to reside in urban areas by 2050, which increases a pressing need for innovative and efficient urban revitalization strategies. Modular construction is an extensively researched topic with potential promise for addressing these challenges; however, there is a lack of comprehensive understanding of its specific opportunities and challenges within the context of urban revitalization. This paper explores the potential of modular construction methods as a viable solution in densely populated areas with limited space and competing land uses. This study employs a systematic literature review using PRISMA and mixed methods analysis with NVivo to identify the opportunities and challenges associated with implementing modular construction in urban settings. The findings highlight the advantages of modular construction, including reduced construction time, enhanced quality control, and improved sustainability. However, challenges such as land scarcity, restrictive zoning laws, and the need for innovative planning and collaboration among stakeholders are also emphasized. This paper provides valuable insights for policymakers, urban planners, and developers to effectively leverage modular construction methods for sustainable and efficient urban renewal efforts.

With the increase in high-rise building construction, failure to consider constructability in the design phase can result in huge wastes in the construction phase, as well as losing opportunities for...

The successful construction of tall buildings requires effective construction management based on various quantitative data. The recent development of ubiquitous sensor networks (USNs) enables massive amounts of data to be collected in real-time. However, the application of USN-based data acquisition systems to repetitive tasks on typical floors of tall buildings can be inefficient, because this may involve the repetitive reinstallation of sensors and the repositioning of data loggers and routers to enable continuous data transfer. To minimize this cumbersome work, a modified data acquisition method using reusable sensor nodes and mobile devices can be a useful solution. This study analyzes the economic aspects of the USN-based systems for concrete temperature monitoring by using the activity-based costing technique. The case study shows that the modified system can reduce the process cost by about 19%. It can also reduce the resource input time of management by about 55%, freeing up time for other management activities. Moreover, the cost benefits should scale up as projects increasingly require more measurement and monitoring. This study should facilitate the application of USN-based information management systems, particularly for tall building construction.

Deep learning-based semantic segmentation of point clouds is a vital tool for efficiently generating accurate as-built Building Information Models (BIM) by automating the identification and classification of building components. While traditional deep learning models have primarily focused on segmenting visible structural elements such as columns, walls, doors, windows, and furniture, the growing popularity of open ceiling designs presents unique challenges. Open ceiling environments expose Mechanical, Electrical, and Plumbing (MEP) components, including pipes, ducts, and electrical conduits, which are often characterized by overlapping geometries, visual similarities, and intricate spatial arrangements. These complexities raise questions about whether existing deep learning-based semantic segmentation models can effectively classify MEP components in such challenging environments. This study addresses this gap by evaluating the performance of RandLA-Net, a lightweight deep learning-based semantic segmentation model, for accurately classifying MEP components in open ceiling environments. Using LiDAR technology, 3D point cloud data was collected within a building featuring an open ceiling design, where components were manually annotated to train and test the model. The performance of the model was evaluated through metrics such as overall accuracy (OA) and mean Intersection over Union (mIoU). The results showed an OA of 90.86% and a mean IoU of 83.1%, indicating that the existing RandLA-Net model is capable of effectively segmenting MEP components alongside structural elements, even within the challenging context of open ceiling designs. The findings suggest that RandLA-Net has strong potential for addressing the complexities of MEP component classification in open ceiling environments. Furthermore, the study highlights the opportunities for enhancing semantic segmentation methods to achieve even greater precision in as-built BIM generation, supporting improved maintenance planning and operational efficiency in complex architectural settings.

Piezoresistive Tactile Sensors