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.

Automated inspection systems utilizing computer vision technology are effective in managing traffic control devices (TCDs); however, they face challenges due to the limited availability of training datasets and the difficulty in generating new datasets. To address this, our study establishes a benchmark for cost-effective model training methods that achieve the desired accuracy using data from related domains and YOLOv5, a one-stage object detector known for its high accuracy and speed. In this study, three model cases were developed using distinct training approaches: (1) training with COCO-based pre-trained weights, (2) training with pre-trained weights from the source domain, and (3) training with a synthesized dataset mixed with source and target domains. Upon comparing these model cases, this study found that directly applying source domain data to the target domain is unfeasible, and a small amount of target domain data is necessary for optimal performance. A model trained with fine-tuning-based domain adaptation using pre-trained weights from the source domain and minimal target data, proved to be the most resource-efficient approach. These results contribute valuable guidance for practitioners aiming to develop TCD models with limited data, enabling them to build optimal models while conserving resources.

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.

Building Information Modeling (BIM) has significantly advanced design efficiency and collaboration in the architecture, engineering, and construction (AEC) industry. However, current BIM workflows remain hindered by complex interfaces, repetitive tasks, and limited automation options, which result in a steep learning curve and labor-intensive design processes. To address the challenges, this research presents a proof-of-concept framework that explores the integration of Artificial Intelligence (AI) and Large Language Models (LLMs) with BIM environments to streamline design modifications through natural language interaction. Using a specialized FreeCAD integration layer, the framework demonstrates the feasibility of translating high-level user instructions into basic BIM operations. Initial testing shows this approach can successfully create simple parametric building elements while reducing token consumption and user effort compared to a baseline approach using generic tools. This feasibility study suggests potential for developing more intuitive BIM interfaces, though further research is needed to achieve robust domain knowledge integration and handle complex architectural relationships. The findings indicate promising directions for future development of LLM-driven BIM automation tools that could enhance accessibility for AEC professionals.

This report evaluates the feasibility of 3D scanning technologies, including LiDAR, terrestrial laser scanning (TLS), and UAV photogrammetry, for improving bridge inspection practices. The study identifies challenges in adopting these technologies, particularly for detecting corrosion, section loss, and deformation in steel girders and other critical structural components. A framework is developed to integrate 3D scanning data with defect detection and structural analysis tools, ensuring compatibility with existing bridge inspection and asset management systems. The report provides practical guidelines for optimizing scanning strategies, data processing workflows, and implementation procedures to support Indiana’s bridge inspection practice. The proposed framework is designed to improve inspection accuracy, enhance efficiency, reduce field inspection time, and support data-driven decision-making for bridge maintenance and rehabilitation planning.