The number of scan-to-BIM projects that convert scanned data into Building Information Modeling (BIM) for facility management applications in the Mechanical, Electrical and Plumbing (MEP) fields has been increasing. This conversion features an application purpose-oriented process, so the Scan-to-BIM work parameters to be applied vary in each project. Inevitably, a modeler manually adjusts the BIM modeling parameters according to the application purpose, and repeats the Scan-to-BIM process until the desired result is achieved. This repetitive manual process has adverse consequences for project productivity and quality. If the Scan-to-BIM process can be formalized based on predefined rules, the repetitive process in various cases can be automated by re-adjusting only the parameters. In addition, the predefined rule-based Scan-to-BIM pipeline can be stored and reused as a library. This study proposes a rule-based Scan-to-BIM Mapping Pipeline to support application-oriented Scan-to-BIM process automation, variability and reusability. The application target of the proposed pipeline method is the plumbing system that occupies a large number of MEPs. The proposed method was implemented using an automatic generation algorithm, and its effectiveness was verified.

Public–private Partnerships have become a common delivery method for diverse types of projects ranging from transportation and energy infrastructure to social infrastructure. Previous research has mainly focused on PPPs for infrastructure and other non-social projects. Although PPP projects for higher education institutions share some common attributes with their traditional counterparts, they also have unique aspects such as institutional culture as well as structure of ownership and management. Hence, the objectives of this research were to (1) conduct a systematic collection and analysis of PPP projects in higher education institutions in the United States; and (2) conduct a gap analysis to provide recommendations for future projects and lessons learned from past ones. A gap analysis of the published data on higher education PPPs was undertaken, identifying 45 educational PPP projects in the United States. The main areas of study were type of project (e.g., housing, commercial, mixed use, etc.), size, and investment made. Additionally, a questionnaire survey was disseminated to experts in the field to collect data on these projects and report on them. The results showed an increasing trend in project size between 1994 and 2018 with the majority being for housing developments while a smaller percentage was for commercial and utility projects. A geographical representation shows a large number of projects clustered in the Southern and Northeastern regions of the United States. Additionally, a questionnaire survey was used to identify samples of these projects and present them as a case study. The number of PPPs is expected to rise due to funding cuts and state appropriation cuts. Finally, the proposed recommendations can also be extrapolated for other social or infrastructure projects.

Pavement markings play a critical role in reducing crashes and improving safety on public roads. As road pavements age, maintenance work for safety purposes becomes critical. However, inspecting all pavement markings at the right time is very challenging due to the lack of available human resources. This study was conducted to develop an automated condition analysis framework for pavement markings using machine learning technology. The proposed framework consists of three modules: a data processing module, a pavement marking detection module, and a visibility analysis module. The framework was validated through a case study of pavement markings training data sets in the U.S. It was found that the detection model of the framework was very precise, which means most of the identified pavement markings were correctly classified. In addition, in the proposed framework, visibility was confirmed as an important factor of driver safety and maintenance, and visibility standards for pavement markings were defined.

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.

Accurate earthwork quantity estimation is essential for effective project planning and cost management in the Architecture, Engineering, and Construction (AEC) industry. Traditional methods for quantity takeoff are often time-consuming and susceptible to human error, particularly when working with unstructured datasets such as CAD drawings. This study introduces the Earthwork Network Architecture (ENA), a novel deep learning framework that incorporates Large Language Models (LLMs), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM) networks, and Transformers to automate and enhance the accuracy of earthwork quantity estimation. We assume that if LLMs can be trained effectively using such unstructured construction dataset, the effects such as improved accuracy and the challenges of LLMs can be clearly examined. Among the architectures tested, the LLM-based ENA demonstrated superior performance, achieving faster convergence, greater loss reduction, and higher classification accuracy, with a Quantity Takeoff Classification accuracy of 97.17%. However, the LLMs required significantly more computational resources compared with other models. These findings suggest that LLMs, typically used in natural language processing, can be effectively adapted for complex AEC datasets. This study lays the groundwork for future AI-driven solutions in the AEC industry, underscoring the potential of LLMs and Transformers to automate the quantity takeoff process and manage multimodal data in construction projects.

Historically the U.S. transportation system has been continuously improved to adopt new policies and technologies. The transportation vehicle energy transition from fossil fuels to electricity is promoted by policymakers and major automakers with an expectation of enhancing sustainability aspects such as fossil fuel consumption reduction, carbon emissions reduction, and lower operations and maintenance costs. However, the electrification of the existing transportation infrastructure system requires substantial upgrades to overcome two major concerns from ordinary drivers and the public. One is the driver’s range anxiety based on the current capability of the electric vehicle (EV) technologies. The other is the availability of EV charging stations near the planned route. To address these two issues, we introduce an agent-based simulation model to project the consequences of electrification in the Indiana state highway system. Specifically, the model is developed to monitor the status of long-distance EV trips between different regions. The multi-agent engine method guarantees the model can adapt to diverse scenarios and complex environments. The simulation experiment verifies that the proposed model can provide the expected outcomes, including the numerical data of electric energy demand and the geospatial information (as location coordinates) of failed trips. By performing a GIS-based analysis of the results, the derived geospatial data can help state transportation agencies determine where to deploy the charging facilities to satisfy the overall charging demand. The proposed simulation framework offers a novel and strategic way to resolve the challenges for EV charging-related research and projects.