Most urban areas in the world have water infrastructure systems, including the buried sewer and water pipelines, which are assessed as in need of extensive rehabilitation. Deterioration by many other factors affects structural integrity. Trenchless technologies such as Cured-in-Place Pipe (CIPP) are now applied in numerous projects while minimizing disturbance in an urban environment. The main purpose of this study is to develop a high strength CIPP material using various composite materials (e.g., glass fiber, carbon fiber, polyester felt, unsaturated polyester resin, and others). Composite samples were made of the materials and tested using three-point bend apparatus to find mechanical properties, which include the flexural modulus, strength, and deflection. A composite combination with glass fibers with thin felt layers shows the best results in mechanical properties. Flexural modulus is a key factor for CIPP liner thickness design. Glass fiber composite yields between four and nine times higher values than the minimum value specified in the American Society for Testing and Materials (ASTM) F1216. This study provides a fundamental baseline for high strength CIPP liners that are capable of using conventional curing technologies.

This research examined how various green space layouts in an urban area affected the surrounding atmospheric air temperature that contributes to a heat island effect. The aim was to develop an understanding of whether this greenery arrangement had an influence on the urban heat island. Field measurements were taken for three different layouts: greenery surrounding a complex/building, greenery in the center of a complex/building, and greenery distributed over a complex/building. The study showed that in the presence of solar heat, there were significant temperature differences among the three layouts. The study showed that the sites with greenery in the middle of a complex generated the lowest urban heat island effect of the three layouts.

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.

The role of technology in education is becoming increasingly important, and the introduction of advanced technology and AI is transforming the way we learn. Virtual reality (VR) is an effective technology that enhances student engagement and improves learning outcomes. However, the cost of implementing VR is a significant concern for educational institutions, making integrating VR technology into education challenging. To address this challenge, this study aims to explore the costs associated with integrating VR into architectural, engineering, and construction (AEC) education. The study had three objectives: to identify relevant case studies that utilized VR in AEC education, to perform keyword analysis, and to conduct a cost analysis of the selected case studies. The thematic analysis identified VR applications in various categories, including VR platforms, construction safety training, design review simulators, civil engineering labs, building information modeling (BIM) integration, architectural design, and surveying engineering. The results revealed that the cost of VR varies based on the application, indicating that it is possible to implement VR in education even on a limited budget. This research provides valuable insights and recommendations for researchers and practitioners who want to adopt VR technology in AEC education effectively.

Highway capacity manuals (HCM) have incorporated the effect of heavy vehicles (HVs) in the form of passenger car equivalents (PCEs) for estimating highway traffic conditions, which include the road’s slopes, the length of the road, the geographical characteristics, and the ratio of HVs to other types of vehicles on the road. The PCEs of HVs are critical for designing, operating, and evaluating highways. In order to analyze the effects of HVs on highway traffic flow, the real-time automatic vehicle classification (AVC) data were collected for three different major highways (four-lane, six-lane, and eight-lane highways, respectively) in the Seoul metropolitan area for the 2 months from August to September 2011. The purpose of this study was to examine the effect of HVs on traffic flows using real-time AVC data and to analyze the relationship among the average speed, the HV ratio, the flow rate, and the number of lanes. The findings revealed that the average speed decreased when the flow rate and HV ratio increased for the six-lane and the eight-lane highways. The average speed also decreased as the flow rate increased for the four-lane highway. However, the average speed stabilized when the HV ratio was in the range of 25–45% and recovered when the HV ratio exceeded 45%; this result implied a need to reexamine the existing methods of deriving the HV factors for the HCM.

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.