This research proposes a "best practice" workflow for the rapid creation and visualization of Building Information Modeling (BIM) models in infrastructure project pursuits. Addressing the lack of standardized evaluation methodologies, the study aimed to reduce time and costs without compromising quality. The workflow integrates rapid modeling techniques, streamlined visualization, and emerging technologies like Virtual Reality (VR) and AI image generation.Validation through two case studies—the Palm Beach County and Concord Hillgrove water treatment plants—demonstrated significant resource savings compared to traditional methods. For average projects, the workflow yielded mean savings of 41.31 hours and $1,837.21. In scenarios with severe constraints, the framework achieved a 67.12% reduction in time requirements. These findings provide a quantifiable baseline for evaluating BIM benefits and optimizing delivery in high-pressure pursuit environments.

The COVID-19 pandemic has precipitated profound socioeconomic and public health ramifications globally. Wastewater-based epidemiology has been increasingly utilized for the surveillance and mitigation of COVID-19 outbreaks and transmission. The implementation of wastewater surveillance methodologies at a small scale has demonstrated considerable cost-effectiveness as an alternative to individual clinical testing, particularly in high-density environments such as academic institutions. Wastewater surveillance necessitates the acquisition and analysis of complex spatiotemporal data, requiring sophisticated interpretation and integration with complementary epidemiological parameters to effectively inform intervention strategies. The systematic management and analysis of these multivariate datasets present formidable logistical and computational challenges for timely decision making. This investigation advances geospatial science through a novel web-based spatial decision support system (SDSS) framework to resolve these challenges. This study encompasses the main campus of the University of North Carolina at Charlotte, where we implemented a spatiotemporal data model that revolutionizes management of multidimensional space-time data of wastewater surveillance. We employ advanced spatiotemporal analysis incorporating innovative cluster detection algorithms and uncertainty quantification to elucidate latent spatio-temporal patterns of SARS-CoV-2 virus abundance, which conventional approaches consistently fail to detect. This investigation conclusively demonstrates the superiority of integrated space-time cluster pattern analysis from both wastewater surveillance and clinical test results, quantifying their differential robustness when subjected to spatiotemporal uncertainties. The SDSS framework developed here represents a breakthrough in automated management, analytics, and dissemination of spatiotemporal wastewater epidemiological data. Our framework delivers transformative capabilities for informing evidence-based prevention strategies and establishing targeted intervention protocols for COVID-19 outbreaks within complex institutional environments.

Highway work zones present significant safety concerns. This study aims to identify spatial and temporal factors that affect the number of highway work zone crashes in North Carolina (NC). The data from 2000 to 2020 were provided by the NC Department of Transportation (NCDOT). Descriptive statistics, exploratory analysis, and time series analysis were employed. It was found that crashes occurred most frequently in construction-related work zones, work zones with ongoing activities, during clear weather conditions, dry surface conditions, daytime, and right next to the work area. The time series analysis identified multiple trends and a seasonal pattern for monthly crash frequencies, which were used to develop a Holt-Winters time series crash prediction model. The model findings indicate that the crash trend and seasonality are important factors in highway work zone crash modeling along with geometric (e.g., lane width) and traffic variables (e.g., volume).

During the COVID-19 pandemic, many academic institutions have been facing the challenge of how to maintain face-to-face instruction while also reducing the chance of an outbreak within the student population. One potential solution that has proven to be efficient is wastewater surveillance of student residence halls. In a case study, BIM models of 18 residence halls on a university campus were developed, each of which included parametric components such as building envelopes and plumbing fixtures. These models also included the locations of COVID-19 wastewater autosamplers. A Dynamo visual programming script was developed to highlight a vertical riser once the connected autosampler showed the existence of the coronavirus. Supplementary 2D floor plans and 3D isometric views can also be generated to help the institute's pandemic management team make informed COVID-19 management decisions. In the absence of someone who is knowledgeable in plumbing, this novel application of the BIM technology can significantly improve visual comprehension of the extent of COVID-19 spread within a facility and assist the management team in decision-making processes under tight time constraints.

The COVID-19 pandemic has been a source of ongoing challenges and presents an increased risk of illness in group environments, including jails, long-term care facilities, schools, and residential college campuses. Early reports that the SARS-CoV-2 virus was detectable in wastewater in advance of confirmed cases sparked widespread interest in wastewater-based epidemiology (WBE) as a tool for mitigation of COVID-19 outbreaks. One hypothesis was that wastewater surveillance might provide a cost-effective alternative to other more expensive approaches such as pooled and random testing of groups. In this paper, we report the outcomes of a wastewater surveillance pilot program at the University of North Carolina at Charlotte, a large urban university with a substantial population of students living in on-campus dormitories. Surveillance was conducted at the building level on a thrice-weekly schedule throughout the university's fall residential semester. In multiple cases, wastewater surveillance enabled the identification of asymptomatic COVID-19 cases that were not detected by other components of the campus monitoring program, which also included in-house contact tracing, symptomatic testing, scheduled testing of student athletes, and daily symptom reporting. In the context of all cluster events reported to the University community during the fall semester, wastewater-based testing events resulted in the identification of smaller clusters than were reported in other types of cluster events. Wastewater surveillance was able to detect single asymptomatic individuals in dorms with resident populations of 150-200. While the strategy described was developed for COVID-19, it is likely to be applicable to mitigation of future pandemics in universities and other group-living environments.

This research was conducted to determine pavement performance jumps after treatment, which are defined as the difference between pretreatment and post-treatment Pavement Condition Rating (PCR) values of asphalt pavements. The North Carolina Department of Transportation (NCDOT) resets the PCR of sections to its highest value of 100 after overlay-type treatments are applied. However, the condition of a pavement after treatment depends on the treatment applied, indicating that the PCR after treatment can be less than 100. This research was conducted to investigate the magnitudes of pavement performance jumps caused by most common types of treatment utilized by the NCDOT. The data for this research were collected by surveys and divided into treatment families. Pretreatment and post-treatment PCR values were calculated to determine the performance jumps, and after-treatment performance curves were developed. The results indicated that post-treatment asphalt performance curves' intercepts are not 100; the values are 92.8, 90.0, 87.8, and 84.6 for asphalt-cement (AC) Construction/Reconstruction, Resurface, Mill + Resurface, and Chip Seal, respectively. These jumps, along with the performance curves, can provide better prediction of the condition of pavement over its lifetime after a treatment is applied and assist not only NCDOT engineers but also engineers in other state DOTs in developing effective treatment plans. (C) 2020 American Society of Civil Engineers.

ABSTRACT Evaluating the structural integrity of curtain walls during the life cycle of a building project can assist architects in developing better designs, help contractors establish better installation methods, and allow facility managers make informed maintenance decisions. This paper presents an effort to develop a process which combines three types of technologies: 3D laser scanning, Building Information Modeling (BIM), and Finite Element Analysis (FEA), to evaluate the structural integrity of a curtain wall. In a case study, a 3D laser scanner was used to scan the curtain wall, the resulting set of point clouds was used to create an actual as-built BIM model. This “as-is” BIM model is different than a construction as-built BIM model in that the former model captures existing deformations developed during construction, installation, and maintenance phases. Then further analysis was completed using simulation with FEA using the BIM model to potentially predict any future structural issues. Wind loads on the building façade and their effect on unintentional stresses built into the glass panel were studied. The final results inform of deformities in the curtain wall and show the amount of wind load the structure can support before there is a risk of structural damage. The contribution of this study is that the harmonious three-step technique quickens the entire process of identifying the risks to a building element. An additional use for these common software packages would be beneficial to all the stakeholders involved in the life cycle of the building, especially those concerned with the facilities management and the building life cycle.

The International Roughness Index (IRI) is an important pavement roughness measure that can be used to trigger appropriate maintenance treatments, approve new and rehabilitated roadways, and determine contractors' performance incentives. This study developed a systematic approach to determine IRI limits and thresholds for flexible pavement. A total of 241 research participants were randomly recruited from 9 counties in 3 regions of North Carolina. Their perceptions of acceptable and unacceptable smoothness of preselected roadways were collected and analyzed. It was concluded that if the measured IRI value is less than or equal to 1.66m/km (105in./mi), most likely this section will be rated as acceptable; the rating will be unacceptable if the IRI value is greater than or equal to 2.37m/km (150in./mi). The acceptable and unacceptable IRI limits for urban and rural roadways in North Carolina were also determined. These findings can be used to derive IRI thresholds for construction approvals and contractors' performance incentives. Furthermore, these IRI thresholds can be used to determine appropriate IRI trigger points for maintenance strategies. Although IRI limits and thresholds were determined using data provided by the North Carolina Department of Transportation (NCDOT), the method developed in this study can be easily adapted for use in other state DOTs.

Equipment management decisions are based on estimates of owning and operating costs throughout the life of a machine. The appropriate selection of the method to develop operating cost models depends on the availability of cumulative cost data and whether machine age is maintained in terms of use or years. Operating rates calculated from annual data are inherently more variable than those calculated from cumulative data because the rates are based only on what was experienced by the machine in the year. The objective of this research was to compare operating cost estimates developed from annual data with observed cumulative cost data to determine the extent and magnitude of overestimates from annual data. Operating cost and use data were collected for backhoe loaders and motor graders operated as part of a state transportation agency maintenance equipment fleet. Annual data was used to develop computational models of estimated operating cost with accumulated use, and compared observed costs and mathematical models developed from cumulative data. The results indicate that cost models based on annual data tend to overestimate operating costs over the life of a machine. Operating cost estimates from annual data were consistently higher than recorded costs and the magnitude of the overestimate in costs ranged from approximately 20 to 40 percent as compared to estimates from cumulative cost records. The magnitude of the overestimate in operating costs is substantial and may lead to decreased price competitiveness, strained relationships within management, and decreased equipment utilization.

This study presents an approach to develop sigmoidal family pavement performance models (pavement performance ratings versus pavement age) for a pavement management system (PMS). Pavement condition data collected from windshield surveys oftentimes suffer quality issues stemming from human subjectivity, and pavement age sometimes not being properly reset after a treatment. These issues can be systematically addressed by the proposed approach, and nonlinear sigmoidal family performance models can then be developed using the cleaned condition data. In a case study, this approach was successfully applied to a sample data set extracted from the North Carolina Department of Transportation (NCDOT) PMS. Contour plots developed for the raw data and the cleaned data showed that the data cleansing process was effective. Goodness-of-Fit indicators and cross-validation suggest that the resulting nonlinear sigmoidal models fit the condition data well. (C) 2015 American Society of Civil Engineers.

This study presents a method to develop piecewise linear (PL) performance models for pavement condition data in a pavement management system (PMS). These condition data are usually ordinal and have more than two severity levels. Ordinal logistic regression is conducted to derive probabilities of each individual severity level. The intersections of probability curves are identified as the breakpoints, which can be used to develop PL models. This proposed method was then applied successfully to develop four PL models, for interstates, U.S. routes, North Carolina routes, and secondary routes, using transverse cracking condition data of flexible pavements in a state DOT's PMS. Results showed that the PL models reflected actual deterioration trends well and that the proposed method is robust. (C) 2014 American Society of Civil Engineers.

Smoothness of pavements has been used for construction acceptance and pay adjustment by many state highway agencies. Currently, the international roughness index (IRI) has become the most widely accepted standard to evaluate pavement smoothness. To establish acceptance criteria for a pavement, its initial IRI values (the IRI rating occurs right after the pavement is constructed) needs to be determined and provided to contractors as a quality assurance measure. The determination of initial IRI values for various pavement types is a challenging task. This is mainly due to the variations in pavement data collected across localities and the limited availability of pavement design and simulation tools. This paper presents a method to address this issue. Flexible pavements’ terminal IRI values were simulated using AASHTOWare Pavement ME Design, the new AASHTO (American Association of State Highway and Transportation Officials) design software. Then statistical analysis was conducted to derive initial IRI values. Once implemented, this method can be adopted by highway agencies to establish new acceptance criteria for different types of pavements.

The project based classroom has grown in popularity with the academic community, primarily due to the new generation of students responding poorly to the deductive, or professor centered classroom. Unfortunately, collaborative work or team assignments are frequently completed by students working independently during the project and combining work near the due date. This negates the intention of cross team communication and the group approach to solving problems. Regrettably, this model of team assignments where students work independently without the intended cross team communication is prevalent on many campuses nationwide. In an effort to effectively engage the new construction management student and provide a "real life" experience, the authors developed the Multicourse Undergraduate Learning Community (MULC).The Multicourse Undergraduate Learning Communities (MULC) project is an instructional tool that utilizes a "real world" project that engages two or more courses in a curriculum. The project is selected based on its ability to simulate industry team relationships as well as reinforcing course learning objectives. With MULC projects, students from each course rely on one another for project deliverables, such as a highway design engineer would rely on a surveyor for land data.The MULC project that was implemented utilized two courses: ETCE 2112 Construction Surveying and ETCE 4251 Highway Design and Construction. In this structure, the instructor driven project was replaced with a student driven model that simulates industry relationships. The project consisted of the design and layout of an access road for a new traffic pattern on campus. Each surveying group was paired with a highway design group to complete the project. The highway design teams (senior level) served as the project lead and each surveying team (sophomore level) was required to communicate with their highway design counterparts to collaboratively complete this project. This paper presents the development of a civil engineering technology/construction management MULC model and the results of the first delivery of a MULC project.

This book describes how specific modifications in patterns of land development could help reduce greenhouse exhaust gases from vehicles. The book is based on a thorough review of dozens of studies by leading researchers in urban planning. It finds that urban development is both a major contributor to climate change and an essential factor in conquering it. The authors argue the premise that one of the best methods of reducing vehicle travel is development that is compact and condensed in nature. Such development would construct places in which people can navigate from place to place without driving. This kind of development includes pedestrian friendly development and mixed use development. There is a developing demand for smaller lots and homes, and condominiums and townhouses nears jobs and activity centers, which are fueled by rising gasoline prices, shrinking households, lengthening work trip commuting, and changing demographics. Existing government regulations and policies reward automobile dependent and sprawling development. The book offers recommendations for making green neighborhoods more affordable and available.

To minimize impact due to travel delay, the US Department of Transportation (USDOT) has been pushing for Accelerated Construction (AC) techniques for public transportation construction. In contrast to traditional construction techniques, the AC technology is envisioned by the federal agency to have the potential to generate great savings for the nation by eliminating unnecessary traffic jams due to slow construction processes.This change in construction philosophy offers a great opportunity to introduce the advanced concept of full monitoring of structural construction/aging processes via embedded sensing technologies. Since this involves both inspection techniques and construction management, this paper suggests an integrated learning approach that can be applied to a design project-oriented course content that is offered in both Civil Engineering Technology (CIET)/Construction Management (CM) and Structural Monitoring (CEE) courses, such that students from both Departments can work separately, but produce one project outcome. Results from a student survey indicated that this study enhanced students' skills of generating creative and realistic solutions for solving open-ended problems, and promoted an active learning environment by diffusing interdisciplinary knowledge and engaging collaborations amongst graduate/undergraduate study groups.

Curtain walls have been used in today' s buildings to create attention-grabbing facade, to improve occupants comfort level, and to preserve building energy. Curtain walls, however, can fail prematurely and cause severe safety concerns. Past studies concluded that flaws during design, fabrication, and construction are main reasons for curtain wall failures. Building Information Modeling (BIM), an emerging technology in the Architecture, Engineering and Construction (A/E/C) industry, is proposed in this study as a holistic approach to address these issues. Ways BIM can be used to solve for design, constructability and fabrication issues will be explored. Conclusions will be drawn and suggestions for further avenues of research will be presented. The results from this study should be beneficial to building owner, glazing contractor, glazing system supplier, architect, structural engineer, general contractor, mechanical engineer, curtain wall consultant, facade engineer, and facility manager.

Building Information Modeling (BIM) has been used by various construction engineering (ConE) programs to fulfill the Body of Knowledge (BOK) requirements, such as cost estimating, construction scheduling and control, project administration, and contract documents. Currently a number of BIM software packages are available to ConE educators. However, guidance to select an appropriate BIM software and an understanding of how this software can be used to instruct aforementioned requirements are minimal to nonexistent. This paper seeks to address these challenges by developing a BIM model using Autodesk Revit. Then 4D simulations and clash detection are performed. How to conduct cost estimating, the 5th dimension of the BIM model, is recommended. This paper outlines strengths and limitations of software packages used in this study, and presents a suggested work flow for a future BIM course. By providing a template to integrate BIM into an existing course or implement a standalone BIM course within construction engineering curricula, this paper should have great potential to directly benefit ConE educators throughout the country.