Funded Research Projects

Investigators: Dr. David Jeong (PI), Dr. Charles Gurganus (Co-PI) and Dr. Kunhee Choi (Co-PI) 

Funding Agency: Texas Department of Transportation

Objective: Understanding and estimating realistic production rates of major work items in a highway project are critical to determining a reasonable project contract time, evaluating the contractor’s baseline schedule, monitoring progress schedules, and ultimately completing the project on time. This project will leverage Texas DOT’s historical project data to extract the as-built information of production rates of various work items, and develop an interactive tool that can generate color-coded heat maps to visualize feasible ranges of production rates of major work items across Texas. An advanced and interactive production rate estimation tool could significantly help Texas DOT achieve the goal of completing highway projects on time. Such a decision-aid tool could significantly improve Texas DOT’s practice in determining more accurate production rates, resulting in more accurate contract time determination, the contractor’s baseline schedule evaluation, and progress schedule monitoring. Such data-driven and practical production rates may provide higher certainty and reliability than the Texas DOT’s current construction production rates table. 

Investigators: David Jeong (PI) and Julie Hartell (Co-PI)

Sponsor: National Science Foundation (NSF)

Objectives: This new NSF REU site focuses on emerging digital technologies and processes that are promising to transform construction into a safer, more productive, more resilient, and more sustainability cognizant sector. Undergraduate students in construction science and related disciplines have largely been neglected and underserved by research opportunities. Undergraduate students, especially underrepresented minorities and women, from programs that lack research resources and opportunities will participate in the proposed summer research experience program, which will be reinforced with enrichment activities and professional development activities. Students will participate in highly innovative research projects sponsored by federal and state funding agencies, as well as industry sponsors. REU students will conduct research on virtual reality, 3D-printing, drones, digital twins, robotics, smart materials, energy simulation, machine learning and artificial intelligence in the context of construction innovation. The participating students will learn analytical skills, independent thinking and cognitive skills, and collaboration skills that are required for developing, testing, and validating emerging digital technologies and sustainable solutions for construction. This unique REU program will provide STEM-field undergraduate students another clear career path to construction through research and innovation.

Investigators: Dr. David Jeong (PI), and Dr. John Habermann (Co-PI), through Interagency Contract (IAC)

Funding Agency: Texas Department of Transportation

Objective: Change orders are the main reason for project delay and cost overrun. The most common reason for change orders in highway projects is “work item omission in contract” which accounts for more than two third of change orders. Identification of missing work items before bid advertisement will significantly help reduce the number of change orders during construction and it will help determining a realistic project budget. Recent advancement in artificial intelligence (AI) can serve as a key technique for the systematic identification of potentially missing work items in a new project by analyzing the space of historical project data. Specifically, as a sub-field of cutting-edge AI techniques, Natural Language Processing (NLP) enables a computer to analyze, understand, and derive meaning from human language in a smart and computational way. Innovative NLP techniques will contribute to the automated and reliable identification of potential change order items due to missing work items. 

Investigators: Dr. David Jeong (PI) and Dr. Ashrant Aryal (Co-PI)

Sponsor: SHELL, Inc.

Objective: Our objective is to explore the current issues in interoperability between P&ID and 3D models. While software vendors might claim that they are compliant with existing data sharing standards, it is likely that not all the necessary data is exported or imported correctly when switching from one software ecosystem to another. We plan on identifying these issues by exploring current systems and their compatibility of sharing and editing models between each other.

Investigators: Dr. KC Choi (PI) and David Jeong (Co-PI)

Funding Agency: Texas Sea Grant – National Oceanic and Atmospheric Administration (NOAA)

Objective: this project is to create, test, and deploy a cyber-enabled digital twinning web platform. We call the proposed cyber-physical platform “AI-based Roadway Flooding Digital Twinning,” or AIR-FLOOD. This novel digital platform measures, maps, and informs interdependent flood/roadway risk for coastal communities seeking to better prepare for future extreme flooding events, providing them with advanced predictive knowledge about the best possible evacuation routes from affected areas, according to a variety of inundation scenarios. The platform will also provide first responders with vital information regarding the best routes for efficient travel to affected communities and offer local and state transportation agencies the information necessary to optimize prioritization of their emergency roadway repair projects.

Investigators: Dr. H David Jeong (PI)

Funding Agency: Montana Department of Transportation 

Objective: In recent years, artificial intelligence (AI) technologies have improved their technical capabilities for pattern recognition and prediction. Promising AI techniques such as Artificial Neural Networks (ANNs) are capable of processing various types of data and learning complex patterns to make a prediction with reliable accuracy. An AI based data-driven model can leverage historical project characteristics and performance data to estimate a reliable project duration for a new project. In this research, an AI-based model and its tool will be developed using historical highway project data to estimate the duration of a highway project with a certain level of confidence. This project will provide a robust support tool for a quick and reliable estimation of a project’s duration.

Investigators: Dr. Charles Gurganus (PI) and Dr. H. David Jeong (Co-PI)

Funding Agency: Texas Department of Transportation

Objective: This project is to develop training modules on the theme of “Basic Construction Schedule Analysis” for TxDOT that encompass various schedule development and analysis issues including schedule requirements, production rate and duration analysis, activity relationship analysis, critical path identification, and recommended communication requirements with contractors during construction.

Investigators: Dr. H. David Jeong (PI), Dr. Zofia Zybkowski (Co-PI), and Dr. Bugrahan Yalvac (Co-PI)

Funding Agency: T3 Grant - Texas A&M University

Objective: The construction industry is one of the largest industry sectors in the U.S. and globally. However, compared with the other industry sectors, the construction industry has failed to improve the productivity over the last fifty years. There is no lack of evidence that the main causes of this failure are poor construction project management and organizational issues.  Effective construction project management theories and principles have evolved over time and they are taught in academia and in industry in traditional settings. However, those theories and principles are inherently highly soft and intangible concepts that affect the entire project life cycle involving a large size of workflows, and interactions between project participants. The current and traditional teaching and learning models are not effective for students and trainees to truly understand and learn actional knowledge for change in the construction industry. One of the most successful ways to impart actional knowledge of those soft theories and principles are through serious games and participatory simulations. These often take the form of low-risk, small-scale controlled experiments that help students determine the impact of a decision before scaling a proposed process up to a full project; and create an enjoyable and effective lesson by imparting an “aha moment” to stakeholders. This project will develop virtual participatory simulation games for teaching and learning construction project management theories and principles and test the efficacy and value as an alternate to existing teaching and learning methods. In addition, the COVID-19 pandemic has created a new business environment where most training and education sessions must be delivered in a virtual setting. Thus, this research topic is timely since the ultimate goal is to test and develop the most efficient education and training approach for construction project management theories and principles in a virtual environment.

Investigators: Dr. H. David Jeong (PI) and Dr. Zofia Zybkowski (Co-PI)

Funding Agency: Construction Industry Advisory Council (CIAC), Department of Construction Science, Texas A&M University

Objective: This project is to develop an on-line, collaborative, virtual simulation games for rapid dissemination of lean design and construction principles, methods and tools to the construction industry Developing virtual lean simulation games is challenging because the games should be dynamic, fun, and interactive to ensure the same or preferably higher level of participation and engagement of the participants as with the in-person games, and more importantly, the same or higher level of learning through virtual settings compared with in-person settings. We will test and hone our proof-of-concept simulation on volunteer practitioners using feedback from pre- and post- simulation surveys and develop an implementation strategy for industry. This research initiative will ultimately contribute to improving construction productivity and efficiency of construction projects.

Investigators: Dr. H David Jeong (PI), Dr. Doug Gransberg (Co-PI), and Dr. KC Choi (Co-PI)

Funding Agency: National Cooperative Highway Research Program (NCHRP)

Objective: This project is to develop a national guide for state DOT staff responsible for scoping projects to improve early construction contingency estimates for common project types with differing levels of complexity. This research focuses on early construction contingency among cost components that impede the accuracy of project cost estimating. The guide will identify project-related risks that are associated with significant cost impacts and that affect the accuracy of early construction contingency estimates compared to actual construction costs.

Investigators: David Jeong (PI) with Phil Barutha (Univ of Nebraska)

Sponsor: Construction Industry Institute (CII)

Objectives: The overarching essential research question that will be evaluated in this study is whether there is a measurable increase in project success rates using I2PD and other related integrated delivery methods as compared to traditional delivery methods. The results of this research will be able to confirm whether the findings of CII RT 341 are consistent with the actual results on live projects that apply some or all of the principles and methods of I2PD.

Investigators: Dr.Ghang Lee (PI, Yonsei)  and David Jeong (Co-PI)

Sponsor: Institute of Information and Communications Technology Planning and Evaluation (IITP, Korea)

Objectives: This research project is an international collaboration with a group of researchers at Yonsei University in South Korea. The goal is to explore and test AI and NLP-based algorithms that can analyze major construction contract requirements from a large number of historical construction contract documents and then, automatically extract and organize major contractual requirements of a new project with a possible level of risk identification.

Investigators: David Jeong (PI) with Brett Huntsman and Maxwell Steadman 

Sponsor: South Dakota Department of Transportation

Objectives: This research project will identify current and potential uses for construction cost indices in South Dakota Department of Transportation and develop methodologies for calculating, maintaining, and using a CCI for each use.  This study will also evaluate the level of risk or uncertainty for projections made using the CCI methods.

Investigators: David Jeong (PI) with Co-PIs: Doug Gransberg, Ali Touran, and KC Choi

Sponsor:  National Cooperative Highway Research Program (NCHRP)

Objectives: This research aims at developing a comprehensive guidebook encompassing procedures, methods, and tools for determining contract time that can work for a wide spectrum of highway infrastructure projects.  A systematic approach and a risk-based methodology will be employed to provide reliable contract time estimation methods over the project delivery process. 

Investigators: David Jeong (PI), Jennifer Shane, Kevin Scheibe, and Sree Nilakanta

Sponsor: Iowa Highway Research Board (IHRB)

Objectives: This research project has developed a historical performance driven framework and its algorithms to determine the life cycle cost of major maintenance units of equipment for local agencies. The algorithms allows the agency to determine the most optimal economic replacement or purchase timing options for each type of equipment.

Investigators:  Bora Cetin (PI), David Jeong (Co-PI), and Jeramy Ashlock (Co-PI)

Sponsor: Iowa Highway Research Board (IHRB)

Objectives: This research project developed a data driven Granular Roadway Asset Management System (GRAMS) to assist local agencies in making more reliable gravel loss estimation and consequently determining annual rock requirements for proper budgeting purposes. In this study, a series of online and in-person meetings and interviews were conducted along with electronic mailing surveys to gather information to develop a user-friendly GRAMS. Advanced statistical analysis methods such as the beta regression model and survival analysis were used as computational algorithms for estimation and risk analysis. When the user enters several input values, the GRAMS can generate a range of estimates under varying budget conditions and different levels of service. The tool is expected to significantly help local agencies to obtain consistency in terms of estimating gravel loss and determining rock requirement and as a result, better defend their granular road maintenance budget requests and management. This tool is primarily based upon empirical data and further calibration is recommended for enhanced estimation.

Investigators: David Jeong (PI) and Joseph Shrestha

Sponsor: Montana Department of Transportation

Objectives: As an extension of MDT8232, the research team developed a Unit Price Visualization Tool which is a powerful tool to generate visual analytics results on a map showing the distribution of interpolated unit prices of bid items across the state. The tool can serve as a quick tool to determine the unit price of a bid item in a certain location based on historical data and interpolation algorithm. It will also help evaluate different unit prices of the same bid item based on its location and quantity of work. The tool is an add-in tool to Environmental Systems Research Institute (ESRI) ArcGIS for Desktop application. 

Investigators: Peter Savolainen (PI) and Co-PIs : Anuj Sharma, David Jeong, Jennifer Shane and others 

Sponsor: Federal Highway Administration (FHWA)

Objectives: In the United States, traffic volumes and congestion are projected to continue increasing, though limited growth is expected in roadway capacity. Consequently, transportation agencies are increasingly focused on maintaining and extending the lifespan of the transportation infrastructure. To this end, it is imperative that construction and maintenance activities can be conducted in a manner that optimizes safety while maintaining acceptable levels of mobility. This project will focus on further improvement areas of additional guidance, training opportunities, and decision support tools for highway work zone safety. Key topics include Post Project Construction Evaluation, Work Near Signalized/Unsignalized Intersections, Traffic Management/Safety During Winter Construction and Severe Weather, Low-Cost Work Zone Management Strategies, and Development and Application of Work Zone Crash Modification Factor. Based on research findings, a series of training materials will be developed. 

Investigators: David Jeong (PI) and Doug Gransberg (Co-PI) 

Sponsor: Montana Department of Transportation

Objectives: this research project will develop construction activity sequencing logics for different types of highway projects based on historical data, which can help quickly identify the most common work sequence of the given project and determine the project schedule. Results are expected to significantly improve the accuracy and reliability of project scheduling practices. 

Investigators: David Jeong (PI), Evegeny Chukharev-Hudilainen, and  Stephen Gilbert

Sponsor: National Science Foundation (NSF)

Objectives: This research proposes a novel approach for a fast and unambiguous reuse of digital models for the civil infrastructure industry by developing an automated data retrieval engine capable of recognizing user information from their natural language queries (e.g., words, phrases, questions) and extracting the desired data from heterogeneous digital datasets by employing the recent advances in Natural Language Processing (NLP) techniques, machine-learning based semantic measure methods to develop the data retrieval system. 

Investigators: David Jeong (PI) with Doug Gransberg and Ali Touran

Sponsor: Construction Industry Institute (CII)

Objectives: Industrial capital projects are risky business ventures. As they increase in size, uncertainty, and complexity, they frequently experience corresponding growth in cost and time to deliver, and they often fail to achieve their desired performance results. The risk-shedding structure of current project delivery methods not only creates a non-collaborative environment that impedes the performance of industrial projects; risk-shedding can also stifle innovation and create inefficiencies during industrial project delivery processes. For decades, the delivery of industrial capital projects has suffered from stunted productivity growth and little broad industry innovation while seeing an increase in claims and disputes. Traditional project delivery has not proven to be reliable for industrial capital projects. The industry is open to alternatives. This research project evaluated the potential of increasing integration and collaboration by applying the principles of IPD and Alliancing in the industrial capital projects sector. The key findings from this project is that the increased integration and collaboration that come from implementing IPD and Alliancing principles can lead to better project performance and increase the probability of successfully achieving the business case objectives of an industrial project.

Investigators: David Jeong (PI) with Chuck Jahren, Jennifer Shane, and Kristen Cetin

Sponsors: Iowa Highway Research Board (IHRB) and Midwest Transportation Center (MTC)

Objectives: Due to an array of advanced digital technologies, much of today’s transportation project data are available in digital format. However, due to the fragmented nature of the highway project delivery process, the growing amount of digital data is being archived and managed separately. This makes it difficult for professionals to take full advantage of the efficiencies of digitized data and information. The purpose of this research was to identify current data workflows and areas for improvement for five of the most common types of highway assets—signs, guardrails, culverts, pavements, and bridges—and offer guidance to practitioners on how to better collect, manage, and exchange asset data. For each type of asset, an information delivery manual (IDM) was developed. Each IDM consists of several process maps (PMs) and one exchange requirement (ER) matrix. A total of 15 PMs and 5 ER matrices were developed. A set of limitations in current data workflows was identified and a set of recommendations to overcome those limitations was also determined and documented. The conclusion was that current data workflows were designed mostly for contract administration purposes. Thus, more efficient asset-centric data workflows need to be implemented to truly streamline the data workflows throughout an asset’s life cycle and minimize wasted resources in recreating data in the asset maintenance stage.

Investigators: Ashley Buss (PI), David Jeong, and Fatih Bektas

Sponsor: Iowa Department of Transportation

Objectives: Pavement preservation has shown to improve pavement performance and the desire to maximize Iowa’s infrastructure investment has led Iowa to invest in collecting pavement performance information. This data, combined with construction and contract records can provide a better understanding of the effectiveness of pavement preservation techniques on Iowa roadways. The overreaching objective of pavement preservation is to bolster the pavement network investment and maintain a higher level of service. This research aims to improve the understanding of pavement preservation strategies in Iowa by focusing on three key areas: (1) documenting the effectiveness using both qualitative and quantitative metrics (2) understanding the important factors that influence the performance of pavement preservation strategies and (3) develop guidance for selecting pavement preservation techniques considering cost and roadway characteristics such as traffic, pavement condition and construction history.

Investigators: David Jeong (PI) with Doug Gransberg

Sponsor: Iowa Department of Transportation

Objectives: The estimation of design efforts and costs plays a vital role in authorizing funds and controlling the budget during the project development process. Typically, the design phase consists of various engineering activities that require substantial efforts to deliver the final construction documents for bid preparation. Estimating these efforts accurately and efficiently is critical for transportation agencies in properly allocating funds, time, and resources. Therefore, there is a need for a proactive scheme to more accurately and reliably estimate design efforts and costs in order to help the design office negotiate more confidently with consulting firms and ultimately to enhance the accountability and transparency of funding decisions. In this Phase I study, historical data driven estimation models including regression models ad neural networks models were developed and tested. Also, a case-based reasoning–based tool was developed to make logical inferences when estimating the design efforts and costs of bridge design efforts. The case-based reasoning approach uses the concept of similarity scores to retrieve the records of the most similar historical bridge design projects. 

Investigators: Doug Gransberg (PI), David Jeong, Chris Harper, and Carla Del Puerto

Sponsor: National Cooperative Highway Research Program (NCHRP)

Objectives: Partnering has historically been viewed as a highly formalized process that encourages cooperation, joint problem solving, and focusing as a team on the objectives of a project.  Key elements of the partnering process have included partnering charters, escalation ladders, periodic workshops and meetings, and performance tracking among other things. The goal of this research project is to provide guidelines for applying the principles of partnering to projects delivered by all alternate contracting methods as well as traditional low bid projects. It also expands and extends the definition of partnering in the highway agency’s context to include program-level initiatives designed to avoid project-level disputes and to swiftly resolve them when they do arise.

Investigators: David Jeong (PI) and Doug Gransberg

Sponsor: Montana Department of Transportation (MDT)

Objectives: A highway construction cost index (HCCI) is an indicator of the purchasing power of a highway agency. Thus, it must reflect the actual construction market conditions. However, the current method is not robust enough to meet this primary goal due to (1) a significantly insufficient sample size of bid items used in HCCI calculation and (2) inability to address the need to track cost trend of construction submarket segments such as, but not limited to, various project types, sizes, and locations. This study develops an advanced methodology to overcome these apparent limitations using two new concepts: (1) dynamic item basket; and (2) multidimensional HCCIs. The dynamic item basket process identifies and utilizes an optimum amount of bid-item data to calculate HCCIs in order to minimize the potential error due to a small sample size, which leads to a better reflection of the current market conditions. Multidimensional HCCIs dissect the state highway construction market into distinctively smaller sectors of interest and thus allow the agency to understand the market conditions with much higher granularity. The new methodology is expected to help making more-reliable decisions in preparing business plans and budgets with more accurate and detailed information about the construction market conditions. 

Investigators: Sri Sritharan (PI), Siemens, David Jeong and others

Sponsor: Department of Energy (DOE)

Objectives: Wind power generation has seen a dramatic increase in the 21st century and the Department of Energy (DOE) envisions that wind energy will become a much larger part of overall power generation in the U.S. by 2050. Wind turbines have continued to grow in size during the last few decades with towers commonly built at a height of 80 m for typical utility-scale turbines in the Unites States. The newly proposed tall tower technology has been designed to be cost-effective in assembling towers as high as 140 m from precast concrete module components that are capable of transport on the U.S. road system. This goal of this research is to present an alternative wind tower design with potential for reducing the overall levelized cost of energy (LCOE). It evaluates a hexagonal precast concrete wind tower solution that facilitates use of a taller wind turbine generator for harvesting stronger, steadier, and more frequent wind resources to increase wind energy production and lower the overall LCOE. The research team developed a stochastic life cycle cost model using a parametric estimate of the cost and fabrication and assembly schedules of this new wind tower design concept as well as forecasting the projected revenue to be created by the new technology.  The key result is that this new design concept is a commercially viable solution, with an estimated LCOE savings ranging from 2% to 4% compared with the typical 80 m turbine deployed in the United States, and it also provides wind power potential to previously untapped regions in the country.  This research result helps to inform energy developers, manufacturers, and policy makers, both regionally and nationally, of a possible economically feasible wind tower design solution. 

Investigators: Doug Gransberg (PI) and David Jeong (CoPI)

Sponsor: Montana Department of Transportation (MDT)

Objectives: A better understanding of top-down estimating practices, and the resulting increases in the accuracy of budgeting efforts, may have significant contributions to public transportation agencies in their efforts to allocate agency funds more efficiently. However, regardless of the vast improvement in computing technologies over the past 30 years, no great advancement in CCE accuracy has been made, preventing state highway agencies from using these technologies within their work. This research project has applied an artificial neural network methodology, as well as a multiple regression estimation model, to compare prediction accuracy of proposed estimating approaches to those achieved under Montana Department of Transportation’s current practices. The research concluded that these advanced methods may provide higher confidence in estimating construction costs in early project development stages.

Investigators: David Jeong (PI), Omar Smadi, Doug Gransberg

Sponsor: Midwest Transportation Center (MTC)

Objectives: Most local agencies in Iowa currently make their pavement treatment decisions based on their limited experience due primarily to lack of a systematic decision-making framework and a decision-aid tool. The lack of objective condition assessment data of agency pavements also contributes to this problem. This study developed a systematic pavement treatment selection framework for local agencies to assist them in selecting the most appropriate treatment and to help justify their maintenance and rehabilitation decisions. The framework is based on an extensive literature review of the various pavement treatment techniques in terms of their technical applicability and limitations, meaningful practices of neighboring states, and the results of a survey of local agencies. The treatment selection framework involves three different steps: pavement condition assessment, selection of technically feasible treatments using decision trees, and selection of the most appropriate treatment considering the return-on-investment (ROI) and other non-economic factors.

Investigators: David Jeong (PI) and Doug Gransberg

Sponsor: Minnesota Local Road Research Board (LRRB)

Objectives: Many urban counties and cities in Minnesota spend more than 30% of their transportation project budgets for ROW acquisition and it continues to become more expensive, difficult, and time consuming for project implementation. This project a) identified barriers and obstacles that occur during the ROW acquisition process that result in cost increases and delays and b) developed recommendations for change in order to improve the existing ROW acquisition process and practices. In total, twenty-two barriers were identified under the categories of project delivery, agency’s internal capability, relationship with the public, appraisal and acquisition, and legislative and other issues. The root causes of those barriers were also analyzed, including uncooperative work environment, lack of tools and methods, distrust of property owners, and adverse effect of eminent domain law. This project has developed twenty five different recommendation ideas for change. Implementation of these ideas is anticipated to greatly enhance the ROW acquisition process but strong leadership and support from top management to implement those ideas are required.

Investigators: David Jeong (PI) and Doug Gransberg

Sponsor: Mid-America Transportation Center

Objectives: A significant amount of time and effort is invested to collect and document various field activity data of a highway project in Daily Work Report (DWR). Although there are many potential benefits of DWR data, the current use of the data is very limited. The objective of this study is to develop an ideal framework for an advanced DWR system to improve the DWR data collection and utilization practices. The study concluded that there is a huge gap between the current and potential level of benefits of DWR data. The challenges for better collection and utilization of DWR data were identified and classified. An ideal framework for an advanced DWR system was developed to overcome those challenges. The ideal framework consists of seven major components: a) data attributes and its relations, b) integration with existing systems, c) visualization of data, d) advanced data collection systems, e) automation of DWR data analysis and reporting, f) human factors, and g) other technical aspects.

Investigators: Doug Gransberg (PI), David Jeong (CoPI), Carla Del Puerto

Sponsor: National Cooperation Highway Research Program (NCHRP)

Objectives: Necessary resources must be allocated to the preconstruction process to permit planners and designers the time and funding to be able to solve technical, environmental, and constructability problems before the construction contract is advertised. Research has proven that correcting errors, omissions, and ambiguities in preconstruction is far less expensive than during construction (4). Therefore, ensuring that the preconstruction phases are allocated sufficient funding to adequately complete the necessary investigations, analyses, etc. is a previously unrecognized determinant of not only the project’s final quality but of the agency’s ability to control cost and schedule growth during project delivery.This research has developed a data-driven holistic framework that comprises both top-down and bottom-up approaches to estimate pre-construction costs that meet various stakeholders’ needs during the preconstruction phases of the project. It demonstrates how to complete pre-construction cost estimates at the point in project development where the typical project is assigned a project identification number.

Investigators: Jennifer Shane (PI), Doug Gransberg, David Jeong

Sponsor: National Cooperation Highway Research Program (NCHRP)

Objectives: Infrastructure needs within the United States have changed from building new facilities to replacing, expanding, and renewing existing facilities. The project management issues involved with infrastructure renewal differ from the project management issues for new construction. Correspondingly, new project management approaches must be integrated into mainstream practices for all sizes and types of projects to accelerate project delivery, reduce project costs, and minimize project disputes. This research project developed a set  of strategies, methods and tools to address the challenges of managing modern infrastructure projects that are considerably more complex than traditional projects. The research results are developed based on the five dimensional project management framework. The five dimensions are (1) cost, (2) schedule, (3) technical, (4) context, and (5) finance.

Investigators: David Jeong (PI), Omar Smadi, Doug Gransberg

Sponsor: Iowa Department of Transportation

Objectives: Most local agencies in Iowa currently make their pavement treatment decisions based on their limited experience due to primarily lack of a systematic decision-making framework and a decision aid tool. The lack of objective condition assessment data of their pavements also contributes to this problem. This study developed a systematic pavement treatment selection framework for local agencies in order to assist them in selecting the most appropriate treatment and justify their maintenance and rehabilitation decisions. The framework is based upon extensive literature review on various pavement treatment techniques in terms of their technical applicability and limitations, meaningful practices of neighboring states and the results of a survey of local agencies.  The treatment selection framework involves three different steps: a) pavement condition assessment, b) selection of technically feasible treatments using decision trees, and c) selection of the most appropriate treatment considering the return on investment (ROI) and other non-economic factors. An Excel-based spreadsheet tool that automates the treatment selection framework was also developed. The spreadsheet tool allows users to enter the severity and extent level of existing distresses and then, it recommends a set of technically feasible treatments. The tool also evaluates the ROI of each feasible treatment and if necessary, it can also evaluate the non-economic value of each treatment option to determine the most appropriate treatment for the pavement under consideration. The framework and the Excel spreadsheet tool developed in this study are anticipated to significantly help local agencies in Iowa to improve their pavement asset management practices and make more economic and defensible decisions on pavement treatment selection.

Investigators: Duncan Rose (PI), David Jeong, and others

Sponsor: Water Environmental Research Foundation (WERF)

Objectives: The optimal investment strategy is directed at minimizing total cost to the utility including deferral or avoidance of the consequences to customers and community of failure (alternatively, “maximize benefit to the community”). It is the interaction of the three end of life determinants (physical life, service level/capacity life, and economic life) when applied on a case-by-case basis that sheds insight as to the optimal point at which reinvestment should occur. This research provides discussion on a selective application of the advanced concepts included in the End of Asset Life reinvestment decision-making process tool using a utility practitioner’s data set. The tool provides step- by-step process guidance to the asset management practitioner in making the cyclical transitional decisions between maintenance, operations and capital with a particular focus on the transition to capital reinvestment. In this research, the survivability or the probability of failure of an asset was determined from a survival curve developed through survival analysis provided sufficient, appropriate data is available.

Investigators: David Jeong (PI) and Phil Lewis

Sponsor: Oklahoma Transportation Center (OkTC)

Objectives: A well-documented data with clear data flow, efficient data management system, appropriate tools and/or approaches to store, extract and manage information and knowledge would enhance the potential user’s decision-making process. This research develops a three-tiered framework to integrate data, information and decision-making in highway projects. The study uses the Juran’s Triple Role concept and context graph to illustrate the relationship between data, information and decision-making. The study discusses the complexity of data and information flow through a conceptual 3-D data flow diagram. The evolution of data and information integration in highway agencies is also outlined in the report. Potential methodologies in extracting information from raw qualitative and quantitative data are summarized. The developed framework was applied to three case studies; a) construction daily work reports, b) preconstruction cost estimation, and c) pavement management. In each case study, the three-tiered framework is applied and the current and ideal level of data and information integration for key decisions are identified and areas for improvement are also identified. The developed framework will guide highway agencies how to generate and place right information and knowledge in the hands of decision makers. The implementation of the framework will empower engineers to make informed and justifiable decisions, and lead to the improved accountability of project development and management. In addition, it will allow the active utilization of currently existing databases and justify the continuous and growing data collection efforts by highway agencies. 

Investigators: David Jeong (PI) and Phil Lewis

Sponsor: Oklahoma Transportation Center (OkTC)

Objectives: In a typical pavement management system, several input models which are computational backbones are used to develop maintenance and rehabilitation plans for pavements. Some of the major input models include the classification of pavement families, deterioration curves, and effectiveness of various treatment options. These major input models are currently in active use without any thorough validation using actual pavement condition assessment data. Validation and calibration of existing input models for pavement management systems has been one of the major technical goals by highway agencies. This research project used the proven Knowledge Discovery in Database (KDD) approach to investigate historical pavement condition assessment data in a structured manner in order to evaluate the performance of current input models and developed new models and/or calibrated the existing models for more accurate and reliable planning for pavement maintenance and rehabilitation activities. The performance of newly developed or calibrated input models were compared with the performance of current input models.

Investigators: CD Kim (PI), David Jeong (PI), Dr. YG Kim

Sponsor: Korean Small and Medium Business Administration (KSMBA)

Objectives: Rapid and reliable decision making and management of construction projects has become important as the owners of construction projects require fast delivery of their projects. In addition, rapid development of information technologies has changed the way business is executed in many industry sectors. As mobile personal computers have become more powerful with improved data storage capabilities and wireless connection abilities, mobile personal computer based project management information system (PMIS) is considered to be an onsite project management system for the future. It is anticipated that these mobile PMISs will significantly improve project productivity and work efficiency by allowing real-time access to project information and consequently, rapid and reliable decision making.  The objective of this study is to assess the needs of mobile PMIS information applications for use on construction jobsites and identify useful and desirable functions that must be available in the mobile PMIS tools in order to effectively handle project management issues in the field.

Investigators: Wayne Francisco (PI), David Jeong, CSIRO in Australia

Sponsor: Water Environmental Research Foundation (WERF)

Objective: The prime output of this project is a decision support tool to assist utilities in more effectively implementing their asset management programs to provide reliable service to their customers, while meeting the requirements of the Clean Water Act.

Investigator: David Jeong (PI)

Sponsor: Oklahoma Transportation Center (OkTC)

Objectives: Recent changes in pavement materials costs have impacted the competitive environment relative to the determination of the most cost effective pavement structure for a specific highway project. In response, State highway agencies have renewed their interest in using alternate pavement type bidding procedures to determine the appropriate pavement type. This research project developed an alternative bidding procedure for pavement projects. Not all highway projects are candidates for alternate bidding. A step by step procedure can help identify candidate highway projects for alternate bidding. In addition to developing a deterministic life cycle cost (LCC) analysis model using historical pavement data, the research team also developed a novel approach, namely, realistic LCCA model. This approach uses the association rule mining technique to find and probabilistically organize various treatment patterns applied to pavements in the past. A case study conducted in this project revealed that this realistic LCCA model is more reliable and is expected to provide better predictions on LCC. Moreover, since this approach is based upon the historical performance of pavements, it is expected to be easier to draw consensus between the asphalt and concrete industries in calculating life cycle costs. 

Investigator: David Jeong (PI)

Sponsor: Oklahoma Transportation Center (OkTC)

Objectives: This study presents data driven and component-based preliminary engineering (PE) cost prediction models by utilizing critical factors retrieved from ten years of historical project data obtained from oklahoma department of transportation’s roadway division. The study used factor analysis of covariance and correlation matrix to investigate the significance of factors affecting preliminary engineering cost and identify the correlation among them. A combination of three models; regression models, decision tree models and neural network models were developed to obtain the optimum roadway PE cost models. The results of this project are expected to significantly influence how efficiently and economically highway projects are planned, executed and managed in the early stages of a project. 

Investigator: David Jeong (PI)

Sponsor: Korean Ministry of Land, Transport and Maritime Affairs (KMLTMA)

Objectives: A new construction method called the robot-based construction automation system (RCA) was developed in South Korea and this system was applied to a real scale pilot project. The RCA system is composed of four sub-technologies; (1) automated identification of construction materials, (2) automated structural material assembly without human resources, (3) robotic crane for building construction, and (4) bolting robot for steel structure. Although these individual technologies have already been developed successfully, it was designed for a mid-rise building in the pilot project. The objective of this research is to identify areas to modify in order to apply this new system to high-rise buildings. The research team has analyzed fundamental applicability of the robot-based automated building construction system to high-rise building. A simulation based analysis was conducted to test whether modified technologies will be viable solutions to high rise building projects when RCA is used.

Investigator: David Jeong (PI)

Sponsor: Korean Institute of Construction Technologies (KICT)

Objectives: In the Architectural, Engineering and Construction (A/E/C) industry, advanced information technologies and the availability of the Internet services have significantly improved the efficiency of data flow between parties involved in construction and have allowed faster and easier access and storage of useful and relevant data to construction projects which were not simply available before. Of all the types of data exchanged between players in construction, construction product information constitutes the largest volume. Some leading construction product information providers have increased their business revenues and profits by operating internet based systems in which the information of the construction products is easily accessible and available in various formats to construction product information users. This study has a) identified various factors that are critical for a successful operation of construction products information market, b) identified and developed successful business models that are currently in operation for construction products information systems in the U.S. A/E/C industry, and c) identified organizational and operational structures of successful construction product information providers and developed a set of recommendations as a business implementation plan.

Investigator: David Jeong (PI)

Sponsor: GHD, Inc.

Objectives: The nation’s water and wastewater pipeline networks are aged and deteriorating. As these infrastructure systems approach their design lives, there is an increasing demand for new construction, replacement, rehabilitation, and maintenance projects. Due to limited financial resources, it is important to get the most of the available budget by making the right decision in the planning, design, construction, and operation and maintenance activities. Asset management aims to maintain a desired level of service at the lowest cost throughout the asset’s life using advanced decision-making processes. The main objective of this research is to develop a framework of advanced asset management that improves asset management practices by optimally utilizing the readily available data. A complete historical dataset from an utility organization was used as a case study. Specifically, this study a) developed a Dynamic Deterioration Model that is capable of doing individual and group prediction depending on data availability, b) conducted a performance comparison study between different modeling techniques in the context Dynamic Deterioration Model to obtain the recommendation on the best modeling technique, c) developed an application of life cycle cost analysis model for sewer pipe material selection, and d) developed an application of a risk assessment model that measures the criticality for the prioritization of sewer pipe replacement. 

Investigators: David Jeong (PI), Javier Irizarry (Co-PI) and Daniel Castro (Co-PI) -Georgia Tech

Sponsor: Oklahoma Transportation Center

Objectives: Most highways in the United States were built during the 1960s and 1970s and have exceeded their design lives. Most transportation departments have turned their focal activities from the expansion of the highway system to maintaining, preserving, and rehabilitating the existing road network. With increasing needs of pavement maintenance projects, there is a need for better management strategies for these high demand projects in the nation. This study investigated project management level solutions to optimizing resources, minimizing costs (including user costs), and reducing time for Portland Cement Concrete (PCC) pavement rehabilitation projects. This study extensively evaluated the applicability of the Construction Analysis for Pavement Rehabilitation Strategies (CA4PRS) software program as a potential solution to achieve the goal. The usability of the CA4PRS was assessed by conducting knowledge inventory surveys at Georgia Department of Transportation and Oklahoma Department of Transportation as well as conducting four case studies. Based on the findings of this study, this project has designed an improved planning procedure to find the most efficient project phasing and closure scenario for PCC pavement rehabilitation projects. A set of recommendations was developed to guide highway agencies.

Investigators: David Jeong (PI) and Gary Oberlender (Co-PI)

Sponsor: Oklahoma Transportation Center (OkTC)

Objectives: In the Phase III, the research team developed Daily Work Report (DWR) based estimation models to determine reasonable production rates of controlling activities of highway projects. The study used available resources such as DWR, soil data, AADT and other existing project data from the various divisions of Oklahoma Department of Transportation as primary source in estimating productivity. The study describes the effects and variations of production rates of highway activities to changes in environmental, geographical and project site conditions. The study also includes a relative comparison between a survey of experts’ estimate of production rate and DWR based prediction models. The models would allow ODOT engineers and schedulers to improve their accuracy and save their time in determining more reliable production rates of major highway construction activities by easily adjusting site conditions and project factors.

Investigators: David Jeong (PI) and Gary Oberlender (Co-PI)

Sponsor: The Charles Machine Works, Inc.

Objectives: Many existing utility systems are located in congested or urban areas.  Installation, rehabilitation, or renewal of these systems using traditional open-cut construction causes significant disruption of service, interference of vehicular traffic, unsafe trenches to workers and pedestrians, and disturbance to businesses.  To reduce the disruptions caused by open-cut construction the Horizontal Directional Drilling (HDD) method has been widely accepted and used for installing gas and water lines, telecommunication cables, and electrical conduits. One application where HDD has had only moderate success is in the installation of gravity flow liquid conduits, such as sewer and storm drainage. The on-grade HDD method is a new and technically viable option for constructing gravity sewer pipelines. This study conducted a comprehensive assessment of its technical capabilities and economic feasibilities over the open-cut method.

Investigators: David Jeong (PI), Gary Oberlender, Oswald Chong

Sponsor: Construction Industry Institute (CII)

Objectives: The greatest challenge facing the cost estimating community over the next decade is the loss of knowledge and experience from highly experienced estimators. A large percentage of experienced estimators are retiring and there is a shortage of qualified cost estimators to replace them in the engineering/construction industry. Cost estimating is a critical function for successful capital projects and reliable estimates depend heavily on the competency of cost estimators. Tools that aid the estimating process cannot replace the judgment and experience of a competent estimator. CII Research Team 253 was chartered to find solutions to both issues: the short supply of competent estimators and the loss of knowledge and experience of retiring estimators.  The research team 1) identified cost estimating competencies and investigated the gaps between the current and desired level of cost estimating competencies, 2) identified effective methods to recruit, attract, and retain cost estimators, 3) identified effective methods and strategies to transfer estimating knowledge and skills, 4) developed plans to educate and train new estimators, and 5) identified career paths for cost estimators. 

Investigators: David Jeong (PI) and Gary Oberlender (Co-PI)

Sponsor: Oklahoma Department of Transportation

Objectives: Phase II has expanded from Phase I and developed a standalone computer software to automatically determine the contract time of Tier II and Tier III projects when quantities of controlling activities are given. The developed software, namely, OK-CTDS uses VB.Net as a main programming language to build graphic user interfaces (GUIs) and uses the Microsoft Access as a main database. The program is linked with the Microsoft project to automatically generate a project schedule and the critical path of the project. The OK-CTDS has an internal function to automatically transfer all the project data to the Microsoft project from the developed software. The validation results show that the developed system can produce a reasonably accurate contract time for highway projects.

Investigators: David Jeong (PI), Samir Ahmed (Co-PI), and Gary Oberlender (Co-PI)

Sponsor: Federal Transit Administration (FTA)

Objectives: Transit agencies rely on surveys conducted on on-board buses and trains or in transit stations to obtain vital information and opinions from a cross section of their customers. The goal of this project is to assess the performance of on-board survey methods that are commonly used and have potential to improve the overall quality and the response rate of on-board surveys. This project provides the analysis of field tests and summarizes the results of recent on-board surveys conducted in major cities. The output of this research project is empirical based on statistical comparisons of different sets of on-board survey techniques. The findings of this report are expected to benefit future data collection efforts of on-board surveys.

Investigators: David Jeong (PI) and Gary Oberlender (Co-PI)

Sponsor: Sekisui-SPR Americas

Objective: The goal of this project was to evaluate various practices of rehabilitating transportation drainage structures used by state departments of transportation and identify the best practices.

Investigators: David Jeong (PI)  and Gary Oberlender (Co-PI)

Sponsor: Oklahoma Transportation Center (OkTC)

Objectives: Contract time is the maximum time allowed in the contract for completion of all work contained in the contract documents. An accurate forecast of contract time is crucial to contract administration as the predicted duration and associated cost form a basis for budgeting, planning, monitoring and even litigation purposes. The goal of Phase I study is to develop a systematic process of estimating construction time for Oklahoma Department of Transportation (ODOT) by improving their old processes. ODOT highway projects were classified into three different Tiers through the evaluation of recently completed highway projects and a series of technical meetings and discussions with ODOT design division engineers, schedulers, contractors and FHWA-Oklahoma Division engineers. Phase I has developed a manual system to determine the contract time for Tier II highway projects.