Bidding is a very competitive process in the construction industry; each competitor’s business is based on winning or losing these bids. Contractors would like to predict the bids that may be submitted by their competitors. This will help contractors to obtain contracts and increase their business. Unit prices that are estimated for each quantity differ from contractor to contractor. These unit costs are dependent on factors such as historical data used for estimating unit costs, vendor quotes, market surveys, amount of material estimated, number of projects the contractor is working on, equipment rental costs, the amount of equipment owned by the contractor, and the risk averseness of the estimator. These factors are nearly similar when estimators are estimating cost of similar projects. Thus, there is a relationship between the projects that a particular contractor has bid in previous years and the cost the contractor is likely to quote for future projects. This relationship could be used to predict bids that the contractor might quote for future projects. For example, a contractor may use historical data for a certain year for bidding on certain type of projects, the unit prices may be adjusted for size, time and location, but the basis for bidding on projects of similar types is the same. Statistical tools can be used to model the underlying relationship between the final cost of the project quoted by a contractor to the quantities of materials or amount of tasks performed in a project. There are a number of statistical modeling techniques, but a model used for predicting costs should be flexible enough that it could adjust to depict any underlying pattern. Data such as amount of work to be performed for a certain line item, material cost index, labor cost index and a unique identifier for each participating contractor is used to predict bids that a contractor might quote for a certain project. To perform the analysis, artificial neural networks and multivariate adaptive regression splines are used. The results obtained from both the techniques are compared, and it is found that multivariate adaptive regression splines are able to predict the cost better than artificial neural networks.

State Highway Agencies (SHAs) are being forced to focus more on rehabilitation, resurfacing, and reconstruction of existing roadways rather than the construction of new facilities. These activities can create several challenges when they must be conducted on roadways with high traffic volumes. This research identified numerous strategies that can be implemented by SHAs that have the potential to influence the overall success of roadway projects with high traffic volumes. This research also created a framework for when to implement these different strategies. These strategies were identified through an in-depth literature review and through case studies conducted on highway projects that were under construction. Through the case studies the different strategies were documented within the context that warranted their use. Information from the case studies was collected and documented through interviews and site visits. The strategies identified through this research were used to create four matrices that summarize the research findings. A general matrix was created to show the motivating project conditions that warrant the use of each strategy. A public relations matrix was created to display the influence the impacted road user groups have on public relations and information strategies. A traffic management matrix was created to show different types of traffic management strategies and the potential impact they will have on the project. Finally, an interdependency matrix was created to show groups of strategies that are related to each other or require the use of other strategies to be able to influence the success of the project. These matrices could be further developed to create a set of guidelines that could be used by a SHA during the planning phases of a roadway project.

Knowledge of construction duration is pertinent to a number of project planning functions prior to detailed design development. Funding, financing, and resource allocation decisions take place early in project design development and are significantly influenced by the construction duration. Currently, there is not an understanding of the project factors having a statistically significant relationship with highway construction duration. Other industry sectors have successfully used statistical regression analysis to identify and model the project parameters related to construction duration. While the need is seen for such work in highway construction, there are very few studies which attempt to identify duration-influential parameters and their relationship with the highway construction duration. This research identifies the project factors, known early in design development, which influence highway construction duration. The factors identified are specific to their respective project types and are those factors which demonstrate a statistically-significant relationship with construction duration. This work also quantifies the relationship between the duration-influential factors and highway construction duration. The quantity, magnitude, and sign of the factor coefficient yields evidence regarding the importance of the project factor to highway construction duration. Finally, the research incorporates the duration-influential project factors and their relationship with highway construction duration into mathematical models which assist in the prediction of construction duration. Full and condensed models are presented for Full-Depth Section and Highway Improvement project types. This research uses statistical regression analysis to identify, quantify, and model these early-known, duration-influential project factors. The results of this research contribute to the body of knowledge of the sponsoring organization (Virginia Department of Transportation), the highway construction industry, and the general construction industry at large. / Ph. D.

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US transportation agencies are dealing with shrinking budgets, limited work forces, and deteriorating infrastructure. In order to cope with funding uncertainty, state highway agencies are now looking into their own organizations and identifying programs, practices, and processes that have potential for cost saving. A quality assurance (QA) program is an integral part of highway construction and ensures a project’s contracted level of quality. The cost of quality (conforming and nonconforming) can constitute a sizable part of total construction cost. As the quality assurance programs evolved, various practices and processes were developed over time and later adopted by state highway agencies. These practices and processes include different QA standards and specifications, varying testing methods, central testing lab vs. on site testing, performance based vs. prescribed quality assurance practices, implementation of innovative quality assurance practices, etc. Therefore, there is an opportunity to assess different QA strategies and recommend those practices that are effective and cost efficient. A national survey was conducted by the authors, which provided a detailed mapping of various QA practices and processes used as part of QA programs and identified areas where agencies can focus on for cost savings. The survey found that QA sampling and testing plans, optimization of sampling plans, optimization of QA standards and specifications, and implementation of innovative test methods and processes are the main areas the agencies should focus to lean the current QA programs.

Collecting load tickets is an example of an antiquated practice that puts inspectors in harm’s way either adjacent to traffic, in close proximity to moving or backing equipment, or at times requires climbing onto trucks to reach tickets. Technology exists to collect this information electronically allowing for safer, efficient inspection methods. Departments of Transportation are charged with inspecting an increasing work load with a diminishing number of inspection staff. Recently, doing more with less has led to the prioritization of inspection activities and resulted in less collection of data and visual inspection on projects. Technology advancements are available to improve data collection and provide for more efficient inspection. Using GPS and GIS technology tied into electronic scale report-out systems, a fleet tracking system traces haul routes, reports travel time and tonnage, and even assists contractors with equipment matching and balancing. Data from this system coupled with other technologies remote monitoring of temperature, intelligent compaction, and network enabled cameras provide an opportunity to enhance inspection and increase construction inspection productivity all the while enriching detail of project records. The contribution of this paper is to provide a framework in which to combine these technologies into a multi-faceted, enhanced inspection approach.

Environmentally conscious construction has received a significant amount of research attention during the last decades. Even though construction literature is rich in studies that emphasize the importance of environmental impact during the construction phase, most of the previous studies failed to combine environmental analysis with other project performance criteria in construction. This is mainly because most of the studies have overlooked the multi-objective nature of construction projects. In order to achieve environmentally conscious construction, multi-objectives and their relationships need to be successfully analyzed in the complex construction environment. The complex construction system is composed of changing project conditions that have an impact on the relationship between time, cost and environmental impact (TCEI) of construction operations. Yet, this impact is still unknown by construction professionals. Studying this impact is vital to fulfill multiple project objectives and achieve environmentally conscious construction. This research proposes an analytical framework to analyze the impact of changing project conditions on the relationship of TCEI. This study includes green house gas (GHG) emissions as an environmental impact category. The methodology utilizes multi-agent systems, multi-objective optimization, analytical network process, and system dynamics tools to study the relationships of TCEI and support decision-making under the influence of project conditions. Life cycle assessment (LCA) is applied to the evaluation of environmental impact in terms of GHG. The mixed method approach allowed for the collection and analysis of qualitative and quantitative data. Structured interviews of professionals in the highway construction field were conducted to gain their perspectives in decision-making under the influence of certain project conditions, while the quantitative data were collected from the Florida Department of Transportation (FDOT) for highway resurfacing projects. The data collected were used to test the framework. The framework yielded statistically significant results in simulating project conditions and optimizing TCEI. The results showed that the change in project conditions had a significant impact on the TCEI optimal solutions. The correlation between TCEI suggested that they affected each other positively, but in different strengths. The findings of the study will assist contractors to visualize the impact of their decision on the relationship of TCEI.

abstract: The demand for new highway infrastructure, the need to repair aging infrastructure, and the drive to optimize public expenditures on infrastructure have led transportation agencies toward alternative contracting methods (ACMs) such as design-build (DB) and construction manager/general contractor (CM/GC). U.S. transportation agencies have substantial experience with traditional design-bid-build delivery. To promote ACMs, the Federal Highway Administration and the National Cooperative Highway Research Program (NCRHP) have published ACM guidance documents. However, the published material and research tend to focus on pre-award activities. The need for guidance on ACM post-award activities is confirmed in NCHRP’s request for a guidebook focusing on ACM contract administration (NCHRP 2016). This dissertation fills the crucial knowledge gap in contract administration functions and tools for DB and CM/GC highway project delivery. First, this research identifies and models contract administration functions in DBB, CM/GC, and DB using integrated definition modeling (IDEF0). Second, this research identifies and analyzes DB and CM/GC tools for contract administration by conducting 30 ACM project case studies involving over 90 ACM practitioners. Recommendations on appropriate use regarding project phase, complexity, and size were gathered from 16 ACM practitioners. Third, the alternative technical concepts tool was studied. Data from 30 DB projects was analyzed to explore the timing of DB procurement and DB initial award performance in relation to the project influence curve. Types of innovations derived from ATCs are discussed. Considerable industry input at multiple stages grounds this research in professional practice. Results indicate that the involvement of the contractor during the design phase for both DB and CM/GC delivery creates unique contract administration functions that need unique tools. Thirty-six DB and CM/GC tools for contract administration are identified with recommendations for effective implementation. While strong initial award performance is achievable in DB projects, initial award performance in this sample of projects is only loosely tied to the level of percent base design at procurement. Cost savings typically come from multiple ATCs, and innovations tend to be incremental rather than systemic, disruptive, or radical. Opportunity for innovation on DB highway projects is influenced by project characteristics and engaging the DB entity after pre-project planning. / Dissertation/Thesis / Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2019

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