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Doctor of Philosophy with a Major in Building Construction

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Enhancing Organizational Transformation for Design-Build Infrastructure Projects: Design Liability, Construction Quality Assurance, and New Engineering Leadership Requirements

2022-07-29 , Lee, Jung Hyun

Major transportation infrastructure projects have used alternative project delivery, such as design-build (DB), to streamline and expedite project delivery, transferring many roles and responsibilities from state departments of transportation (DOTs) to private actors. One challenge that state DOTs face in their major DB projects is ensuring that the DB team upholds the highest standards of design and construction quality in the integrated design and construction environment. The overarching objectives of this study are to support decision-makers in streamlining project delivery by identifying challenges related to understanding gaps between public owners' expectations and the industry's perception and suggesting recommendations to mitigate the gaps. Most specifically, this study addresses issues found in DB transportation infrastructure projects and recommends innovative solutions to overcome those issues in the following areas: (1) design liability, (2) construction quality assurance, and (3) a new engineering leadership requirement on the DB team. This study utilizes a mixed-method research methodology, combining quantitative and qualitative techniques to identify key areas of variances in the integrated DB infrastructure projects. The data in this study come from a survey and semi-structured interviews. Because of the interdisciplinary nature of the research, it is necessary to capture several viewpoints from a wide range of subject-matter experts (SMEs) from multiple domains, including design consultants, highway contractors, public owners, owner representatives, insurance and legal advisors, and construction engineering and inspection (CEI) specialists. The results show that SMEs had considerably different perceptions regarding the frequency and severity of design claim sources in the DB environment. Inconsistencies between CEI perceptions and DOT requirements for quality assurance roles and responsibilities are identified. The results also highlight that a new engineering leadership requirement on the DB team will add value to large and complex projects. This study contributes to the body of knowledge in proactive design and construction quality management by providing decision-makers insights into design liability issues and opportunities to reduce them, providing guidance on reinforcing the quality assurance program for current and future DB projects, and mitigating gaps between the DOT's expectations and the industry's perceptions. The findings of this study have important implications for future practice and offer constructive guidance on streamlining project delivery in the DB transportation infrastructure market.

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Embodied life cycle assessment and potential environmental impacts of improvement options for detached single-family houses in Atlanta

2019-01-11 , Shirazi, Arezoo

20% of US energy consumption and the consequential environmental impacts are associated with the building sector. Previous studies showed that approximately 30% of a building's life cycle energy is attributed to its embodied energy. The residential housing market alone has a significant impact on US emissions. According to a recent report from the Washington Post, detached single-family houses represent the most common style of housing in major US cities and it is close to 40% for Atlanta. This study focuses on residential buildings in the Atlanta metropolitan area. The overarching objective of this research is to include the changes of building construction methods and building energy codes into an embodied Life Cycle Assessment (LCA) model to evaluate the long-term impacts of improvement options for the residential buildings in the region. The primary contributions of this research are: (1) benchmarking the generic characteristics of existing residential buildings considering building codes and construction changes in the region; (2) investigating the trend of embodied energy and emissions of benchmarked buildings considering the 1970s transition in the construction industry; and (3) identifying potential improvement options for benchmarked buildings and comparing the embodied energy and environmental impacts of identified options. The main findings of this research showed: (1) lower embodied energy and environmental impacts per unit area for houses built before 1970s; (2) lower embodied energy and impacts per unit area for 2-story houses; (3) a range of 1.8 to 3.9 Gj/m2 embodied energy for residential buildings in the region; (4) highest environmental impacts for attic/knee insulation and heating, ventilation and air conditioning (HVAC) units replacement through retrofitting residential buildings; and (5) significant environmental impacts for foundation wall insulation and window upgrading through retrofitting dwellings built before the 1970s. The results of this research highlight the role of the life cycle approach for selecting low emission options during the design and implementation of construction and retrofit actions for residential dwellings. The results could further be used to investigate the potential improvement options for an optimum energy usage while reducing life cycle emissions by renovating existing residential buildings in a region.

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Evaluating supplier diversity development programs (SDDP) from the diverse supplier enterprise (DSE) perspective in the facility management industry

2016-04-04 , Hatcher, Michael B.

Supplier diversity refers to the practice of creating opportunities for historically underutilized populations in the workforce and business arena. Supplier diversity encompasses initiatives specifically designed to increase the number of enterprises owned by people from ethnic minority groups who supply public, private, and/or voluntary sector organizations with goods and services (Ram & Smallbone, 2003). Supplier diversity initiatives were once driven solely by governmental policies focused on ethnic minorities. Also, minority vendor purchasing programs were designed to increase the volume of goods and services purchased by corporations from minority-owned businesses (Giunipero, 1981). Guided by the existing literature related to supplier diversity, this qualitative phenomenological study investigated the current state of Supplier Diversity Development Programs (SDDP) from the diverse supplier perspective. Primarily this research illuminated the (1) lived experiences of DSE Supplier Diversity Development Program participants (2) investigated the extent to which SDDPs eliminate or mitigate barriers/impediments to diverse suppliers previously identified in academic literature, and (3) evaluated the impact of SDDP participation on DSE business capacity development. This study explored and evaluated Supplier Diversity Development Programs to serve as a guide for (a) public and private POs in the facility management industry that currently utilize some supplier diversity development programs and (b) organizations seeking to implement SDDPs in the future. This research identified and posited a series of recommendations for the improvement of existing programs and the creation of new Supplier Diversity Development Programs. This research found that a Supplier Diversity Development Program that aligns program expectation with program delivery will result in greater levels of positive program participation outcomes. In addition this research study found SDDP mitigates DSE barriers/impediments and impacts DSE business capacity development, by way of building relationships, administering education, raising awareness, and creating platforms for access and engagement.

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Challenges and opportunities in environmental planning and permitting on transportation design-build projects

2013-07-03 , Hannon, David

Environmental planning and permitting for transportation projects is often seen as one of the top reasons for project delay. On design-build projects, this process is often treated as the critical path to advertising the project and on all transportation projects many critical phases of the project such as right of way acquisition, final design, and construction cannot begin until the environmental planning process is complete. The objective of this research is to identify challenges to the environmental planning and permitting process and opportunities for managing those challenges. To identify these challenges and opportunities, a synthesis of transportation and design-build research was done along with interviews with agencies leaders at seven State Departments of Transportation (DOTs). Once these challenges and opportunities were identified, example environmental planning documents and requests for proposals were reviewed from various State DOTs to document their usage. Additionally follow up interviews were conducted with environmental planning experts with experience on design-build projects from six of the State DOTs that were previously interviewed. This research contributes to the state of knowledge through providing comprehensive information on environmental planning and permitting challenges that must be managed on design-build transportation projects and opportunities for managing these challenges. Managing the identified challenges by utilizing these opportunities provides transportation agencies with opportunities to make the environmental planning and permitting process on design-build projects more efficient. This research contributes to the state of practice of transportation agencies through providing opportunities for streamlining environmental analysis and permitting that is vital to transportation agencies who strive to accelerate the delivery of design-build projects.

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Industry 4.0 And Short-Term Outlook for AEC Industry Workforce

2021-12-13 , Quintana, Emilio

Technology is uniquely transforming our society to a significant degree. This transformation has been described as Industry 4.0 and encompasses machine learning, computerization, automation, artificial intelligence, and robotics. Industry 4.0 is currently impacting the United States’ workplace and is projected in continue uniquely changing our society over the next twenty years or so. Looking specifically at the AEC industry, this paper researches how the AEC industry workplace could be impacted by Industry 4.0 over the next several years. The hypothesis that jobs more at risk for automation should see low or negative growth and lower wages over the next several years was tested by using U.S. Bureau of Labor Statistics (BLS) occupational wage data and growth projections to create an opportunity value for each occupation, and then evaluating the relationship between the opportunity value and probability of automation. A statistical significance was found between the two variables. The hypothesis that certain skills are particularly associated with high growth/high wage jobs versus low growth/low wage jobs was tested by scraping important skills/qualities from the individual occupational webpages hosted by the U.S. Bureau of Labor Statistics, and then comparing the approximately top 80% of skills scraped between the two groups. Certain skills/qualities were found to be particularly associated with each group. Finally, the occupations associated with the AEC industry were compared with the findings from the first two hypotheses. The discoveries were that the AEC industry is potentially more susceptible to Industry 4.0 than other industries. This research is of significance because research into how the AEC industry workplace will be impacted by Industry 4.0 over the next several years was not found in the research background, and it has implications on potential career choices, skill requirements, and areas of research and development. Recommendations for future work include utilizing new data sources, Monte Carlo simulations, cohort analysis, and cluster analysis to make more specific forecasts on Industry 4.0’s impact on the AEC industry.

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Quantitative analysis for modeling uncertainty in construction costs of transportation projects with external factors

2018-08-23 , Baek, Minsoo

Highway construction costs are subject to significant upward and downward variations from project to project and over time. Variations in construction cost disturb transportation agencies in making right investment decisions and estimating accurate construction costs for projects. Transportation agencies face considerable uncertainty in estimating project costs that often leads to significant over- and under-estimation of highway construction costs. The underestimation of project costs can lead to cost overrun, financial problem, and project delay or cancellation. The overestimation of project costs results in an inefficient budget allocation of public funds that could be used on other needed projects. Transportation agencies can also face credibility issues with the public if cost estimation problems remain unresolved. A wide range of variables has been identified in different studies to explain variations in construction cost. There is a value in conducting a research study that attempts to consider a comprehensive list of variables with potentials to explain the variations. The study needs to simultaneously take into account all possible explanatory variables to examine their relations with construction costs. The overarching objective of this research is to assess the effects of several potential variables on explaining variations in submitted unit price bids for major asphalt line items in highway projects. First, stepwise regression analysis will be utilized to develop an explanatory model for describing variations in the submitted unit price bid. The identified variables used to build the explanatory model are classified into two major tiers. Tier 1 represents project specific factors, such as variables related to project characteristics, project location and its distance to major supply sources and price adjustment clauses. Tier 2 represents global and external factors, such as variables related to level of activities in local highway construction market, macroeconomic indicators and energy market conditions. Secondly, it is shown that there is a significant spatial correlation between construction project cost and geographical location of the project that a generalized linear modeling approach may overlook. Geographically weighted regression analysis will be conducted to develop explanatory models for describing variations in the submitted unit price bids considering the spatial correlation. Lastly, the effect of natural disasters on highway construction costs will be examined. Cumulative sum (CUSUM) control chart will be utilized to monitor and detect the change in submitted unit price bids for hurricane-impacted and not hurricane-impacted areas. The primary contributions of this research to the existing body of knowledge are: (1) creation of a multiple regression model to explain variations in submitted unit price bids; (2) creation of local regression models to describe variations in the submitted unit price bids considering the spatial correlation; and (3) empirical assessment of the impact of natural disasters on the variation in the submitted unit price bids. The primary contributions of this research to the state of practice are: (1) enhancing the capability of cost engineers in preparing more-accurate budgets and bids; (2) aiding a bottom-up estimating approach that requires more knowledge about the projects and market; and (3) helping capital project planners set and adjust the timing of the project lettings in the light of market conditions.

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Evaluating the impacts of enterprise resource planning on organizational performance for small to medium enterprises in manufacturing

2015-04-02 , Sedehi, Arya

Today’s fast-paced global economy has intensified the demand for manufacturing companies to make their products more quickly and with higher quality to meet heightened consumer expectations while reducing costs. This competitive environment requires small to medium enterprise’s (SMEs) to implement well-designed business processes and leverage information technology (IT), such as an Enterprise Resource Planning (ERP) system, within their facilities to become more agile, flexible, and integrated to meet changing market demands. Issues emerge when facility managers lack reliable data on performance and costs, which subsequently impairs even basic decisions for resource allocation or process improvement. Although the benefits of a successful ERP implementation in large firms are recognized, there is a general lack of empirical IT productivity literature focusing on SMEs. This research is expected to contribute to a framework for performance measurement, providing facility decision-makers with important metrics for analyzing their firm’s ability to improve upon competitive priorities. Employing the Delphi process, key performance indicators (KPIs) including time, speed, quality, and cost, and corresponding performance measurement metrics, investigations are conducted between traditional manufacturing processes in SMEs and processes enhanced through ERP adoption. In this longitudinal case study, significant improvements are observed in production operations relative to time following ERP implementation including a reduction in the defect rate, total manufacturing cost, and scrap rate along with increases in on-time delivery and flexibility.

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Modeling and predicting the variation of US highway construction cost

2019-03-25 , Cao, Yang

The U.S. government attaches great importance to highway construction every year. Because of the importance of highway construction projects and the tremendous expenditure, the budgeting and cost control is a significant job for all federal agencies and state Department of Transportation (DOTs). A major problem for highway construction costs is that they exhibit a significant variation over time such as the highway construction cost indexes (HCCI), and it is mainly caused by a complex interactive effect such as market-related and project-specific factors. The variation hinders the estimators to catch the correct trend of the market and thus poses a challenge for both owners, such as state, local Department of Transportation, and contractors, in correct budgeting and cost estimating. The main objective of this PhD research is to explore the explanatory variables and develop machine learning methods to model and predict highway construction cost and examine the accuracy of the forecasting results with those of the existing methods such as regression analysis and time series models. The major promising feature of the proposed methods over existing ones is the capability to explain the non-linear relation, which is prevailing in practice. Besides, there is no restriction for the proposed models, compared to linear regression, which assumes the linear relation and normal-distributed-error, and time series analysis, which requires the stationarity of data before analysis. The dissertation summarizes the results from two parts of the research: (1) Univariate time series index prediction in Long Short-Term Memory; (2) Modeling the unit price bids submitted for major asphalt line items in Georgia, project-specific and macroeconomic factors. A deep learning model based on the recurrent neural network will be developed due to its strength in long term memory and catching the variation of the data. The Texas HCCI is used as an illustrative example because Texas reports a frequent volatile index. The performance of the model will be tested in different prediction scenarios (short-term, midterm and long-term). A dataset containing fifty-seven variables with potential power to explain the variability of the submitted unit price bid are collected in this study. These variables represent a wide range of factors in six aspects: project characteristics, project location and its distance to major supply sources for critical materials, level of activities in the local highway construction market, overall construction market conditions, macroeconomic conditions, and oil market conditions. The machine learning feature selection algorithm Boruta analysis will be used to select the most significant features with the greatest capability to predict the unit price bid for asphalt line items. The partial dependence plots endow the explanatory power to the developed machine learning models. An ensemble learning model will be constructed based on the selected features to forecast the unit price bid. The accuracy of the predicted machine learning models will be compared and validated with the existing multiple regression model and the Monte Carlo Simulation. The main contribution of this research to the body of knowledge in cost forecasting are be summarized from three aspects: first, the research developed a new set of machine learning models that provide more accurate costs forecasts compared to the existing methods. For example, the non-linear machine learning methods are more accurate than the time series models which are frequently used in the former research. In the field of cost research, a small improvement in model accuracy results in a significant amount of actual impact in budget estimation. Second, the modified encoder and decoder architecture performed well in numerical time series data prediction problem. Instead of making one sequence of output, the roll-forward forecasting turned out to be more accurate. Third, from the practical application perspective, the proposed machine learning models can handle a wide range of issues with the input data that are common in the field of highway construction cost forecasting, such as missing values. Another practicality contribution is that methods in this research are applicable to big data which is an industry trend, while most former models were developed based on a small dataset. The research also proposed a construction cost database which will largely provide the convenience for easy utilization of the model. Fourth, the research identified the most significant features to forecast the variation of unit price bids of resurfacing projects in Georgia, and the analysis laid emphasis on the explanatory power of prediction models. With the improved prediction capability, state DOTs can benefit from the proposed models in preparing more accurate budgets and cost estimates for highway construction projects. The analysis process and proposed models in research are also applicable to other time series data prediction and cost estimating problems.

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Changes in quality management approaches for design-build highway projects

2018-04-27 , Lee, Jung Hyun

The purpose of this study is to determine changes in quality management approaches of design-build (DB) highway projects compared to those in design-bid-build (DBB) projects. Identifying the existing challenges in the quality management procedures in DB environment requires conducting a content analysis. This involved reviewing regulations, FHWA policy documents, quality manuals, and state DOT solicitation documents. To obtain a deeper understanding of the state of the practice in state DOTs and to identify best practices in handling the identified challenges, this study conducted structured interviews of DOT personnel and industry experts. The results indicate that responsibility for quality assurance is being transferred to design-build teams. The findings of this study show six areas of changes in DB highway projects: (1) acceptance approaches; (2) selection criteria; (3) independent assurance procedures; (4) non-conformance reports; (5) cost mechanisms; and (6) pay factor adjustment.

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Analysis of construction cost variations using macroeconomic, energy and construction market variables

2014-06-20 , Shahandashti, Seyed Mohsen

Recently, construction cost variations have been larger and less predictable. These variations are apparent in trends of indices such as Engineering News Record (ENR) Construction Cost Index (CCI) and National Highway Construction Cost Index (NHCCI). These variations are problematic for cost estimation, bid preparation and investment planning. Inaccurate cost estimation can result in bid loss or profit loss for contractors and hidden price contingencies, delayed or cancelled projects, inconsistency in budgets and unsteady flow of projects for owner organizations. Cost variation has become a major concern in all industry sectors, such as infrastructure, heavy industrial, light industrial, and building. The major problem is that construction cost is subject to significant variations that are difficult to forecast. The objectives of this dissertation are to identify the leading indicators of CCI and NHCCI from existing macroeconomic, energy and construction market variables and create appropriate models to use the information in past values of CCI and NHCCI and their leading indicators in order to forecast CCI and NHCCI more accurately than existing CCI and NHCCI forecasting models. A statistical approach based on multivariate time series analysis is used as the main research approach. The first step is to identify leading indicators of construction cost variations. A pool of 16 candidate (potential) leading indicators is initially selected based on a comprehensive literature review about construction cost variations. Then, the leading indicators of CCI are identified from the pool of candidate leading indicators using empirical tests including correlation tests, unit root tests, and Granger causality tests. The identified leading indicators represent the macroeconomic and construction market context in which the construction cost is changing. Based on the results of statistical tests, several multivariate time series models are created and compared with existing models for forecasting CCI. These models take advantage of contextual information about macroeconomic condition, energy price and construction market for forecasting CCI accurately. These multivariate time series models are rigorously diagnosed using statistical tests including Breusch-Godfrey serial correlation Lagrange multiplier tests and Autoregressive conditional heteroskedasticity (ARCH) tests. They are also compared with each other and other existing models. Comparison is based on two typical error measures: out-of-sample mean absolute prediction error and out-of-sample mean squared error. Based on the unit root tests and Granger causality tests, consumer price index, crude oil price, producer price index, housing starts and building permits are selected as leading indicators of CCI. In other words, past values of these variables contain information that is useful for forecasting CCI. Based on the results of cointegration tests, Vector Error Correction (VEC) models are created as proper multivariate time series models to forecast CCI. Our results show that the multivariate time series model including CCI and crude oil price pass diagnostic tests successfully. It is also more accurate than existing models for forecasting CCI in terms of out-of-sample mean absolute prediction error and out-of-sample mean square error. The predictability of the multivariate time series modeling for forecasting CCI is also evaluated using stochastically simulated data (Simulated CCI and crude oil price). First, 50 paths of crude oil price are created using Geometric Brownian Motion (GBM). Then, 50 paths of CCI are created using Gaussian Process that is considering the relationship between CCI and crude oil price over time. Finally, 50 multivariate and univariate time series models are created using the simulated data and the predictability of univariate and multivariate time series models are compared. The results show that the multivariate modeling is more accurate than univariate modeling for forecasting simulated CCI. The sensitivity of the models to inputs is also examined by adding errors to the simulated data and conducting sensitivity analysis. The proposed approach is also implemented for identifying the leading indicators of NHCCI from the pool of candidate leading indicators and creating appropriate multivariate forecasting models that use the information in past values of NHCCI and its leading indicators. Based on the unit root tests and Granger causality tests, crude oil price and average hourly earnings in the construction industry are selected as leading indicators of NHCCI. In other words, past values of these variables contain information that is useful for forecasting NHCCI. Based on the results of cointegration tests, Vector Error Correction (VEC) models are created as the proper multivariate time series models to forecast NHCCI. The results show that the VEC model including NHCCI and crude oil price, and the VEC model including NHCCI, crude oil price, and average hourly earnings pass diagnostic tests. These VEC models are also more accurate than the univariate models for forecasting NHCCI in terms of out-of-sample prediction error and out-of-sample mean square error. The findings of this dissertation contribute to the body of knowledge in construction cost forecasting by rigorous identification of the leading indicators of construction cost variations and creation of multivariate time series models that are more accurate than the existing models for forecasting construction cost variations. It is expected that proposed forecasting models enhance the theory and practice of construction cost forecasting and help cost engineers and capital planners prepare more accurate bids, cost estimates and budgets for capital projects.

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