Doctor of Philosophy with a Major in Building Construction

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Doctor of Philosophy with a Major in Building Construction

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https://hdl.handle.net/1853/72547

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Degree Series

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School of Building Construction

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Now showing 1 - 10 of 23

A framework for developing machining learning models for facility life-cycle cost analysis through BIM and IoT

(Georgia Institute of Technology, 2019-04-02) Gao, Xinghua

This thesis presents a research project that developed a machine learning-enabled facility life-cycle cost analysis (LCCA) framework using data provided by Building Information Models (BIM) and the Internet of Things (IoT). First, a literature review and a questionnaire survey were conducted to determine the independent variables affecting the facility life-cycle cost (LCC). The potential data sources were summarized, and a data integration process introduced. Then, the framework for developing machine learning models for facility LCCA was proposed. A domain ontology for machine learning-enabled LCCA (LCCA-Onto) was developed to encapsulate knowledge about LCC components and their roles in relation to sibling ontologies that conceptualize the LCCA process. A series of experiments were conducted on a university campus to demonstrate the application of the proposed machine learning-enabled LCCA framework. Finally, the author’s vision of the future smart built environment was discussed.
Modeling and predicting the variation of US highway construction cost

(Georgia Institute of Technology, 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.
Sustainable energy technology, adoption, rebound, and resilience

(Georgia Institute of Technology, 2019-01-22) Hashemi Toroghi, Shahaboddin

While in the United States, centralized generation and distribution network are the basis of the current electric infrastructure, the recent surge in uptake of solar photovoltaic (PV) systems introduces a new avenue to decentralize this system. Furthermore, PV systems can substitute the grid electricity and increase the share of renewable energy sources. While by 2018, five states in the U.S. (California, Hawaii, Nevada Massachusetts, and Vermont) could reach 10% threshold for the share of solar sources in generating electricity, at the country level this share is still less than 3%; whereas in some other countries, such as Germany and Japan, it has already reached more than 6%. This dissertation examines the diffusion of PV systems from three perspectives, addressing three gaps in knowledge: an empirical study of the diffusion of PV systems in Georgia, a method to estimate renewable rebound effect, and a framework to quantify the resilience capacity of an electric infrastructure system with emergency electricity generators, including PV systems. Three studies present the primary contributions of this research. Study 1 examines the diffusion of PV systems in Georgia, identifies characteristics of adopters and patterns of adoption, and forecasts the future adoption of PV systems. Study 2 introduces a new approach to estimate the direct rebound effect, subsequent of a major adoption of PV systems. Study 3 presents a state-of-the-art framework that quantifies the resilience capacity of an electric infrastructure system with emergency electricity generators. The findings of the study 1 provides a benchmark for the future adoption of PV systems and highlights the impact of socio-economic and location-based factors in the diffusion of PV systems in Georgia. These findings can be used to shape a more effective policy, aiming to increase the share of PV systems, or evaluate the effectiveness of a policy. The finding of the study 2 opens a new avenue to compute the rebound effect and can support development of a policy to mitigate the renewable rebound effect in a targeted region. The finding of the study 3 can help system designers to customize the design of a resilient system based on its characteristics. The introduced framework can further be used to investigate improvement of the resilience capacity in an electric infrastructure system by increasing the penetration of PV systems, or other decentralized electricity generators in a region.
Embodied life cycle assessment and potential environmental impacts of improvement options for detached single-family houses in Atlanta

(Georgia Institute of Technology, 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.
Quantitative analysis for modeling uncertainty in construction costs of transportation projects with external factors

(Georgia Institute of Technology, 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.
Investigation of hybrid ventilation potential of commercial buildings in US

(Georgia Institute of Technology, 2018-07-30) Chen, Jianli

As one of the largest energy consumers in our society, commercial buildings take up approximately 20% of total energy consumption based on the data from Department of Energy (DOE). Among this energy consumption, nearly half of it is consumed by air conditioning systems for maintaining a comfortable thermal environment for building occupants. Despite this high energy consumption, complains of thermal comfort and health problems still commonly exist in air-conditioned buildings. The mean building satisfaction rate was only reported as 59% based on a large survey of building occupants, which is far below the minimum thermal comfort requirement in ASHRAE standard 55. Meanwhile, there also exist health problems in air-conditioned buildings, which include both building related diseases (typically caused by specific exposure to infectious indoor source) and sick building syndrome, which describes a group of general symptoms including eye or throat irritation, shortness of breath, visual disturbance etc. Thus, in these years, coupling natural ventilation with mechanical ventilation, hybrid ventilated buildings have attracted more attention from both academia and industry with increasing awareness of building sustainability. Hybrid ventilated buildings have the potential to minimize the energy bills for owners without compromising the thermal comfort of building occupants. Compared to the mechanical ventilated building, the hybrid ventilation system allows opening the window when the outdoor environment is favorable, which provides occupants with amenity to nature and saves energy in the building operation. Compared to the natural ventilation building, the hybrid ventilation building could protect the building occupants from the unfavorable outdoor environment with air conditioners on. As the first step to further popularize the hybrid ventilation building, this dissertation will provide a thorough investigation of the hybrid ventilation potential across different US climates with accounting for comprehensive and influential aspects related to the usage of natural ventilation, including different levels of uncertainties a hybrid ventilation building could experience, the influence of building intelligence and the impact of outdoor air quality. How to better assess the thermal comfort risks and utilize simulation to design this type of building will also be presented.
User-perceived effectiveness of unmanned aircraft system (UAS) integration in infrastructure construction environments

(Georgia Institute of Technology, 2018-04-10) Kim, Sungjin

A multi-layered performance analysis (MPA) method was proposed. Information analysis, technology performance, and human performance addressed based on the users' experience and perception measurement method in this study. Field-testing and participatory user field experiment were also conducted. Results can provide a better understanding of UAS integration and information needs to use the UAS in the construction domain. The findings during field-testing and group interviews can identify important factors and demonstrate the effectiveness of UAS integration based on the identified factors. The main challenge of this study is the small number of the data sample. However, industry representatives who have significant work experience participated, and the result of this study based on their experience and perception could have significant effects on the UAS integration in the construction environment. The MPA method contributes to transforming the research paradigm from the technology-centric method to the human-technology combined approach that considers human performance. The main findings can also function as the foundation to develop practical user guidelines and policy for the construction and infrastructure industry.
If these walls could talk: Automated performance measurement for building modeling decisions using data analytics

(Georgia Institute of Technology, 2018-01-10) Yarmohammadi, Saman

Building information modeling (BIM) is instrumental in documenting design, enhancing customer experience, and improving product functionality in capital projects. However, high-quality building models do not happen by accident, but rather because of a managed process that involves several participants from different disciplines and backgrounds. Throughout this process, the different priorities of design modelers often result in conflicts that can negatively impact project outcomes. There is a need for effective management of the modeling process to prevent such unwanted outcomes. Effective management of this process requires an ability to closely monitor the modeling process and correctly measure the modelers' performance. Nevertheless, existing methods of performance monitoring in building design practices lack an objective measurement system to quantify modeling progress. The widespread utilization of BIM tools presents a unique opportunity to retrieve granular design process data and conduct accurate performance measurements. This research improves upon previous efforts by presenting a novel application programming interface (API)-enabled approach to automatically collect detailed design development data directly from BIM software packages and efficiently calculate several modeling performance measures. The primary objective of this research is to create and examine the feasibility of a proposed automated design performance monitoring framework. The proposed framework provides the following capabilities: (a) non-intrusive and cost-effective data acquisition for capturing design development events in real time, (b) scalable and high-speed ingestion for the storage of design modeling data, (c) objective measurement of designer performance and estimating levels of effort required to complete design tasks, and (d) identifying optimal design teams using empirical performance information. In chapter 3, the utilization of modeling development information embedded in design log files that are produced by Autodesk Revit is proposed as a rich source of performance data. To this end, generalized suffix tree (GST) data structures are utilized to find common, frequent command sequences among Revit users. In addition to identifying the common command execution patterns, the average time it takes the selected modelers to execute command sequences is calculated. The obtained results demonstrate that there is a statistically significant difference between the modelers in terms of the time it takes them to conduct similar modeling tasks. Chapter 4 utilizes modeling software solution’s APIs to automatically collect and store timestamped design development information. The proposed passive data recording approach allows for the real-time capture of comprehensive user interface (UI) interaction and model element modification events. The proposed framework is also implemented as an Autodesk Revit plugin. An experiment is then conducted to verify the accuracy of this plugin. Throughout this experiment, manual recordings of model development events were compared against the automatically generated plugin output. Chapter 5 outlines the details of an approach to identify the optimal design modeling team configuration based on automatically collected performance data. To this end, an experiment is conducted to capture data using the developed Revit plugin. Experiment participants’ individual production rates are estimated to establish the validity of the proposed approach to identify the optimal design team configurations. The presented approach uses the earliest due date (EDD) sequencing rule in combination with the critical path method (CPM) to calculate the maximum lateness for different design team arrangements. The primary contributions of this study to the state of knowledge are as follows: (a) proposing a tailored string mining algorithm that is capable of extracting meaningful information from timestamped design development data, (b) developing a framework based on APIs to automatically collect design modeling data, and (c) creating a mathematical model to estimate design modeling project completion times based on individual performance data and project requirements. This study contributes to the state of practice by (a) allowing design project managers to gain an unprecedented insight into the evolution of a building model using the information embedded in design log files, (b) helping design managers to acquire progress information without the need to manually record and report data, and (c) enabling design managers to identify an optimal modeling team arrangement based on automatically captured, quantitative performance information.
Analyzing uncertainty in the price of materials and financial risk management strategies

(Georgia Institute of Technology, 2017-05-11) Ilbeigi, Mohammad

Significant volatility and unprecedented uncertainty in the price of asphalt cement is a serious challenge for both contractors and state DOTs with regards to proper cost estimating and budgeting of transportation projects. Previous studies indicate that owner organizations often overpay for projects under fixed-price contracts that transfer the material price risk to contractors due to increased risk premiums and hidden contingencies in contractors’ submitted bid prices. A common method widely used by state DOTs for handling the issue of extra risk premiums in submitted bid prices and avoiding overpayment to contractors is to offer price adjustment clauses (PACs) in contracts. A PAC is a risk sharing contractual mechanism that guarantees an adjustment in payment to contractors based on the size and direction of the material price change. Although uncertainty in the price of asphalt cement is a serious challenge for both contractors and state DOTs and many transportation agencies utilize PACs to control consequences of material price volatility, there is little knowledge about analyzing uncertainties in the price of asphalt cement and actual performance of PACs. This dissertation aims to analyze uncertainty in the price of asphalt cement and examine performance of PACs in highway construction projects. After a comprehensive review of the existing body of knowledge about uncertainties in the price of critical materials in transportation projects and PACs, time series analysis is conducted and four univariate time series forecasting models are created to forecast future price of asphalt cement. The results of the time series forecasting show that all four time series models can predict the future values of asphalt cement price with proper accuracy but among the four models, the ARIMA and Holt Exponential Smoothing models are the most accurate prediction models with less than 2% error. Then, ARCH/GARCH time series analysis is conducted to quantify and forecast level of uncertainties in the price of asphalt cement. The results of this step can help transportation agencies systematically measure, analyze and forecast the uncertainties in the price of asphalt cement and implement their risk management strategies at the right time. In next step, impacts of offering PACs on submitted bid prices for major asphalt line items are analyzed using multivariate regression analysis. Finally, effects of offering PACs on dispersion of submitted bid prices and number of bidders are analyzed using system monitoring processes.
Stakeholder alignment strategies for highway infrastructure public-private partnerships

(Georgia Institute of Technology, 2017-03-31) Mostaan, Kia

The U.S. Department of Transportation (U.S. DOT) and state DOTs across the nation seek private investments to leverage their shrinking financial resources. Involvement of the private sector in financing and delivery of highway public-private partnerships (P3s) in the United States has experienced various limitations and challenges. The lack of standard approaches for P3 project delivery as well as public agencies’ varying levels of maturity in P3 implementation have negative impacts on successful project delivery. There is a need for research to determine the variability in public sector’s project delivery practice, due to its negative impacts that lead to market inefficiency and unpredictability. It is necessary to evaluate and analyze improvement strategies that can standardize P3 project delivery and enhance partnership alignment between the public and private sectors. The overarching objective of this study is to propose recommendations and enablers for improving alignment of public and private sectors in P3s. This study employs a three-phase combinatory research approach to achieve the research objectives. At first, a national survey of state DOTs is conducted to determine the degree of variability in public sector’s P3 practice. Following the public sector survey, twenty-five P3 experts are identified and selected from organizations that are active in the U.S. P3 market. A structured interview protocol is utilized to conduct interviews consistent with study questions and document the results. The third and final phase of the study methodology prior to concluding the analysis and providing recommendations is to conduct case studies of three mature P3 programs (Florida, Texas, and Virginia DOTs). The final phase of the research methodology aims to demonstrate best practices for P3 implementation and sustainment through case studies of agencies in the United States. While there is ample research on P3s in general, this study focuses on the alignment of public and private sectors in highway P3s. This study identifies the leading factors and issues that affect P3 decision-making by the public sector and the inconsistency in P3 implementation across project phases. This study also determines and evaluates the factors that can influence the public and private sector alignment in U.S. P3s and compares them with international best practices. Finally, by identifying recommended strategies and enabling mechanisms, this research aims to mitigate the lack of alignment between the public and private sectors in the U.S. P3 market. This study also demonstrates how mature P3 programs in the U.S. have achieved sustained partnerships. The final contribution of this study is a set of detailed recommendations for alignment of public and private sectors in U.S. P3s. The findings of this study are relevant for the U.S. P3 market, but may also be useful for planners and policy-makers in other countries. The major stakeholders impacted by this research involve public sector agencies, such as state DOTs, state and national infrastructure banks, metropolitan planning organizations (MPOs), permitting agencies and private sector stakeholders, such as multinational development companies, contractors, investments banks, procurement, financial and legal advisors.