Abstracts – Browse Results
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Abbasian Hosseini, S A (2015) Social and engineering aspects of construction site management using simulation and social network analysis, Unpublished PhD Thesis, , North Carolina State University.
Abudayyeh, O Y (1991) An automated data acquisition and data storage model for improving cost and schedule control, Unpublished PhD Thesis, , North Carolina State University.
Al-Ibrahim, A (2006) Optimizing roof maintenance and replacement decisions, Unpublished PhD Thesis, , North Carolina State University.
Alsharef, A F A (2022) Leveraging data analytics to improve construction operations and occupational safety, Unpublished PhD Thesis, , North Carolina State University.
Arocho Rosa, I d M (2015) The impact of transportation construction projects and activities on emissions, Unpublished PhD Thesis, , North Carolina State University.
Attia, T M (2002) The impact of communication technologies on group problem-solving performance in construction, Unpublished PhD Thesis, , North Carolina State University.
Bai, Y (1996) Planning and control model for robotic bridge painting, Unpublished PhD Thesis, , North Carolina State University.
Banerjee, S (2022) Developing an organization-wide knowledge repository with intelligent knowledge transference to enhance construction project outcomes, Unpublished PhD Thesis, , North Carolina State University.
Becker, T C (2012) Improving the predictability of construction project outcomes through project level indirect construction cost practices, Unpublished PhD Thesis, , North Carolina State University.
Chmielewski, H T (2023) Overcoming modeling barriers in long-term interdependent infrastructure systems planning, Unpublished PhD Thesis, , North Carolina State University.
Choi, B (2003) Topics in risk-based design and performance evaluation of structures, Unpublished PhD Thesis, , North Carolina State University.
Dorr, E E (1979) Economies of scale in high school construction and operation, Unpublished PhD Thesis, , North Carolina State University.
Hollar, D A (2011) Predicting preliminary engineering costs for highway projects, Unpublished PhD Thesis, , North Carolina State University.
- Type: Thesis
- Keywords: construction cost; construction phase; environmental impact; funding; highway; workforce; cost estimation; forecasting; productivity; estimator; validation
- ISBN/ISSN:
- URL: https://www.proquest.com/docview/897919512
- Abstract:
Preliminary engineering (PE) for a highway project encompasses two efforts: planning to minimize the physical, social, and human environmental impacts of projects and engineering design to deliver the best alternative. State transportation agencies strive to manage these efforts efficiently, seeking to maximize the utilization of limited funding and workforce productivity. Managers need a feasible PE budget early in project development. The results reported herein will enable engineers and managers to develop PE budgets during the preconstruction phase of highway project development. Typically, transportation managers establish a project's PE budget using a percentage of estimated project construction costs disregarding other project-specific parameters. The commonly accepted rule of thumb for PE costs is 10% of estimated construction costs. This research effort sought to improved PE cost estimation methods by investigating available historical data supporting statistical analyses, developing predictive regression models forecasting projects' PE costs, and assessing model performance through validation. Cost data were acquired for 461 bridge projects and 188 roadway projects let for construction between 1999 and 2009 in North Carolina. Separate analyses were performed on bridges and roadways. Both project types are included in North Carolina's State Transportation Improvement Program. Many variables applicable for bridges were not applicable or available for roadways. Analysis of the roadway data yielded a mean ratio of PE cost to estimated construction cost (the PE cost ratio) of 11.7%. Comparatively, the bridge projects exhibited a mean PE cost ratio of 27.8%, approximately 2.4 times greater than the roadway mean. Using multiple linear regression and hierarchical linear modeling, we developed prediction models to forecast the PE cost ratio of future bridge and roadway projects. Before model development began, we randomly selected a portion of the data for validation. Regression modeling used the remainder of the data. Validation of the bridge model (using 70 projects) resulted in a mean absolute percentage error (MAPE) of 43%. The bridge model utilized eight variables, four numerical and four categorical, with interactions. When validating on 38 roadway projects, the roadway model yielded an average absolute error (AAE) of ± 6.91%. Considering the roadway database exhibited a mean PE cost ratio of 11.7%, this AAE represents a 59% relative error. Comparatively, predictive regression modeling did yield better PE cost estimates than using the commonly accepted rule of thumb, or any single parameter estimator. However, the relative error was high, in the 40% to 60% range. Example calculations using the developed models (bridge and roadway) are presented. Improvements to agency preconstruction cost accounting and tracking processes would support better modeling efforts. Lack of consistent historical data influenced our model development. Others pursing PE cost analyses should thoroughly evaluate and assess data quality before initiating similar modeling efforts.
Isied, M M (2023) Critical assessment of asphalt mixture design procedures and asphalt mixture classification systems, Unpublished PhD Thesis, , North Carolina State University.
Javanmardi, A (2019) Strategies and predictive models for reducing workflow variability in construction production systems, Unpublished PhD Thesis, , North Carolina State University.
Kranz, C N (2021) Optimizing compost incorporation for stormwater infiltration, runoff quality, and vegetation establishment in post-construction soils, Unpublished PhD Thesis, , North Carolina State University.
Lee, D (2023) Development of a real-time automated mobile robotic welding system in construction, Unpublished PhD Thesis, , North Carolina State University.
Lee, J (2005) Value analysis of Wi-Fi agent functions in construction, Unpublished PhD Thesis, , North Carolina State University.
Namian, M (2017) Factors affecting construction hazard recognition and safety risk perception, Unpublished PhD Thesis, , North Carolina State University.
Noghabaei, M (2021) Visual and behavioral data analysis in immersive virtual environments for enhancing construction safety, planning, and control, Unpublished PhD Thesis, , North Carolina State University.
Nuntasunti, S (2004) The effects of visual-based information logistics in construction, Unpublished PhD Thesis, , North Carolina State University.
Orgut, R E (2017) Metrics that matter: Improving project controls and analytics in construction industry, Unpublished PhD Thesis, , North Carolina State University.
Piper, B E B (2014) Optimization methods for improving the resilience of civil infrastructure systems subject to natural hazards, Unpublished PhD Thesis, , North Carolina State University.
Rihani, R A (2006) An investigation of critical success factors for robotic masonry, Unpublished PhD Thesis, , North Carolina State University.
Russell, M M (2013) Allocation of time buffer to construction project task durations, Unpublished PhD Thesis, , North Carolina State University.
Vereen, S C (2013) Forecasting skilled labor demand in the US construction industry, Unpublished PhD Thesis, , North Carolina State University.
Wambeke, B W (2011) Identifying, prioritizing, and reducing variation of construction related tasks, Unpublished PhD Thesis, , North Carolina State University.
Zuluaga Santa, C M (2018) Protecting bridge maintenance workers: Evaluating fall protection supplementary devices using virtual prototyping and wearable technology, Unpublished PhD Thesis, , North Carolina State University.