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S A (2015) Social and engineering aspects of construction site management using simulation and social network analysis, Unpublished PhD Thesis, , North Carolina State University.
Abudayyeh, O Y (1991) An automated data acquisition and data storage model for improving cost and schedule control, Unpublished PhD Thesis, , North Carolina State University.
Al-Ibrahim, A (2006) Optimizing roof maintenance and replacement decisions, Unpublished PhD Thesis, , North Carolina State University.
Alsharef, A F A (2022) Leveraging data analytics to improve construction operations and occupational safety, Unpublished PhD Thesis, , North Carolina State University.
Arocho Rosa, I d M (2015) The impact of transportation construction projects and activities on emissions, Unpublished PhD Thesis, , North Carolina State University.
Attia, T M (2002) The impact of communication technologies on group problem-solving performance in construction, Unpublished PhD Thesis, , North Carolina State University.
Bai, Y (1996) Planning and control model for robotic bridge painting, Unpublished PhD Thesis, , North Carolina State University.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knowledge repository with intelligent knowledge transference to enhance construction project outcomes, Unpublished PhD Thesis, , North Carolina State University.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the predictability of construction project outcomes through project level indirect construction cost practices, Unpublished PhD Thesis, , North Carolina State University.
Chmielewski, H T (2023) Overcoming modeling barriers in long-term interdependent infrastructure systems planning, Unpublished PhD Thesis, , North Carolina State University.
Choi, B (2003) Topics in risk-based design and performance evaluation of structures, Unpublished PhD Thesis, , North Carolina State University.
Dorr, E E (1979) Economies of scale in high school construction and operation, Unpublished PhD Thesis, , North Carolina State University.
Hollar, D A (2011) Predicting preliminary engineering costs for highway projects, Unpublished PhD Thesis, , North Carolina State University.
Isied, M M (2023) Critical assessment of asphalt mixture design procedures and asphalt mixture classification systems, Unpublished PhD Thesis, , North Carolina State University.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.
- Type: Thesis
- Keywords: construction activities; workforce; construction cost; construction labor; interest rate; pipeline; unemployment; wages; forecasting; recruitment; training; United States; cost information; productivity; time series; owner; professional; stakeholder
- ISBN/ISSN:
- URL: https://www.proquest.com/docview/1513569810
- Abstract:
Ensuring an adequate supply of skilled laborers to meet demand and avoid potential shortages or surpluses has been an issue of concern to construction industry professionals and researchers for quite some time. Poor industry image, declining wages, lack of training opportunities, and training time lag between new, unskilled laborers becoming skilled are a few of the many factors identified by researchers and professionals over the past 20 years as having contributed to skilled labor mismatch. Although construction unemployment reached record high levels in February of 2007 (27.1%) due to poor economic conditions in the United States, as we continue through the 2010's and into the 2020's, maintaining an adequate and competitive workforce that is able to meet the future skilled labor demands is an issue of importance to the construction industry. Forecasts of skilled construction labor demand provide valuable information that can ensure that construction industry participants and stakeholders are aware of future labor force needs and are prepared to recruit, train, and retain an adequate pipeline of skilled laborers. Forecasts of skilled labor demand for the construction industry are important to ensuring a sustainable skilled labor workforce. Therefore, the main objective of this work was to make accurate and useful forecasts of future skilled construction labor demand. Crucial to developing reliable forecasts is the collection of accurate and consistent data with which models can be developed and on which to base projections. Data were collected in this effort for five key independent variables (interest rate, material price, construction output, productivity, and real wage) and the dependent variable (labor demand) from a variety of existing data sources. The availability and quality of economic and construction industry data intended for use in the skilled labor demand forecast model is assessed and evaluated. Productivity data, in particular, has historically been difficult to collect. For this effort, a new productivity metric was developed using labor and cost information from a sample of typical construction activities in the RS Means Building Construction Cost Data manual. The newly developed metric was used as input to the developed forecast model. The model developed in this research used vector autoregression (VAR). VAR modeling was selected because of its ability to analyze multivariate time series data. The forecast model was successfully validated against two years of actual data. Potential data trends for each independent model variable were developed. Various combinations of the potential trends were used in the model to formulate and compare different forecast scenarios through 2023. The most likely scenario results in a forecasted need of approximately 5.3–6.3 million skilled laborers needed in the construction industry by 2023. Recommendations are given as to how the availability and quality of construction industry labor data can be improved. One key recommendation is that more extensive data collection can be undertaken to produce accurate and consistent data. Doing so will provide support for more accurate forecasts for planning, recruitment, and retention efforts. Also, the industry should use and strive to improve the newly developed metric for construction industry labor productivity, since this allows construction professionals to be able to analyze industry level productivity by means of a commonly used industry reference manual. Overall, the research findings present a reliable forecast model that produces short to medium term forecasts of skilled labor demand with reasonable accuracy. Construction industry participants and stakeholders, including practitioners, owners, researchers, training providers, government agencies, and employment policy makers, can use the resulting data, model, and forecast scenarios to be proactive in their planning and policy making as it relates to ensuring an adequate skilled construction labor force in the future.
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.