Abstracts – Browse Results
Click on the titles below to expand the information about each abstract.
Viewing 7 results ...
Abdul Nabi, M (2023) Promoting enhanced decision-making and proactive management for modularization in the construction industry using risk-based approach, analytical hierarchy process, and graph theory, Unpublished PhD Thesis, , Missouri University of Science and Technology.
Algraiw, I H A (2015) The engagement of expert opinions in the modeling of multi-attribute decision making for the selection of project delivery methods in building construction, Unpublished PhD Thesis, , Missouri University of Science and Technology.
Elsayegh, A (2021) Collaborative planning in the construction industry: A holistic framework for assessing collaborative planning practices and predicting project performance, Unpublished PhD Thesis, , Missouri University of Science and Technology.
Gillis, W L, III (2013) Development of an integrative commissioning methodology for new-building construction, Unpublished PhD Thesis, , Missouri University of Science and Technology.
Hassane Assaad, R (2021) Innovative modeling and management of infrastructure systems, engineering and construction operations, and offsite construction technology using computational data analytics, Unpublished PhD Thesis, , Missouri University of Science and Technology.
- Type: Thesis
- Keywords: artificial intelligence; asset management; construction engineering; construction project; construction technology; improvement; learning; offsite; offsite construction; workforce; productivity; safety; continuous improvement; machine learning; data minin
- ISBN/ISSN:
- URL: https://www.proquest.com/docview/2580910493
- Abstract:
The construction industry has been facing considerable challenges due to the inadequacy of the traditional methods in executing, managing, and modeling infrastructure and construction projects. While many techniques have been developed to improve the decision-making process in the industry, there is no evidence of sufficient and continuous improvements in the industry's adoption and implementation of innovative techniques such as new management approaches, modern modeling methods, and emerging computational data analytics. To this end, the goal of this research is to address some of the recent challenges faced in the industry with a focus on infrastructure asset management, construction engineering and management operations, and offsite construction technology. The research goals and objectives were achieved through multiple management, modeling, and computational analytical methods; including artificial intelligence and supervised machine learning algorithms, mathematical and risk modeling, statistical and multivariate time series analysis, clustering techniques and unsupervised data mining algorithms, and surveys and industry panel meetings. The research has numerous intellectual merits, methodological contributions, and practical implications as it addresses critical research areas that have not been investigated before and strengthens areas which needed in-depth examination and further advancements. The findings, outcomes, and conclusions of this research will contribute in further improving the cost, time, productivity, and safety considerations in the industry; leveraging innovative management, modeling, and computational analytics in infrastructure and construction projects; devising data-driven decision-making processes; and administrating and preparing the workforce of the future.
Loduca, D P (2011) Exploratory study of barriers to use of Feigenbaum's quality cost strategy within design engineering firms, Unpublished PhD Thesis, , Missouri University of Science and Technology.
Szydlik, C C (2014) Identifying and overcoming the barriers to sustainable construction, Unpublished PhD Thesis, , Missouri University of Science and Technology.