Innovative modeling and management of infrastructure systems, engineering and construction operations, and offsite construction technology using computational data analytics

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.