Abstracts:As facility owners and operators, public sector organizations (such as government agencies) that provide public facilities and services not only maintain the existing capital assets but also plan for future developments on an ongoing basis through a capital improvement program (CIP). The CIP identifies capital projects and is typically funded through several distinct funding sources with specific usage, timing, and reporting restrictions. The continuous nature of capital improvement planning compounded by the potential for scope or funding availability changes further makes it a challenge to retain the flexibility to add new and different funding sources to an existing funding allocation plan. Existing literature about this problem is sparse, and a systemic solution is yet to be proposed that enables an owner organization to meet its goals while maintaining fiscal responsibility toward its funding agencies. The financial digital twin (FinDT) framework developed in this study integrates fund allocation and accounting with project acquisition in real time and enables the owner to automate funding allocation tasks and to synchronize change management while ensuring compliance with funding restrictions. The goal of this framework is to leverage rule-based knowledge management to consolidate and continually optimize the efficient use of accurate financial information at all stages of the capital asset lifecycle. This is accomplished through automation and digital twin (DT) integration. A use case was developed to demonstrate and evaluate the FinDT, which was found to hold potential for more efficient information management and support for decision making through enterprise-level data integration. The framework developed in this study can be used to display other nongeometric attribute information in a building information model for the purposes of visualization and interactive, efficient decision making.

Abstracts:Accurate and timely assessment of pavement condition is critical to determine optimal maintenance plans. Due to the high costs of ground-based inspections, agencies often limit their monitoring to major roads and the condition of some elements of the road network remains unknown. Satellites, capable of rapidly collecting information over wide areas, can be a cost-effective alternative to monitor pavement condition. This wide coverage, however, comes at the expense of lower levels of accuracy. The objective of this study is to quantify the value of satellite-based information in optimal inspection and maintenance strategies. To account for the uncertainties associated with satellite observations, the system was modeled as a partially observable Markov decision process (POMDP) to determine optimal life-cycle inspection and maintenance policies. To estimate the value of information obtained from satellite inspections, two cases representing current pavement condition practices were simulated: (1) as an alternative to inspect highways, roads that are traditionally monitored with annual automated distress surveys, and (2) as an option to inspect local or ancillary roads, which are not typically monitored. Results indicate that satellite observations result in up to 6.5% reduction in cost if they are used to make maintenance and inspection decisions over the pavement life cycle. Savings are higher for nonmonitored roads compared to major roads that are annually inspected with automated distress surveys. Satellite information was found to become valuable at 70% level of accuracy when used in combination with more accurate systems.