While serviceability, safety, and sustainability of deteriorating infrastructure have received significant attention in civil engineering, accessible approaches are needed to obtain actionable information about a structure over time. In particular, any intelligent infrastructure system's performance is governed by the 1) cost of sensing system required to measure structural states; 2) algorithms used to extract intelligence amongst the enormous quantities of multidimensional data; and 3) different approaches used to link intelligence to decisions. This work presents a theoretical framework for designing integrated SHM systems leveraging smart sensor and material technologies.

In this dissertation, two types of sensing techniques for intelligent infrastructure are explored. The first is a bio-inspired sensing skin, termed soft elastomeric capacitor (SEC), that measures surface strain through a chance in capacitance. The SEC is a highly scalable technology and is a low-cost solution for monitoring local states over a global surface. Here, the SEC is used to detect and monitor cracks in a full-scale post-tension concrete component. The second is multifunctional self-sensing Carbon Fiber-Reinforced Polymer (CFRP), capable of detecting changes in its physical state (e.g., strain or damage). The prototyped CFRP capacitor is experimentally characterized through static and dynamic tests.

In order to effectively utilize these advanced sensing techniques, methods for sensor network design and implementation are investigated. Of interest are computationally inexpensive real-time adaptive representations using available measurements. This is done through the meta-modeling of the monitored structural components and integrating real-time online parameter estimation algorithms. To explore real-time applicability, the proposed method is applied to the high-rate state estimation problem.