Our built environment is replete with engineered structural systems (e.g., civil infrastructure, automobiles, marine and aerospace structures, among others), and the socioeconomic well-being of our society is strongly dependent on their safe and reliable operations. However, structural safety degrades with age, and they can sustain damage due to various natural hazards and extreme events (e.g., earthquakes, hurricanes, and landslides, to name a few). In addition, because of increasingly closer human and engineered structure interactions, human error and/or fatigue can also threaten the safe operations of various structural systems. Therefore, structural health monitoring (SHM) techniques have attracted extensive attention, mainly for their potential of timely detecting structural damage while minimizing service downtime and economic loss. A similar monitoring paradigm can also be employed to assess human operators’ performance for avoiding human-induced structural failures and accidents.

The SHM paradigm typically relies on sensing systems to provide rich datasets regarding structural performance. While various off-the-shelf sensing transducers have been employed, the complex damage modes, drastically different materials, intricate geometries, and diverse operational environments make it challenging for conventional sensors to effectively quantify structural health and human performance. Multifunctional materials, on the other hand, can be designed using a bottom-up approach for realizing novel sensing mechanisms that could be better suited for extracting relevant damage features while tailoring their designs for specific engineering applications.

The primary objective of this dissertation was to develop, characterize, and implement multifunctional material-based sensing systems for both structural health and human performance monitoring. This work aimed to leverage the extraordinary mechanical and electrical properties of nanostructured materials (including carbon nanotubes and graphene) to achieve robust and high-performance sensing systems. Although previous endeavors have been dedicated to developing nanomaterial-based sensing systems, their practical applications could be hindered by complex material fabrication processes and poor scalability. Therefore, this study employed simple, scalable, and low-cost manufacturing techniques to fabricate multifunctional nanocomposites of optimized mechanical properties and strain sensing characteristics. In addition, a topological design methodology was proposed to strategically engineer the strain sensing properties of nanocomposite thin films for different target applications.

To overcome the discrete sensing limitations of current transducers, this work coupled the nanocomposite sensors with electrical impedance tomography and compressed sensing algorithms for achieving spatial sensing capability. Extensive laboratory tests were performed to characterize their spatial sensing performance. In the last phase of this work, the spatial sensing systems were implemented to monitoring seismic loading-induced structural damage on a full-scale reinforced concrete shear wall. At the same time, fabric-based sensors were fabricated and integrated with socket prosthesis surrogates to demonstrate their applicability for human monitoring applications (e.g., assistive rehabilitation and pressure ulcers prevention). Overall, this dissertation advanced multifunctional material-based sensing systems for monitoring engineered structures and human health.

This dissertation presents a search for the Standard Model production of four top quarks (pp$\rightarrow$\tttt) using events with opposite-sign dilepton ($\mu^+\mu^-$, $\mu^\pm e^\mp$, or $e^+e^-$)+jets signatures. The data used in this analysis are from proton-proton collisions at a center-of-mass energy $\sqrt{s}=13$ TeV recorded by the CMS detector at LHC during year 2016. The total integrated luminosity of the data corresponds to 35.8 $\mathrm{fb}^{-1}$. A multivariate analysis method is used to discriminate \tttt signal events from background events based on global event and jet properties. No significant deviation is observed in data from the prediction of the background. An upper limit is set on the cross section of Standard Model \tttt production to be 64 fb at 95\% confidence level. A combination of the measurements with results from single-lepton ($\mu$, $e$)+jets final states and a previous measurement from the CMS experiment is performed and the combined cross section measurement is $13^{+11}_{-9}$ fb, with an observed signal significance of 1.4 standard deviations.