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Research interest in predictive modeling within the structural engineering community has recently been focused on Bayesian inference methods, with particular emphasis on analytical and sampling approaches. In this study, we explore variational inference, a relatively unknown class of Bayesian inference approaches which has potential to realize the computational speed, accuracy, and scalability necessary for structural health monitoring applications. We apply this method to the predictive modeling of a simulated Bouc-Wen system subject to base vibration and compare the performance of this inference approach to that of the unscented Kalman filter. From this investigation, we find that though variational inference is more computationally intensive than the unscented Kalman filter, it exhibits superior performance and flexibility.

The process of acquiring large-scale complex systems is usually characterized by cost and schedule overruns. We develop and evaluate a model of the acquisition process that accounts for the strategic behavior of different parties. Specifically, we cast our model in terms of government-funded projects and assume the following steps. First, the government publishes a request for bids. Then, private firms offer their proposals in a bidding process and the winner bidder enters in a contract with the government. The contract describes the system requirements and the corresponding monetary transfers for meeting them. The winner firm devotes effort to deliver a system that fulfills the requirements. This can be assumed as a game that the government plays with the bidder firms. The objective of this paper is to study how different parameters in the acquisition procedure affect the bidders' behaviors and therefore, the utility of the government. Using reinforcement learning, we seek to learn the optimal policies of involved actors in this game. In particular, we study how the requirements, contract types such as cost-plus and incentive-based contracts, number of bidders, problem complexity, etc., affect the acquisition procedure. Furthermore, we study the bidding strategy of the private firms and how the contract types affect their strategic behavior. Also, we study the effects of different contract types on the winner's optimal effort level necessary to meet the system requirements. We run exhaustive numerical simulations, which show that cost-plus contracts are particularly prone to strategic misrepresentation. This analysis can be expanded to help the government select procedures that achieve specific goals, such us minimizing cost overruns.

Reliable platforms for data collation during airline schedule operations have significantly increased the quality and quantity of available information for effectively managing airline schedule disruptions. To that effect, this paper applies macroscopic and microscopic techniques by way of basic statistics and machine learning, respectively, to analyze historical scheduling and operations data from a major airline in the United States. Macroscopic results reveal that majority of irregular operations in airline schedule that occurred over a one-year period stemmed from disruptions due to flight delays, while microscopic results validate different modeling assumptions about key drivers for airline disruption management like turnaround as a Gaussian process.

Image data remains an important tool for post-event building assessment and documentation. After each natural hazard event, significant efforts are made by teams of engineers to visit the affected regions and collect useful image data. In general, a global positioning system (GPS) can provide useful spatial information for localizing image data. However, it is challenging to collect such information when images are captured in places where GPS signals are weak or interrupted, such as the indoor spaces of buildings. The inability to document the images’ locations hinders the analysis, organization, and documentation of these images as they lack sufficient spatial context. In this work, we develop a methodology to localize images and link them to locations on a structural drawing. A stream of images can readily be gathered along the path taken through a building using a compact camera. These images may be used to compute a relative location of each image in a 3D point cloud model, which is reconstructed using a visual odometry algorithm. The images may also be used to create local 3D textured models for building-components-of-interest using a structure-from-motion algorithm. A parallel set of images that are collected for building assessment is linked to the image stream using time information. By projecting the point cloud model to the structural drawing, the images can be overlaid onto the drawing, providing clear context information necessary to make use of those images. Additionally, components- or damage-of-interest captured in these images can be reconstructed in 3D, enabling detailed assessments having sufficient geospatial context. The technique is demonstrated by emulating post-event building assessment and data collection in a real building.

A systematic approach to mass-production of graphene and other 2D materials is essential for current and future technological applications. By combining a sequential statistical design of experiments with in-situ process monitoring, we demonstrate a method to optimize graphene growth on copper foil in a roll-to-roll rf plasma chemical vapor deposition system. Data-driven predictive models show that gas pressure, nitrogen, oxygen, and plasma power are the main process parameters affecting the quality of graphene. Furthermore, results from in-situ optical emission spectroscopy reveal a positive correlation of CH radical to high quality of graphene, whereas O and H atoms, Ar+ ion, and C2 and CN radicals negatively correlate to quality. This work demonstrates the deposition of graphene on copper foil at 1 m/min, a scale suitable for large-scale production. The techniques described here can be extended to other 2D materials and roll-to-roll manufacturing processes.