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The C+ score for US bridges on the 2017 infrastructure report card underscores the need for improved data-driven methods to understand bridge performance. There is a lot of interest and prior work in using inspection records to determine bridge health scores. However, aggregating, cleaning, and analyzing bridge inspection records from all states and all past years is a challenging task, limiting the access and reproducibility of findings. This research introduces a new score computed using inspection records from the National Bridge Inventory (NBI) data set. Differences between the time series of condition ratings for a bridge and a time series of average national condition ratings by age are used to develop a health score for that bridge. This baseline difference score complements NBI condition ratings in further understanding a bridge’s performance over time. Moreover, the role of bridge attributes and environmental factors can be analyzed using the score. Such analysis shows that bridge material type has the highest association with the baseline difference score, followed by snowfall and maintenance. This research also makes a methodological contribution by outlining a data-driven approach to repeatable and scalable analysis of the NBI data set. 

US Bridges scored a C+ on the 2017 infrastructure report card. There is a need for substantial improvement in bridge conditions as many of them are structurally deficient and can become unsafe in the near future. The nation's most recent bridge rehabilitation estimate is $123 billion. Many state's department of transportation (DOT) have limited resources, leaving them with difficult decisions about where to invest and allocate limited resources. To make cost-effective decisions, these bridge stakeholders need clean data and studies to estimate the future bridge conditions. This will give them data-driven, accurate life-cycle models for bridges and improved inspections intervals. Previous researchers have identified factors that may cause bridge deterioration. Unfortunately, these researchers limit their data to specific regions and bridge types. This severely limits their result's general applicability. In this thesis, we approach bridge health-related decision making challenges using a novel data science perspective. This bridge health deterioration study provides new insights into making bridge rehabilitation and reconstruction decisions. In this research, we use all US inspection record data regulated by the Federal Highway Agency that is available in the National Bridge Inventory (NBI) database and precipitation data from the Center for Disease Control and Prevention (CDC). Our specific contributions are 1) providing a reference big data pipeline implementation for bridge health-related datasets; 2) demonstrating the feasibility of data science to study bridge deterioration; 3) developing repeatable methods for sharing large datasets with reproducible analysis driven by data science and making them available to other researchers. Further, our curated datasets and platforms are used to analyze the statistical significance of bridge deterioration factors as identified by the literature and subject matter experts at the Nebraska State DOT. From our results, we found that bridge material type has the highest association in comparison to other factors such as average daily traffic, average daily truck traffic, structure length, maintainer, region, and precipitation. This research used all NBI inspection records and precipitation rates from all US counties. 

Focusing particles into a tight stream is critical for many microfluidic particle-handling devices such as flow cytometers and particle sorters. This work presents a fundamental study of the passive focusing of polystyrene particles in ratchet microchannels via direct current dielectrophoresis (DC DEP). We demonstrate using both experiments and simulation that particles achieve better focusing in a symmetric ratchet microchannel than in an asymmetric one, regardless of the particle movement direction in the latter. The particle focusing ratio, which is defined as the microchannel width over the particle stream width, is found to increase with an increase in particle size or electric field in the symmetric ratchet microchannel. Moreover, it exhibits an almost linear correlation with the number of ratchets, which can be explained by a theoretical formula that is obtained from a scaling analysis. In addition, we have demonstrated a DC dielectrophoretic focusing of yeast cells in the symmetric ratchet microchannel with minimal impact on the cell viability. 

Abstract Insulator‐based dielectrophoresis (iDEP) exploits the electric field gradients formed around insulating structures to manipulate particles for diverse microfluidic applications. Compared to the traditional electrode‐based dielectrophoresis, iDEP microdevices have the advantages of easy fabrication, free of water electrolysis, and robust structure, etc. However, the presence of in‐channel insulators may cause thermal effects because of the locally amplified Joule heating of the fluid. The resulting electrothermal flow circulations are exploited in this work to trap and concentrate nanoscale particles (of 100 nm diameter and less) in a ratchet‐based iDEP microdevice. Such Joule heating‐enabled electrothermal enrichment of nanoparticles are found to grow with the increase of alternating current or direct current electric field. It also becomes more effective for larger particles and in a microchannel with symmetric ratchets. Moreover, a depth‐averaged numerical model is developed to understand and simulate the various parametric effects, which is found to predict the experimental observations with a good agreement.