Browsing by Author "Chen, Jian"
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Item Cable-stayed bridge condition evaluations by data analysis methodologies based on structural health monitoring system(University of Delaware, 2023) Chen, JianFor transportation agencies or private bridge owners, damage detection is a key interest as they manage their structural assets. In the past, most damage was detected using periodic visual inspection, which is time-consuming, expensive, and subjective. The advent of Structural Health Monitoring (SHM) systems which can capture the real-time response of structures can help bridge owners evaluate their bridges for potential life-safety issues and help them mitigate economic loss. ☐ The goal of this study is to develop methodologies that can be used to evaluate bridge condition efficiently by monitoring bearing displacements, strains, and cable tensions. These explicit parameters can be easily understood and used by bridge owners. In so doing, the Indian River Inlet Bride (IRIB), and data collected from its SHM system, were used to evaluate the methodologies developed. Different characteristics of the IRIB were analyzed using machine learning algorithms and statistical modeling. Direct and indirect criteria that transportation agencies can easily interpret were generated and calculated. The effectiveness and sensitivity of the proposed methodologies were validated by damage simulation. The evaluations incorporated the monitoring of bearing displacements and strain peaks, as well as cable tension tracking under critical wind load conditions. The approached developed can be used to improve the structure inspection and evaluation process, thereby strengthening the owners structural management process. ☐ The dissertation research resulted in four papers written by the author. These papers will be presented in the distinct chapters of this dissertation. In Chapter 4, bearing displacements were predicted using ANN. The same algorithm is applied to deck strains in Chapter 6. Both of these chapters focus on real-time monitoring. In Chapter 5, statistical modeling was applied to strain data to evaluate bridge condition in long-term perspective, The final paper is pressented in Chapter 7 and it is focused on cable tension and natural frequencies monitoring. Various conclusions were made based on the research. The first conclusion made by analyzing bearing displacements is that they can be accurately predicted using distributed thermal loads on the bridge. There is a nonlinear relationship between the thermal loads and bearing displacements. The second conclusion is that the abnormal responses of the bridge can be detected by comparing Strain Threshold Indexes (STIs) with Decision Boundaries (DBs). Statistical analysis was applied to truck-induced strain peaks, and corresponding STIs and DBs were calculated. The truck-induced strain peaks were also analyzed by Artificial Neural Network (ANN). The trained ANNs represented the relationship between strain gages at different locations on the bridge. The foundation idea is that the relationship among their strain peaks should be stable when a truck passes over a bridge and the bridge condition does not change. Otherwise, the response of the bridge under abnormal conditions does not match with the prediction of the strain ANNs. A mismatch will cause large Prediction Errors (PEs), and an abnormal responses warning will be triggered. The final conclusion is that the cable tension estimation process is significantly influenced by wind load conditions. By identifying the general critical wind load conditions using Decision Trees (DTs), cable tensions can be estimated using just two hours of data. The computational load and storage requirements can be heavily reduced by limiting the data to two hours. ☐ In summary, bearing displacements, truck-induced strain peaks, and cable tensions were monitored closely using a variety of proposed methodologies, and each methodology has shown promise in adding to the way in which SHM data can be used for assessing bridge conditions.Item PathRings: a web-based tool for exploration of ortholog and expression data in biological pathways(BioMed Central Ltd., 2015-05-19) Zhu, Yongnan; Sun, Liang; Garbarino, Alexander; Schmidt, Carl; Fang, Jinglong; Chen, Jian; Yongnan Zhu, Liang Sun, Alexander Garbarino, Carl Schmidt, Jinglong Fang and Jian Chen; Sun, Liang; Schmidt, CarlBackground High-throughput methods are generating biological data on a vast scale. In many instances, genomic, transcriptomic, and proteomic data must be interpreted in the context of signaling and metabolic pathways to yield testable hypotheses. Since humans can interpret visual information rapidly, a means for interactive visual exploration that lets biologists interpret such data in a comprehensive and exploratory manner would be invaluable. However, humans have limited memory capacity. Current visualization tools have limited viewing and manipulation capabilities to address complex data analysis problems, and visual exploratory tools are needed to reduce the high mental workload imposed on biologists. Results We present PathRings, a new interactive web-based, scalable biological pathway visualization tool for biologists to explore and interpret biological pathways. PathRings integrates metabolic and signaling pathways from Reactome in a single compound graph visualization, and uses color to highlight genes and pathways affected by input data. Pathways are available for multiple species and analysis of user-defined species or input is also possible. PathRings permits an overview of the impact of gene expression data on all pathways to facilitate visual pattern finding. Detailed pathways information can be opened in new visualizations while maintaining the overview, that form a visual exploration provenance. A dynamic multi-view bubbles interface is designed to support biologists’ analytical tasks by letting users construct incremental views that further reflect biologists’ analytical process. This approach decomposes complex tasks into simpler ones and automates multi-view management. Conclusions PathRings has been designed to accommodate interactive visual analysis of experimental data in the context of pathways defined by Reactome. Our new approach to interface design can effectively support comparative tasks over substantially larger collection than existing tools. The dynamic interaction among multi-view dataset visualization improves the data exploration. PathRings is available free at http://raven.anr.udel.edu/~sunliang/PathRings webcite and the source code is hosted on Github: https://github.com/ivcl/PathRings webcite.Item WebGIVI: a web-based gene enrichment analysis and visualization tool(BioMed Central, 2017-05-04) Sun, Liang; Zhu, Yongnan; Mahmood, A. S. M. Ashique; Tudor, Catalina O.; Ren, Jia; Vijay-Shanker, K.; Chen, Jian; Schmidt, Carl J.; Liang Sun, Yongnan Zhu, A. S. M. Ashique Mahmood, Catalina O. Tudor, Jia Ren, K. Vijay-Shanker, Jian Chen and Carl J. Schmidt; Sun, Liang; Mahmood, A. S. M. Ashique; Tudor, Catalina O.; Ren, Jia; Vijay-Shanker, K.; Schmidt, Carl J.BACKGROUND: A major challenge of high throughput transcriptome studies is presenting the data to researchers in an interpretable format. In many cases, the outputs of such studies are gene lists which are then examined for enriched biological concepts. One approach to help the researcher interpret large gene datasets is to associate genes and informative terms (iTerm) that are obtained from the biomedical literature using the eGIFT text-mining system. However, examining large lists of iTerm and gene pairs is a daunting task. RESULTS: We have developed WebGIVI, an interactive web-based visualization tool (http://raven.anr.udel.edu/webgivi/) to explore gene:iTerm pairs. WebGIVI was built via Cytoscape and Data Driven Document JavaScript libraries and can be used to relate genes to iTerms and then visualize gene and iTerm pairs. WebGIVI can accept a gene list that is used to retrieve the gene symbols and corresponding iTerm list. This list can be submitted to visualize the gene iTerm pairs using two distinct methods: a Concept Map or a Cytoscape Network Map. In addition, WebGIVI also supports uploading and visualization of any two-column tab separated data. CONCLUSIONS: WebGIVI provides an interactive and integrated network graph of gene and iTerms that allows filtering, sorting, and grouping, which can aid biologists in developing hypothesis based on the input gene lists. In addition, WebGIVI can visualize hundreds of nodes and generate a high-resolution image that is important for most of research publications. The source code can be freely downloaded at https://github.com/sunliang3361/WebGIVI. The WebGIVI tutorial is available at http://raven.anr.udel.edu/webgivi/tutorial.php.