Data-Driven and Model-Based Methods with Physics-Guided Machine Learning for Damage Identification

Structural health monitoring (SHM) has been widely used for structural damage diagnosis and prognosis of a wide range of civil, mechanical, and aerospace structures. SHM methods are generally divided into two categories: (1) model-based methods; (2) data-driven methods. Compared with data-driven SHM, model-based methods provide an updated physics-based numerical model that can be used for damage prognosis when long-term data is available. However, the performance of model-based methods is susceptible to modeling error in establishing the numerical model, which is usually unavoidable due to model simplification and omission. The major challenge of data-driven SHM methods lies in data insufficiency, e.g., lack of data covering as many as possible damage states, especially for large-scale structures. Hence, multi-site damage identification using data-driven methods can be more challenging as pattern recognition theoretically requires sufficient data from each damage scenario. The main objectives of this dissertation are to: (1) integrate model-based and data-driven SHM methods so that their shortcomings can be weakened while their respective merits can be preserved when implementing damage identification; (2) improve the accuracy of data-driven methods for multi-site damage identification with limited measured data. To achieve the first research objective, physics-guided machine learning (PGML) is proposed to improve the performance of pattern recognition in data-driven SHM with insufficient measured data. The results of model-based SHM (i.e., FE model updating) are taken as an implicit representation of physics underlying the monitored structure, which is incorporated into the learning process of a neural network model with the physics guidance introduced into the loss function. In addition to PGML, transfer learning (TL) is used to bridge the gap between the numerical and experimental domains of SHM. The distribution difference and manifold discrepancy between the two domains is minimized through TL as a means of domain adaptation. To improve the performance of multi-site damage identification in data-driven SHM, multi-label classification (MLC) and constrained independent component analysis(cICA) methods are applied to investigate the correlations between damage cases sharing common damaged sites. Finally, as a case study, a two-step strategy of identifying structural damage of offshore wind turbines via FE model updating is proposed

Nuevas aportaciones al desarrollo de modelos QSAR/QSPR para la predicción de la mutagenicidad de contaminantes ambientales y su interacción con sustancias activas presentes en el medio

Se estudió mediante modelos QSAR, la posible mutagenicidad de sustancias presentes en el medio ambiente como los ácidos haloacéticos (derivados de la cloración del agua) y los carbonilos alfa, beta insaturados (sobre todo los empleados como monómeros para la preparación de materiales dentales de restauración) y su posible interacción con la beta ciclodextrina, la cual está presente como excipiente en productos farmacéuticos y como estabilizador de aromas, colorantes y algunas vitaminas en alimentos. Como resultado de este estudio pudimos destacar: -El ácido fluoroiodoacético y difluoroiodoacético podrían ser mutagénicos debido a los valores de potencia mutagénica obtenidos con los modelos desarrollados. Sustancias que podrían encontrarse en aguas fluoradas ricas en ioduro/bromuro. Además es posible que estén presentes en aguas fluoradas ricas en bromuro/ioduro hecho que pondría en duda la necesidad de fluorar el agua potable. - Sustancias comúnmente empleadas como monómeros dentales presentaron predicciones negativas para el ensayo de Ames y un carácter mutagénico para el ensayo con células de mamífero, a excepción del UDMA (Uretil dimetacrilato). - Respecto a la posible interacción de estas sustancias con la beta-ciclodextrina, los ácidos haloacéticos presentan valores de complejación inferiores a los que normalmente presentan fármacos o componentes de los alimentos, por lo que es de esperar que la interacción entre los ácidos haloacéticos y la beta-CD sea de escasa importancia. En cuanto a los monómeros dentales hay que resaltar que sustancias como el TEGDMA, 1,6-ADMA, 1,8-ADMA, GMR, MEPC y 6-HHMA, predichos como mutagénicos, presentan valores de complejación superiores a los que presentan fármacos o componentes de los alimentos. Por lo tanto, estas sustancias podrían desplazar de sus complejos a fármacos o componentes de los alimentos pudiéndose llegar a algún tipo de interacción.Farmaci

Projection-Based Clustering through Self-Organization and Swarm Intelligence

It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm (DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures. The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining

Projection-Based Clustering through Self-Organization and Swarm Intelligence: Combining Cluster Analysis with the Visualization of High-Dimensional Data

Cluster Analysis; Dimensionality Reduction; Swarm Intelligence; Visualization; Unsupervised Machine Learning; Data Science; Knowledge Discovery; 3D Printing; Self-Organization; Emergence; Game Theory; Advanced Analytics; High-Dimensional Data; Multivariate Data; Analysis of Structured Dat

Toxicity prediction using multi-disciplinary data integration and novel computational approaches

Current predictive tools used for human health assessment of potential chemical hazards rely primarily on either chemical structural information (i.e., cheminformatics) or bioassay data (i.e., bioinformatics). Emerging data sources such as chemical libraries, high throughput assays and health databases offer new possibilities for evaluating chemical toxicity as an integrated system and overcome the limited predictivity of current fragmented efforts; yet, few studies have combined the new data streams. This dissertation tested the hypothesis that integrative computational toxicology approaches drawing upon diverse data sources would improve the prediction and interpretation of chemically induced diseases. First, chemical structures and toxicogenomics data were used to predict hepatotoxicity. Compared with conventional cheminformatics or toxicogenomics models, interpretation was enriched by the chemical and biological insights even though prediction accuracy did not improve. This motivated the second project that developed a novel integrative method, chemical-biological read-across (CBRA), that led to predictive and interpretable models amenable to visualization. CBRA was consistently among the most accurate models on four chemical-biological data sets. It highlighted chemical and biological features for interpretation and the visualizations aided transparency. Third, we developed an integrative workflow that interfaced cheminformatics prediction with pharmacoepidemiology validation using a case study of Stevens Johnson Syndrome (SJS), an adverse drug reaction (ADR) of major public health concern. Cheminformatics models first predicted potential SJS inducers and non-inducers, prioritizing them for subsequent pharmacoepidemiology evaluation, which then confirmed that predicted non-inducers were statistically associated with fewer SJS occurrences. By combining cheminformatics' ability to predict SJS as soon as drug structures are known, and pharmacoepidemiology's statistical rigor, we have provided a universal scheme for more effective study of SJS and other ADRs. Overall, this work demonstrated that integrative approaches could deliver more predictive and interpretable models. These models can then reliably prioritize high risk chemicals for further testing, allowing optimization of testing resources. A broader implication of this research is the growing role we envision for integrative methods that will take advantage of the various emerging data sources.Doctor of Philosoph

Large space structures and systems in the space station era: A bibliography with indexes

Bibliographies and abstracts are listed for 1219 reports, articles, and other documents introduced into the NASA scientific and technical information system between July 1, 1990 and December 31, 1990. The purpose is to provide helpful information to the researcher, manager, and designer in technology development and mission design according to system, interactive analysis and design, structural and thermal analysis and design, structural concepts and control systems, electronics, advanced materials, assembly concepts, propulsion, and solar power satellite systems

Proceedings of the Scientific-Practical Conference "Research and Development - 2016"

talent management; sensor arrays; automatic speech recognition; dry separation technology; oil production; oil waste; laser technolog

The Human Cell Atlas White Paper

The Human Cell Atlas (HCA) will be made up of comprehensive reference maps of all human cells - the fundamental units of life - as a basis for understanding fundamental human biological processes and diagnosing, monitoring, and treating disease. It will help scientists understand how genetic variants impact disease risk, define drug toxicities, discover better therapies, and advance regenerative medicine. A resource of such ambition and scale should be built in stages, increasing in size, breadth, and resolution as technologies develop and understanding deepens. We will therefore pursue Phase 1 as a suite of flagship projects in key tissues, systems, and organs. We will bring together experts in biology, medicine, genomics, technology development and computation (including data analysis, software engineering, and visualization). We will also need standardized experimental and computational methods that will allow us to compare diverse cell and tissue types - and samples across human communities - in consistent ways, ensuring that the resulting resource is truly global. This document, the first version of the HCA White Paper, was written by experts in the field with feedback and suggestions from the HCA community, gathered during recent international meetings. The White Paper, released at the close of this yearlong planning process, will be a living document that evolves as the HCA community provides additional feedback, as technological and computational advances are made, and as lessons are learned during the construction of the atlas

Evaluation of the availability and applicability of computational approaches in the safety assessment of nanomaterials: Final report of the Nanocomput project

This is the final report of the Nanocomput project, the main aims of which were to review the current status of computational methods that are potentially useful for predicting the properties of engineered nanomaterials, and to assess their applicability in order to provide advice on the use of these approaches for the purposes of the REACH regulation. Since computational methods cover a broad range of models and tools, emphasis was placed on Quantitative Structure-Property Relationship (QSPR) and Quantitative Structure-Activity Relationship (QSAR) models, and their potential role in predicting NM properties. In addition, the status of a diverse array of compartment-based mathematical models was assessed. These models comprised toxicokinetic (TK), toxicodynamic (TD), in vitro and in vivo dosimetry, and environmental fate models. Finally, based on systematic reviews of the scientific literature, as well as the outputs of the EU-funded research projects, recommendations for further research and development were also made. The Nanocomput project was carried out by the European Commission’s Joint Research Centre (JRC) for the Directorate-General (DG) for Internal Market, Industry, Entrepreneurship and SMEs (DG GROW) under the terms of an Administrative Arrangement between JRC and DG GROW. The project lasted 39 months, from January 2014 to March 2017, and was supported by a steering group with representatives from DG GROW, DG Environment and the European Chemicals Agency (ECHA).JRC.F.3-Chemicals Safety and Alternative Method