Im Zusammenhang mit Sanierungsmaßnahmen an bestehenden Brückentragwerken muss auch der Zustand ihrer Lager bewertet werden. Im Falle historischer Brückenlagertechnik bleiben hierbei oft Fragen unbeantwortet, zum Beispiel zu den Möglichkeiten einer zerstörungsfreien Beurteilung des Lagerwerkstoffs. Am Beispiel des ab etwa 1880 zunehmend für Lager verwendeten Werkstoffs Stahlguss – früher zumeist Stahlformguss – verfolgt der Beitrag den Ansatz, die mittels Spektralanalyse ermittelte Werkstoffrezeptur als Ausgangspunkt für zentrale Aussagen zur Werkstofffestigkeit zu nutzen. Der Artikel erläutert die Methodik, diskutiert Ergebnisse des Vorgehens und zeigt eine interessante Option für eine quasi zerstörungsfreie Werkstoffuntersuchung auf.

Nonsuicidal self-injury (NSSI) is a form of direct self-harm that involves willful damage to bodily tissue without suicidal intent; it includes behaviors such as cutting, burning, carving, biting, scraping, and scratching of the skin, as well as hitting and skin and scab picking. Engagement in NSSI has been shown to relate to a host of maladaptive states and outcomes, including depression, anxiety, poor emotion regulation, and suicidal ideation and attempts. Socially sanctioned forms of body modification (e.g. tattoos and piercings) have received less attention as potential self-harm outlets, but have been posited to represent similar physical outlets of emotional pain. Indirect self-harm, in contrast, can include behaviors such as substance abuse, disordered eating, participation in abusive relationships, and sexual risk-taking. Extant literature suggests that self-harm in either form is associated with higher levels of self-criticism than healthy adults endorse. However, few studies have examined self-criticism in each of these self-harming subgroups. Female participants were recruited online using Amazon's Mechanical Turk. Results from the present study indicate that 1) direct self-harming individuals are considerably more self-critical than indirect self-harmers and control subjects, 2) those who engage in multiple forms of self-harm are more self-critical than those engaging in only one form, 3) self-criticism did not significantly predict self-harming behaviors, and 4) there are no significant differences in self-criticism based on developmental trajectory of self-harming behaviors. Additionally, individuals with body modification (e.g. tattoos, piercings) did not exhibit different levels of self-criticism than those without socially sanctioned alterations. Implications, limitations, and future directions for research of this nature are discussed.

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Beneath North America's roads lie 1.6 million miles of pipeline that provides users with potable water and carry away wastewater. These buried infrastructure systems have been functioning for duration longer than their intended design life, often with little or no repair. Asset management of pipeline systems pose a major challenge for most municipalities due to budgetary constraints, demand for quality service, and need to preserve existing pipeline infrastructure. The first step in developing and implementing a comprehensive asset management plan is to perform a condition assessment. There is a gamut of inspection and monitoring technologies available to enable the condition assessment of pipelines. All of these have advantages and limitations, which determine the performance quality and effectiveness of an individual technology for particular utility assets. Unfortunately, utilities choose technologies not suitable for their specific assets and collect data that is not useful for understanding the condition of their system. The objective of this thesis is to develop a framework for the effective selection of condition assessment technologies for water and wastewater utilities. A Microsoft-Excel based framework is developed to help the utility managers in selecting condition assessment technologies for their water and wastewater pipeline assets. The recommended tool selection approach uses a multi-step exclusion protocol in which the tools are excluded on the basis of their applicability relating to technical feasibility and technical suitability for a particular situation. Usable tools are then compared against a performance and cost database to determine performance and cost in a given project/ utility condition. This thesis provides a brief description and review of 24 non-destructive commercialized condition assessment technologies, including the principal and implementation considerations. A framework for decision system tool was developed to facilitate utilities in selecting appropriate condition assessment technologies. This framework could facilitate the selection of usable technologies by excluding the options which are not technically feasible and suitable. The user can then further explore the usable tools and determine the most suitable technologies for their assets. The data considered in the research is provided by technology providers, thus it may lack complete understanding of the capabilities and limitations of technology. This thesis also presents a case study which highlights the existing gap between the understanding of capabilities and limitations of various technologies. A program is developed as a part of this thesis, Condition Assessment Selection Tool (CAST), which consists of performance and economic database, a graphical user interface to facilitate user input, and the results of the comparison of each usable technology in the database to the project information provided by the user for their assets. The results are presented as performance indices and economic indices indicating the performance and technology cost of usable technologies. A data reliability index was also developed to provide a scale for comparing the reliability of the existing data in the database. / Master of Science

In this dissertation, we introduce three projects in machine learning and reliability applications after the general introductions in Chapter 1. The first project concentrates on the multivariate sensory data, the second project is related to the bivariate recurrent process, and the third project introduces thermal index (TI) estimation in accelerated destructive degradation test (ADDT) data, in which an R package is developed. All three projects are related to and can be used to solve certain reliability problems. Specifically, in Chapter 2, we introduce a clustering method for multivariate functional data. In order to cluster the customized events extracted from multivariate functional data, we apply the functional principal component analysis (FPCA), and use a model based clustering method on a transformed matrix. A penalty term is imposed on the likelihood so that variable selection is performed automatically. In Chapter 3, we propose a covariate-adjusted model to predict next event in a bivariate recurrent event system. Inspired by geyser eruptions in Yellowstone National Park, we consider two event types and model their event gap time relationship. External systematic conditions are taken account into the model with covariates. The proposed covariate adjusted recurrent process (CARP) model is applied to the Yellowstone National Park geyser data. In Chapter 4, we compare estimation methods for TI. In ADDT, TI is an important index indicating the reliability of materials, when the accelerating variable is temperature. Three methods are introduced in TI estimations, which are least-squares method, parametric model and semi-parametric model. An R package is implemented for all three methods. Applications of R functions are introduced in Chapter 5 with publicly available ADDT datasets. Chapter 6 includes conclusions and areas for future works. / Doctor of Philosophy / This dissertation focuses on three projects that are all related to machine learning and reliability. Specifically, in the first project, we propose a clustering method designated for events extracted from multivariate sensory data. When the customized event is corresponding to reliability issues, such as aging procedures, clustering results can help us learn different event characteristics by examining events belonging to the same group. Applications include diving behavior segmentation based on vehicle sensory data, where multiple sensors are measuring vehicle conditions simultaneously and events are defined as vehicle stoppages. In our project, we also proposed to conduct sensor selection by three different penalizations including individual, variable and group. Our method can be applied for multi-dimensional sensory data clustering, when optimal sensor design is also an objective. The second project introduces a covariate-adjusted model accommodated to a bivariate recurrent event process system. In such systems, events can occur repeatedly and event occurrences for each type can affect each other with certain dependence. Events in the system can be mechanical failures which is related to reliability, while next event time and type predictions are usually of interest. Precise predictions on the next event time and type can essentially prevent serious safety and economy consequences following the upcoming event. We propose two CARP models with marginal behaviors as well as the dependence structure characterized in the bivariate system. We innovate to incorporate external information to the model so that model results are enhanced. The proposed model is evaluated in simulation studies, while geyser data from Yellowstone National Park is applied. In the third project, we comprehensively discuss three estimation methods for thermal index. They are the least-square method, parametric model and semi-parametric model. When temperature is the accelerating variable, thermal index indicates the temperature at which our materials can hold up to a certain time. In reality, estimating the thermal index precisely can prolong lifetime of certain product by choosing the right usage temperature. Methods evaluations are conducted by simulation study, while applications are applied to public available datasets.