Bridge monitoring studies indicate that the quasi-static response of a bridge, while dependent on various input forces, is affected predominantly by variations in temperature. In many structures, the quasi-static response can even be approximated as equal to its thermal response. Consequently, interpretation of measurements from quasi-static monitoring requires accounting for the thermal response in measurements. Developing solutions to this challenge, which is critical to relate measurements to decision-making and thereby realize the full potential of SHM for bridge management, is the main focus of this research. This research proposes a data-driven approach referred to as temperature-based measurement interpretation (TB-MI) approach for structural performance evaluation of bridges based on continuous bridge monitoring. The approach characterizes and predicts thermal response of structures by exploiting the relationship between temperature distributions across a bridge and measured bridge response. The TB-MI approach has two components - (i) a regression-based thermal response prediction (RBTRP) methodology and (ii) an anomaly detection methodology. The RBTRP methodology generates models to predict real-time structural response from distributed temperature measurements. The anomaly detection methodology analyses prediction error signals, which are the differences between predicted and real-time response to detect the onset of anomaly events. In order to generate realistic data-sets for evaluating the proposed TB-MI approach, this research has built a small-scale truss structure in the laboratory as a test-bed. The truss is subject to accelerated diurnal temperature cycles using a system of heating lamps. Various damage scenarios are also simulated on this structure. This research further investigates if the underlying concept of using distributed temperature measurements to predict thermal response can be implemented using physics-based models. The case study of Cleddau Bridge is considered. This research also extends the general concept of predicting bridge response from knowledge of input loads to predict structural response due to traffic loads. Starting from the TB-MI approach, it creates an integrated approach for analyzing measured response due to both thermal and vehicular loads. The proposed approaches are evaluated on measurement time-histories from a number of case studies including numerical models, laboratory-scale truss and full-scale bridges. Results illustrate that the approaches accurately predicts thermal response, and that anomaly events are detectable using signal processing techniques such as signal subtraction method and cointegration. The study demonstrates that the proposed TB-MI approach is applicable for interpreting measurements from full-scale bridges, and can be integrated within a measurement interpretation platform for continuous bridge monitoring.

The punctuality of rail traffic in Sweden has not increased in a long period of time and this causes problems for people, companies and the community as it affects everyone in different ways. How the Swedish Transport Administration and SJ work on improving the train traffic and punctuality will be addressed in this study. This study will have focus on how data-driven methods can increase the punctuality of train traffic. The study will show which factors are critical for data-driven methods using literature as well as models with descriptions, as a result, the Swedish Transport Administration and SJ will be in focus for how the development of punctuality of train traffic goes. This is a case study with a literature search as well as qualitative interviews as data collection, the literature search will primarily show which factors are necessary for datadriven methods and it will also help form the interview questions, later on the interviews will show how the Swedish Transport Administration and SJ work today so that comparisons can be drawn and a result can be produced.

Soil moisture is an important variable in many environmental researches and application areas as it affects the interactions between atmosphere and land surface by controlling the energy and water exchange. The current measurement techniques are insufficient to acquire accurate large-scale root zone soil moisture (RZSM) data at the spatial resolution of interest. Though assorted models have been successfully applied in relatively small areas to estimate RZSM, the large-scale estimation is still facing challenges as it requires the flexibility and practicality of the models for the applications under various conditions. Though physically based soil moisture models are widely used, the errors in model physics affect the flexibility of these models meanwhile their large demand of data and computational resources reduces the practicality. On the contrary, the statistical and data-driven methods have high potential but their applications for large-scale RZSM estimation have not been fully explored. To develop feasible models for large-scale RZSM estimation using the surface observations, artificial neural networks, specifically multilayer perceptrons (MLPs), were applied in this study to estimate RZSM at the depths of 20cm and 50cm, using the data of 557 stations in the United States. Two experiments including four models were developed and the input variables of the models were carefully selected. The sensitivity analysis found that surface soil moisture and the cumulative rainfall, snowfall, air temperature and surface soil temperature were important inputs. If given soil texture data as inputs, the models achieved better performance and were extremely sensitive to them. The results showed that the MLPs were effective and flexible for the estimation of soil moisture at 20cm under various climate types and were insensitive to the potential errors in soil moisture datasets. However, the results of the estimation at 50cm are not as good as that of the 20cm. / Thesis / Master of Science (MSc)

No description available.

No / We present a new skin deformation method to create dynamic skin deformations in this paper. The core elements of our approach are a dynamic deformation model, an efficient data-driven finite difference solution, and a curve-based representation of 3D models.We first reconstruct skin deformation models at different poses from the taken photos of a male human arm movement to achieve real deformed skin shapes. Then, we extract curves from these reconstructed skin deformation models. A new dynamic deformation model is proposed to describe physics of dynamic curve deformations, and its finite difference solution is developed to determine shape changes of the extracted curves. In order to improve visual realism of skin deformations, we employ data-driven methods and introduce skin shapes at the initial and final poses in to our proposed dynamic deformation model. Experimental examples and comparisons made in this paper indicate that our proposed dynamic skin deformation technique can create realistic deformed skin shapes efficiently with a small data size.

In the era of Industry 4.0, conventional supply chains are undergoing a transformation into digital supply chains with the wide application of digital technologies such as big data, cloud computing, and Internet of Things. A digital supply chain is an intelligent and value-driven process that has superior features such as speed, flexibility, transparency, and real-time inventory monitoring and management. This concept is further included in the framework of Supply Chain 4.0, which emphasizes the connection between supply chain and Industry 4.0. In this context, data analytics for supply chain management presents a promising research opportunity. This thesis aims to investigate the use of data analytics in supply chain decision-making, including modelling, monitoring, and optimization. First, this thesis investigates supply chain monitoring (SCMo) using data analytics. The goal of SCMo is to raise an alarm when abnormal supply chain events occur and identify the potential reason. We propose a framework of SCMo based on a data-driven method, principal component analysis (PCA). Within this framework, supply chain data such as inventory levels and customer demand are collected, and the normal operating conditions of a supply chain are characterized using PCA. Fault detection and diagnosis are implemented by examining the monitoring statistics and variable contributions. A supply chain simulation model is developed to carry out the case studies. The results show that dynamic PCA (DPCA) successfully detected abnormal behaviour of the supply chain, such as transportation delay, low production rate, and supply shortage. Moreover, the contribution plot is shown to be effective in interpreting the abnormality and identify the fault-related variables. The method of using data-driven methods for SCMo is named data-driven SCMo in this work. Then, a further investigation of data-driven SCMo based on another statistical process monitoring method, canonical variate analysis (CVA), is conducted. CVA utilizes the state-space model of a system and determines the canonical states by maximizing the correlation between the combination of past system outputs and inputs and the combination of future outputs. A state-space model of supply chain is developed, which forms the basis of applying CVA to detect supply chain faults. The performance of CVA and PCA are assessed and compared in terms of dimensionality reduction, false alarm rate, missed detection rate, and detection delay. Case studies show that CVA identifies a smaller system order than PCA and achieves comparable performance to PCA in a lower-dimensional latent space. Next, we investigate data-driven supply chain control under uncertainty with risk taken into account. The method under investigation is reinforcement learning (RL). Within the RL framework, an agent learns an optimal policy that maps the state to action during the process of interacting with the non-deterministic environment, such that a numerical reward is maximized. The current literature regarding supply chain control focuses on conventional RL that maximizes the expected return. However, this may be not the best option for risk-averse decision makers. In this work, we explore the use of safe RL, which takes into account the concept of risk in the learning process. Two safe RL algorithms, Q-hat-learning and Beta-pessimistic Q-learning, are investigated. Case studies are carried out based on the supply chain simulator developed using agent-based modelling. Results show that Q-learning has the best performance under normal scenarios, while safe RL algorithms perform better under abnormal scenarios and are more robust to changes in the environment. Moreover, we find that the benefits of safe RL are more pronounced in a closed-loop supply chain. Finally, we investigate real-time supply chain optimization. The operational optimization problems for supply chains of realistic size are often large and complex, and solving them in real time can be challenging. This work aims to address the problem by using a deep learning-based model predictive control (MPC) technique. The MPC problem for supply chain operation is formulated based on the state space model of a supply chain, and the optimal state-input pairs are precomputed in the offline phase. Then, a deep neural network is built to map the state to input, which is then used in the online phase to reduce solution time. We propose an approach to implement the deep learning-based MPC method when there are delayed terms in the system, and a heuristic approach to feasibility recovery for mixed-integer MPC, with binary decision variables taken into account. Case studies show that compared with solving the nominal MPC problem online, deep learning-based MPC can provide near-optimal solution at a lower computational cost. / Thesis / Doctor of Philosophy (PhD)

Reconstruction of sonar images is an inverse problem, which is normally solved with model-based methods. These methods may introduce undesired artifacts called angular and range leakage into the reconstruction. In this thesis, a method called Learned Primal-Dual Reconstruction, which combines a data-driven and a model-based approach, is used to investigate the use of data-driven methods for reconstruction within sonar imaging. The method uses primal and dual variables inspired by classical optimization methods where parts are replaced by convolutional neural networks to iteratively find a solution to the reconstruction problem. The network is trained and validated with synthetic data on eight models with different architectures and training parameters. The models are evaluated on measurement data and the results are compared with those from a purely model-based method. Reconstructions performed on synthetic data, where a ground truth image is available, show that it is possible to achieve reconstructions with the data-driven method that have less leakage than reconstructions from the model-based method. For reconstructions performed on measurement data where no ground truth is available, some variants of the learned model achieve a good result with less leakage.

Data-driven maintenance bears the potential to realize various benefits based on multifaceted data assets generated in increasingly digitized industrial environments. By taking advantage of modern methods and technologies from the field of data science and analytics (DSA), it is possible, for example, to gain a better understanding of complex technical processes and to anticipate impending machine faults and failures at an early stage. However, successful implementation of DSA projects requires multidisciplinary expertise, which can rarely be covered by individual employees or single units within an organization. This expertise covers, for example, a solid understanding of the domain, analytical method and modeling skills, experience in dealing with different source systems and data structures, and the ability to transfer suitable solution approaches into information systems. Against this background, various approaches have emerged in recent years to make the implementation of DSA projects more accessible to broader user groups. These include structured procedure models, systematization and modeling frameworks, domain-specific benchmark studies to illustrate best practices, standardized DSA software solutions, and intelligent assistance systems. The present thesis ties in with previous efforts and provides further contributions for their continuation. More specifically, it aims to create supportive artifacts for the selection, evaluation, and application of data-driven methods in the field of industrial maintenance. For this purpose, the thesis covers four artifacts, which were developed in several publications. These artifacts include (i) a comprehensive systematization framework for the description of central properties of recurring data analysis problems in the field of industrial maintenance, (ii) a text-based assistance system that offers advice regarding the most suitable class of analysis methods based on natural language and domain-specific problem descriptions, (iii) a taxonomic evaluation framework for the systematic assessment of data-driven methods under varying conditions, and (iv) a novel solution approach for the development of prognostic decision models in cases of missing label information. Individual research objectives guide the construction of the artifacts as part of a systematic research design. The findings are presented in a structured manner by summarizing the results of the corresponding publications. Moreover, the connections between the developed artifacts as well as related work are discussed. Subsequently, a critical reflection is offered concerning the generalization and transferability of the achieved results. Thus, the thesis not only provides a contribution based on the proposed artifacts; it also paves the way for future opportunities, for which a detailed research agenda is outlined.:List of Figures List of Tables List of Abbreviations 1 Introduction 1.1 Motivation 1.2 Conceptual Background 1.3 Related Work 1.4 Research Design 1.5 Structure of the Thesis 2 Systematization of the Field 2.1 The Current State of Research 2.2 Systematization Framework 2.3 Exemplary Framework Application 3 Intelligent Assistance System for Automated Method Selection 3.1 Elicitation of Requirements 3.2 Design Principles and Design Features 3.3 Prototypical Instantiation and Evaluation 4 Taxonomic Framework for Method Evaluation 4.1 Survey of Prognostic Solutions 4.2 Taxonomic Evaluation Framework 4.3 Exemplary Framework Application 5 Method Application Under Industrial Conditions 5.1 Conceptualization of a Solution Approach 5.2 Prototypical Implementation and Evaluation 6 Discussion of the Results 6.1 Connections Between Developed Artifacts and Related Work 6.2 Generalization and Transferability of the Results 7 Concluding Remarks Bibliography Appendix I: Implementation Details Appendix II: List of Publications A Publication P1: Focus Area Systematization B Publication P2: Focus Area Method Selection C Publication P3: Focus Area Method Selection D Publication P4: Focus Area Method Evaluation E Publication P5: Focus Area Method Application / Datengetriebene Instandhaltung birgt das Potential, aus den in Industrieumgebungen vielfältig anfallenden Datensammlungen unterschiedliche Nutzeneffekte zu erzielen. Unter Verwendung von modernen Methoden und Technologien aus dem Bereich Data Science und Analytics (DSA) ist es beispielsweise möglich, das Verhalten komplexer technischer Prozesse besser nachzuvollziehen oder bevorstehende Maschinenausfälle und Fehler frühzeitig zu erkennen. Eine erfolgreiche Umsetzung von DSA-Projekten erfordert jedoch multidisziplinäres Expertenwissen, welches sich nur selten von einzelnen Personen bzw. Einheiten innerhalb einer Organisation abdecken lässt. Dies umfasst beispielsweise ein fundiertes Domänenverständnis, Kenntnisse über zahlreiche Analysemethoden, Erfahrungen im Umgang mit verschiedenen Quellsystemen und Datenstrukturen sowie die Fähigkeit, geeignete Lösungsansätze in Informationssysteme zu überführen. Vor diesem Hintergrund haben sich in den letzten Jahren verschiedene Ansätze herausgebildet, um die Durchführung von DSA-Projekten für breitere Anwendergruppen zugänglich zu machen. Dazu gehören strukturierte Vorgehensmodelle, Systematisierungs- und Modellierungsframeworks, domänenspezifische Benchmark-Studien zur Veranschaulichung von Best Practices, Standardlösungen für DSA-Software und intelligente Assistenzsysteme. An diese Arbeiten knüpft die vorliegende Dissertation an und liefert weitere Artefakte, um insbesondere die Selektion, Evaluation und Anwendung datengetriebener Methoden im Bereich der industriellen Instandhaltung zu unterstützen. Insgesamt erstreckt sich die Abhandlung auf vier Artefakte, die in einzelnen Publikationen erarbeitet wurden. Dies umfasst (i) ein umfangreiches Systematisierungsframework zur Beschreibung zentraler Ausprägungen wiederkehrender Datenanalyseprobleme im Bereich der industriellen Instandhaltung, (ii) ein textbasiertes Assistenzsystem, welches ausgehend von natürlichsprachlichen und domänenspezifischen Problembeschreibungen eine geeignete Klasse von Analysemethoden vorschlägt, (iii) ein taxonomisches Evaluationsframework zur systematischen Bewertung von datengetriebenen Methoden unter verschiedenen Rahmenbedingungen sowie (iv) einen neuartigen Lösungsansatz zur Entwicklung von prognostischen Entscheidungsmodellen im Fall von eingeschränkter Informationslage. Die Konstruktion der Artefakte wird durch einzelne Forschungsziele im Rahmen eines systematischen Forschungsdesigns angeleitet. Neben der Darstellung der einzelnen Forschungsbeiträge unter Bezugnahme auf die erzielten Ergebnisse der dazugehörigen Publikationen werden auch die Verbindungen zwischen den entwickelten Artefakten beleuchtet und Zusammenhänge zu angrenzenden Arbeiten hergestellt. Zudem erfolgt eine kritische Reflektion der Ergebnisse hinsichtlich ihrer Verallgemeinerung und Übertragung auf andere Rahmenbedingungen. Dadurch liefert die vorliegende Abhandlung nicht nur einen Beitrag anhand der erzeugten Artefakte, sondern ebnet auch den Weg für fortführende Forschungsarbeiten, wofür eine detaillierte Forschungsagenda erarbeitet wird.:List of Figures List of Tables List of Abbreviations 1 Introduction 1.1 Motivation 1.2 Conceptual Background 1.3 Related Work 1.4 Research Design 1.5 Structure of the Thesis 2 Systematization of the Field 2.1 The Current State of Research 2.2 Systematization Framework 2.3 Exemplary Framework Application 3 Intelligent Assistance System for Automated Method Selection 3.1 Elicitation of Requirements 3.2 Design Principles and Design Features 3.3 Prototypical Instantiation and Evaluation 4 Taxonomic Framework for Method Evaluation 4.1 Survey of Prognostic Solutions 4.2 Taxonomic Evaluation Framework 4.3 Exemplary Framework Application 5 Method Application Under Industrial Conditions 5.1 Conceptualization of a Solution Approach 5.2 Prototypical Implementation and Evaluation 6 Discussion of the Results 6.1 Connections Between Developed Artifacts and Related Work 6.2 Generalization and Transferability of the Results 7 Concluding Remarks Bibliography Appendix I: Implementation Details Appendix II: List of Publications A Publication P1: Focus Area Systematization B Publication P2: Focus Area Method Selection C Publication P3: Focus Area Method Selection D Publication P4: Focus Area Method Evaluation E Publication P5: Focus Area Method Application

Gravel roads are essential for economic development as they facilitate the movement of people, transportation of goods and services, and promote cultural and social development. They typically connect sparsely populated rural areas to urban centres, providing essential access for residents and entrepreneurs. Maintaining these roads to an acceptable level of service is crucial for the efficient and safe transportation of goods and services. However, substantial maintenance investmentis required, yet resources are limited. Gravel roads are prone to dust, potholes, corrugations, rutting and loose gravel. They deteriorate faster than paved roads, and their failure development is affected by traffic action and physical, geometric and climatic factors. Thus, more condition monitoring and proper road condition assessment are necessary for dynamic maintenance planning to reach efficiency and effectiveness using objective, data-driven condition assessment methods to ensure all-year-round access. However, objective data-driven methods (DDMs) are not frequently used for gravel road condition assessment, and where they have been applied, the practical implementation is limited. Instead, visual windshield assessment and manual methods are predominant. Visual assessments are unreliable and susceptible to human judgement errors, while manual methods are time-consuming and labour-intensive. Maintenance activities are predetermined despite dynamic maintenance needs, and the planning is based on historical failure data rather than the actual road condition. This thesis establishes a data-driven approach to gravel road maintenance describing the systematic assessment of the gravel road condition and collection of the condition data to ensure efficient and effective maintenance planning. This thesis uses a design research methodology based on a literature review, concept development, interview study and field experiments. A holistic approach is proposed for data-driven maintenance of gravel roads encompassing objective condition data collection, processing, analysing, and interpreting the findings for obtaining reliable information concerning the condition to gravel road decision support by utilising the opportunities presented by technological advancements, particularly sensor technology. Then, decision-making is primarily influenced by the objectively collected gravel road condition data rather than the evaluator’s perception or experience. The successful implementation of a data-driven approach depends on the quality of the collected data; therefore, data relevance and quality are emphasised in this thesis. The lack of data quality and relevance hinders effective data utilisation, leading to less precisionin decision-making and ineffective decisions. Furthermore, the thesis proposes a participatory data-driven approach for unpaved road condition monitoring, allowing road users to be part of the maintenance process and providing an efficient and effective alternative for collecting road condition data and accomplishing broad coverage at minimum cost. A top-down iiapproach for data-driven gravel road condition classification is proposed to achieve an objective assessment to address the lack of readily available quality and relevant condition data. The established data-driven approach to gravel road maintenance is evaluated and verified with field experiments on three gravel roads in Växjö municipality, Southern Sweden. The research findings indicate that properly implementing a data-driven approach to gravel road maintenance would ensure efficient and effective condition assessment and classification, which are a basis for a maintenance management system of gravel roads and enable road maintainers and authorities to achieve cost-effective decision-making. / Sustainable maintenance of gravel road

[ES] El desarrollo del medio digital ha transformado nuestra forma de consumo en las últimas décadas. La invención de Internet, su democratización, la aparición de múltiples dispositivos de acceso y las redes sociales, la tecnificación de los objetos y la llegada de la inteligencia artificial han tenido un impacto significativo en la sociedad, así como entidades esenciales como las empresas y sus marcas. La integración total de la digitalización en las marcas es una realidad, y cada vez se opta más por este medio como un espacio prioritario para aportar valor al público a través de sus productos o servicios. Esta investigación se centra en profundizar en el significado de la marca digital y en sus características esenciales. Para ello, se realizará un recorrido histórico de la evolución de los signos identitarios con relación a la tecnología, lo que permitirá tener un enfoque global en su adaptación a cada uno de los avances digitales. Además, se analizarán los múltiples significados de marca y se revisará y recogerá la metodología específica para su creación: el branding. Con el fin de entender las particularidades y ventajas de los marcos de trabajo aplicados en el sector digital y del desarrollo de software, se estudiarán metodologías iterativas basadas en sistemas ágiles como el Design Thinking, el Diseño Centrado en Usuario o el Atomic Design, entre otros. Finalmente, a partir del estudio realizado, se generará una metodología híbrida para crear marcas digitales capaces de adaptarse mejor a los cambios de contexto del medio. Para ello, se hará uso de procesos, herramientas y plataformas complementarias empleadas en ámbitos tecnológicos y se diseñará un proceso de revisión constante con el fin de asegurar la calidad y el buen funcionamiento de las marcas en todo momento. / [CA] El desenvolupament dels mitjans digitals han transformat la nostra forma de consum en les últimes dècades. La invenció d'Internet, la seua democratització, l'aparició de múltiples dispositius d'accés i les xarxes socials, la tecnificació dels objectius i l'arribada de la intel·ligència artificial han tingut un impacte significatiu en la societat i en les entitats essencials com les empreses i les seues marques. La integració total de la digitalització en les marques és una realitat, i cada vegada s'opta més per aquest mitjà com un espai prioritari per a aportar valor al públic. Aquesta investigació es centra en aprofundir en el significat de la marca digital i en les seues característiques essencials. Per a això, es realitzarà un recorregut històric de l'evolució dels signes identitaris en relació amb la tecnologia, la qual cosa permetrà tindre un enfocament global de la seua adaptació a cadascun dels sorgiments digitals. A més a més, s'analitzaran els múltiples significats de marca i es revisarà i recollirà la metodologia específica per a la seua creació: el branding. Amb la finalitat d'entendre les particularitats i avantatges dels marcs de treball aplicats al sector digital i del desenvolupament del software, s'estudiaran metodologies iteratives basades en sistemes àgils com el Design Thinking, el Disseny Centrat en l'Usuari, l'Atomic Design, entre d'altres. Finalment, a partir de l'estudi realitzat, es generarà una metodologia híbrida per a crear marques digitals capaces d'adaptar-se millor als canvis de context del mitjà. Per a això, es farà ús dels processos, eines i plataformes complementàries emprades en àmbits tecnològics i es dissenyarà un procés de revisió constant amb la finalitat d'assegurar la qualitat i el bon funcionament de les marques en tot moment. / [EN] The advancement of digital media has significantly impacted how we consume information in recent years. With the Internet and social networks becoming more accessible, coupled with the emergence of multiple access devices and the application of artificial intelligence, society and essential entities such as companies and their brands have been significantly affected. Digitalisation has become an integral part of branding, and companies now prioritize using digital media to provide value to their customers. This research explores the meaning of digital branding and its fundamental characteristics. It will provide a historical overview of how identity signs have evolved with technological advancements, offering a comprehensive approach to their adaptation in the digital age. To fully understand the advantages and nuances of digital and software development frameworks, this study will delve into iterative methodologies based on agile systems, such as Design Thinking, User-Centered Design, and Atomic Design. Ultimately, the study will generate a hybrid methodology for creating digital brands that can adapt better to environmental changes. For this purpose, other complementary processes, such as tools and platforms used in technological fields, will be used. A constant review process will also be present to ensure the quality and proper functioning of the brands at all times. / Canavese Arbona, A. (2023). Diseño de identidades digitales: metodología iterativa para la creación y desarrollo de marcas [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/196737