TY - CHAP A1 - Ahmad, Sofyan A1 - Zabel, Volkmar A1 - Könke, Carsten T1 - WAVELET-BASED INDICATORS FOR RESPONSE SURFACE MODELS IN DAMAGE IDENTIFICATION OF STRUCTURES T2 - Digital Proceedings, International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering : July 04 - 06 2012, Bauhaus-University Weimar N2 - In this paper, wavelet energy damage indicator is used in response surface methodology to identify the damage in simulated filler beam railway bridge. The approximate model is addressed to include the operational and surrounding condition in the assessment. The procedure is split into two stages, the training and detecting phase. During training phase, a so-called response surface is built from training data using polynomial regression and radial basis function approximation approaches. The response surface is used to detect the damage in structure during detection phase. The results show that the response surface model is able to detect moderate damage in one of bridge supports while the temperatures and train velocities are varied. KW - Angewandte Mathematik KW - Computerunterstütztes Verfahren KW - Angewandte Informatik Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170306-27588 SN - 1611-4086 ER - TY - THES A1 - Alkam, Feras T1 - Vibration-based Monitoring of Concrete Catenary Poles using Bayesian Inference N2 - This work presents a robust status monitoring approach for detecting damage in cantilever structures based on logistic functions. Also, a stochastic damage identification approach based on changes of eigenfrequencies is proposed. The proposed algorithms are verified using catenary poles of electrified railways track. The proposed damage features overcome the limitation of frequency-based damage identification methods available in the literature, which are valid to detect damage in structures to Level 1 only. Changes in eigenfrequencies of cantilever structures are enough to identify possible local damage at Level 3, i.e., to cover damage detection, localization, and quantification. The proposed algorithms identified the damage with relatively small errors, even at a high noise level. KW - Parameteridentifikation KW - Bayesian Inference, Uncertainty Quantification KW - Inverse Problems KW - Damage Identification KW - Concrete catenary pole KW - SHM KW - Inverse Probleme KW - Bayes’schen Inferenz KW - Unschärfequantifizierung KW - Schadenerkennung KW - Oberleitungsmasten Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210526-44338 UR - https://asw-verlage.de/katalog/vibration_based_monitoring_of_co-2363.html VL - 2021 PB - Bauhaus-Universitätsverlag CY - Weimar ER - TY - THES A1 - Brehm, Maik T1 - Vibration-based model updating: Reduction and quantification of uncertainties N2 - Numerical models and their combination with advanced solution strategies are standard tools for many engineering disciplines to design or redesign structures and to optimize designs with the purpose to improve specific requirements. As the successful application of numerical models depends on their suitability to represent the behavior related to the intended use, they should be validated by experimentally obtained results. If the discrepancy between numerically derived and experimentally obtained results is not acceptable, a model revision or a revision of the experiment need to be considered. Model revision is divided into two classes, the model updating and the basic revision of the numerical model. The presented thesis is related to a special branch of model updating, the vibration-based model updating. Vibration-based model updating is a tool to improve the correlation of the numerical model by adjusting uncertain model input parameters by means of results extracted from vibration tests. Evidently, uncertainties related to the experiment, the numerical model, or the applied numerical solving strategies can influence the correctness of the identified model input parameters. The reduction of uncertainties for two critical problems and the quantification of uncertainties related to the investigation of several nominally identical structures are the main emphases of this thesis. First, the reduction of uncertainties by optimizing reference sensor positions is considered. The presented approach relies on predicted power spectral amplitudes and an initial finite element model as a basis to define the assessment criterion for predefined sensor positions. In combination with geometry-based design variables, which represent the sensor positions, genetic and particle swarm optimization algorithms are applied. The applicability of the proposed approach is demonstrated on a numerical benchmark study of a simply supported beam and a case study of a real test specimen. Furthermore, the theory of determining the predicted power spectral amplitudes is validated with results from vibration tests. Second, the possibility to reduce uncertainties related to an inappropriate assignment for numerically derived and experimentally obtained modes is investigated. In the context of vibration-based model updating, the correct pairing is essential. The most common criterion for indicating corresponding mode shapes is the modal assurance criterion. Unfortunately, this criterion fails in certain cases and is not reliable for automatic approaches. Hence, an alternative criterion, the energy-based modal assurance criterion, is proposed. This criterion combines the mathematical characteristic of orthogonality with the physical properties of the structure by modal strain energies. A numerical example and a case study with experimental data are presented to show the advantages of the proposed energy-based modal assurance criterion in comparison to the traditional modal assurance criterion. Third, the application of optimization strategies combined with information theory based objective functions is analyzed for the purpose of stochastic model updating. This approach serves as an alternative to the common sensitivity-based stochastic model updating strategies. Their success depends strongly on the defined initial model input parameters. In contrast, approaches based on optimization strategies can be more flexible. It can be demonstrated, that the investigated nature inspired optimization strategies in combination with Bhattacharyya distance and Kullback-Leibler divergence are appropriate. The obtained accuracies and the respective computational effort are comparable with sensitivity-based stochastic model updating strategies. The application of model updating procedures to improve the quality and suitability of a numerical model is always related to additional costs. The presented innovative approaches will contribute to reduce and quantify uncertainties within a vibration-based model updating process. Therefore, the increased benefit can compensate the additional effort, which is necessary to apply model updating procedures. N2 - Eine typische Anwendung von numerischen Modellen und den damit verbundenen numerischen Lösungsstrategien ist das Entwerfen oder Ertüchtigen von Strukturen und das Optimieren von Entwürfen zur Verbesserung spezifischer Eigenschaften. Der erfolgreiche Einsatz von numerischen Modellen ist abhängig von der Eignung des Modells bezüglich der vorgesehenen Anwendung. Deshalb ist eine Validierung mit experimentellen Ergebnissen sinnvoll. Zeigt die Validierung inakzeptable Unterschiede zwischen den Ergebnissen des numerischen Modells und des Experiments, sollte das numerische Modell oder das experimentelle Vorgehen verbessert werden. Für die Modellverbesserung gibt es zwei verschiedene Möglichkeiten, zum einen die Kalibrierung des Modells und zum anderen die grundsätzliche Änderung von Modellannahmen. Die vorliegende Dissertation befasst sich mit der Kalibrierung von numerischen Modellen auf der Grundlage von Schwingungsversuchen. Modellkalibrierung ist eine Methode zur Verbesserung der Korrelation zwischen einem numerischen Modell und einer realen Struktur durch Anpassung von Modelleingangsparametern unter Verwendung von experimentell ermittelten Daten. Unsicherheiten bezüglich des numerischen Modells, des Experiments und der angewandten numerischen Lösungsstrategie beeinflussen entscheidend die erzielbare Qualität der identifizierten Modelleingangsparameter. Die Schwerpunkte dieser Dissertation sind die Reduzierung von Unsicherheiten für zwei kritische Probleme und die Quantifizierung von Unsicherheiten extrahiert aus Experimenten nominal gleicher Strukturen. Der erste Schwerpunkt beschäftigt sich mit der Reduzierung von Unsicherheiten durch die Optimierung von Referenzsensorpositionen. Das Bewertungskriterium für vordefinierte Sensorpositionen basiert auf einer theoretischen Abschätzung von Amplituden der Spektraldichtefunktion und einem dazugehörigen Finite Elemente Modell. Die Bestimmung der optimalen Konfiguration erfolgt durch eine Anwendung von Optimierungsmethoden basierend auf genetischen Algorithmen und Schwarmintelligenzen. Die Anwendbarkeit dieser Methoden wurde anhand einer numerischen Studie an einem einfach gelagerten Balken und einem real existierenden komplexen Versuchskörper nachgewiesen. Mit Hilfe einer experimentellen Untersuchung wird die Abschätzung der statistischen Eigenschaften der Antwortspektraldichtefunktionen an diesem Versuchskörper validiert. Im zweiten Schwerpunkt konzentrieren sich die Untersuchungen auf die Reduzierung von Unsicherheiten, hervorgerufen durch ungeeignete Kriterien zur Eigenschwingformzuordnung. Diese Zuordnung ist entscheidend für Modellkalibrierungen basierend auf Schwingungsversuchen. Das am Häufigsten verwendete Kriterium zur Zuordnung ist das modal assurance criterion. In manchen Anwendungsfällen ist dieses Kriterium jedoch kein zuverlässiger Indikator. Das entwickelte alternative Kriterium, das energy-based modal assurance criterion, kombiniert das mathematische Merkmal der Orthogonalität mit den physikalischen Eigenschaften der untersuchten Struktur mit Hilfe von modalen Formänderungsarbeiten. Ein numerisches Beispiel und eine Sensitivitätsstudie mit experimentellen Daten zeigen die Vorteile des vorgeschlagenen energiebasierten Kriteriums im Vergleich zum traditionellen modal assurance criterion. Die Anwendung von Optimierungsstrategien auf stochastische Modellkalibrierungsverfahren wird im dritten Schwerpunkt analysiert. Dabei werden Verschiedenheitsmaße der Informationstheorie zur Definition von Zielfunktionen herangezogen. Dieser Ansatz stellt eine Alternative zu herkömmlichen Verfahren dar, welche auf gradientenbasierten Sensitivitätsmatrizen zwischen Eingangs- und Ausgangsgrößen beruhen. Deren erfolgreicher Einsatz ist abhängig von den Anfangswerten der Eingangsgrößen, wobei die vorgeschlagenen Optimierungsstrategien weniger störanfällig sind. Der Bhattacharyya Abstand und die Kullback-Leibler Divergenz als Zielfunktion, kombiniert mit stochastischen Optimierungsverfahren, erwiesen sich als geeignet. Bei vergleichbarem Rechenaufwand konnten ähnliche Genauigkeiten wie bei den Modellkalibrierungsverfahren, die auf Sensitivitätsmatrizen basieren, erzielt werden. Die Anwendung von Modellkalibrierungsverfahren zur Verbesserung der Eignung eines numerischen Modells für einen bestimmten Zweck ist mit einem Mehraufwand verbunden. Die präsentierten innovativen Verfahren tragen zu einer Reduzierung und Quantifizierung von Unsicherheiten innerhalb eines Modellkalibrierungsprozesses basierend auf Schwingungsversuchen bei. Mit dem zusätzlich generierten Nutzen kann der Mehraufwand, der für eine Modellkalibrierung notwendig ist, nachvollziehbar begründet werden. T2 - Modellkalibrierung basierend auf Schwingungsversuchen: Reduzierung und Quantifizierung von Unsicherheiten T3 - ISM-Bericht // Institut für Strukturmechanik, Bauhaus-Universität Weimar - 2011,1 KW - Dynamik KW - Optimierung KW - Modellkalibrierung KW - Modezuordung KW - optimale Sensorpositionierung KW - model updating KW - mode pairing KW - optimal sensor positions KW - dissimilarity measures KW - optimization Y1 - 2011 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20110926-15553 ER - TY - JOUR A1 - Faizollahzadeh Ardabili, Sina A1 - Najafi, Bahman A1 - Alizamir, Meysam A1 - Mosavi, Amir A1 - Shamshirband, Shahaboddin A1 - Rabczuk, Timon T1 - Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters JF - Energies N2 - The production of a desired product needs an effective use of the experimental model. The present study proposes an extreme learning machine (ELM) and a support vector machine (SVM) integrated with the response surface methodology (RSM) to solve the complexity in optimization and prediction of the ethyl ester and methyl ester production process. The novel hybrid models of ELM-RSM and ELM-SVM are further used as a case study to estimate the yield of methyl and ethyl esters through a trans-esterification process from waste cooking oil (WCO) based on American Society for Testing and Materials (ASTM) standards. The results of the prediction phase were also compared with artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS), which were recently developed by the second author of this study. Based on the results, an ELM with a correlation coefficient of 0.9815 and 0.9863 for methyl and ethyl esters, respectively, had a high estimation capability compared with that for SVM, ANNs, and ANFIS. Accordingly, the maximum production yield was obtained in the case of using ELM-RSM of 96.86% for ethyl ester at a temperature of 68.48 °C, a catalyst value of 1.15 wt. %, mixing intensity of 650.07 rpm, and an alcohol to oil molar ratio (A/O) of 5.77; for methyl ester, the production yield was 98.46% at a temperature of 67.62 °C, a catalyst value of 1.1 wt. %, mixing intensity of 709.42 rpm, and an A/O of 6.09. Therefore, ELM-RSM increased the production yield by 3.6% for ethyl ester and 3.1% for methyl ester, compared with those for the experimental data. KW - Biodiesel KW - Optimierung KW - extreme learning machine KW - machine learning KW - response surface methodology KW - support vector machine KW - OA-Publikationsfonds2018 Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20181025-38170 UR - https://www.mdpi.com/1996-1073/11/11/2889 IS - 11, 2889 SP - 1 EP - 20 PB - MDPI CY - Basel ER - TY - GEN A1 - Nikulla, Susanne T1 - Untersuchung des dynamischen Verhaltens von Eisenbahnbrücken bei wechselnden Umweltbedingungen N2 - Im Zuge des Ausbaus von Eisenbahnstrecken für den Hochgeschwindigkeitsverkehr muss sichergestellt werden, dass keine Resonanz zwischen den periodisch einwirkenden Radlasten und den Brückeneigenfrequenzen entsteht. Bei der Untersuchung einzelner Bauwerke wurden teilweise recht große Schwankungen des dynamischen Verhaltens im Verlauf der Jahreszeiten festgestellt. Um diese Beobachtungen zu präzisieren, wurden an zwei ausgewählten Walzträger-in-Beton-Brücken über den Zeitraum von 15 Monaten Beschleunigungsmessungen durchgeführt. Die gewonnenen Daten wurden mit der Stochastic Subspace Methode, die im ersten Teil der Arbeit näher erläutert wird, ausgewertet. Es konnte für alle Eigenmoden ein Absinken der Eigenfrequenz bei steigender Temperatur beobachtet werden. Um die Ursachen hierfür genauer zu untersuchen, wurde für eine der beiden Brücken ein Finite-Elemente-Modell mit dem Programm SLang erstellt. Mittels einer Sensitivitätsanalyse wurden die für das Schwingverhalten maßgebenden Systemeigenschaften identifiziert. Die anschließend durchgeführte Strukturoptimierung unter Nutzung des genetischen Algorithmus sowie des adaptiven Antwortflächenverfahrens konnte die Temperaturabhängigkeit einzelner Materialparameter aufzeigen, die zumindest eine Ursache für Schwankungen der Eigenfrequenzen darstellen. KW - Dynamik KW - Systemidentifikation KW - Beschleunigungsmessung KW - Strukturoptimierung KW - Modalanalyse KW - Lufttemperatur KW - Zustandsraummodell KW - Stochastic Subspace Identification Y1 - 2008 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20081020-14324 ER - TY - CHAP A1 - Jaouadi, Zouhour A1 - Lahmer, Tom ED - Gürlebeck, Klaus ED - Lahmer, Tom T1 - Topology optimization of structures subjected to multiple load cases by introducing the Epsilon constraint method T2 - Digital Proceedings, International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering : July 20 - 22 2015, Bauhaus-University Weimar N2 - A topology optimization method has been developed for structures subjected to multiple load cases (Example of a bridge pier subjected to wind loads, traffic, superstructure...). We formulate the problem as a multi-criterial optimization problem, where the compliance is computed for each load case. Then, the Epsilon constraint method (method proposed by Chankong and Haimes, 1971) is adapted. The strategy of this method is based on the concept of minimizing the maximum compliance resulting from the critical load case while the other remaining compliances are considered in the constraints. In each iteration, the compliances of all load cases are computed and only the maximum one is minimized. The topology optimization process is switching from one load to another according to the variation of the resulting compliance. In this work we will motivate and explain the proposed methodology and provide some numerical examples. KW - Angewandte Informatik KW - Angewandte Mathematik KW - Building Information Modeling KW - Computerunterstütztes Verfahren KW - Data, information and knowledge modeling in civil engineering; Function theoretic methods and PDE in engineering sciences; Mathematical methods for (robotics and) computer vision; Numerical modeling in engineering; Optimization in engineering applications Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170314-28042 SN - 1611-4086 ER - TY - JOUR A1 - Alemu, Yohannes L. A1 - Habte, Bedilu A1 - Lahmer, Tom A1 - Urgessa, Girum T1 - Topologically preoptimized ground structure (TPOGS) for the optimization of 3D RC buildings JF - Asian Journal of Civil Engineering N2 - As an optimization that starts from a randomly selected structure generally does not guarantee reasonable optimality, the use of a systemic approach, named the ground structure, is widely accepted in steel-made truss and frame structural design. However, in the case of reinforced concrete (RC) structural optimization, because of the orthogonal orientation of structural members, randomly chosen or architect-sketched framing is used. Such a one-time fixed layout trend, in addition to its lack of a systemic approach, does not necessarily guarantee optimality. In this study, an approach for generating a candidate ground structure to be used for cost or weight minimization of 3D RC building structures with included slabs is developed. A multiobjective function at the floor optimization stage and a single objective function at the frame optimization stage are considered. A particle swarm optimization (PSO) method is employed for selecting the optimal ground structure. This method enables generating a simple, yet potential, real-world representation of topologically preoptimized ground structure while both structural and main architectural requirements are considered. This is supported by a case study for different floor domain sizes. KW - Bodenmechanik KW - Strukturanalyse KW - Optimierung KW - Stahlbetonkonstruktion KW - Dreidimensionales Modell KW - ground structure KW - TPOGS KW - topology optimization KW - 3D reinforced concrete buildings Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20230517-63677 UR - https://link.springer.com/article/10.1007/s42107-023-00640-2 VL - 2023 SP - 1 EP - 11 PB - Springer International Publishing CY - Cham ER - TY - THES A1 - Nickerson, Seth T1 - Thermo-Mechanical Behavior of Honeycomb, Porous, Microcracked Ceramics BT - Characterization and analysis of thermally induced stresses with specific consideration of synthetic, porous cordierite honeycomb substrates N2 - The underlying goal of this work is to reduce the uncertainty related to thermally induced stress prediction. This is accomplished by considering use of non-linear material behavior, notably path dependent thermal hysteresis behavior in the elastic properties. Primary novel factors of this work center on two aspects. 1. Broad material characterization and mechanistic material understanding, giving insight into why this class of material behaves in characteristic manners. 2. Development and implementation of a thermal hysteresis material model and its use to determine impact on overall macroscopic stress predictions. Results highlight microcracking evolution and behavior as the dominant mechanism for material property complexity in this class of materials. Additionally, it was found that for the cases studied, thermal hysteresis behavior impacts relevant peak stress predictions of a heavy-duty diesel particulate filter undergoing a drop-to-idle regeneration by less than ~15% for all conditions tested. It is also found that path independent heating curves may be utilized for a linear solution assumption to simplify analysis. This work brings forth a newly conceived concept of a 3 state, 4 path, thermally induced microcrack evolution process; demonstrates experimental behavior that is consistent with the proposed mechanisms, develops a mathematical framework that describes the process and quantifies the impact in a real world application space. T3 - ISM-Bericht // Institut für Strukturmechanik, Bauhaus-Universität Weimar - 2019,4 KW - Keramik KW - ceramics Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20190911-39753 ER - TY - JOUR A1 - Zhang, Chao A1 - Hao, Xiao-Li A1 - Wang, Cuixia A1 - Wei, Ning A1 - Rabczuk, Timon T1 - Thermal conductivity of graphene nanoribbons under shear deformation: A molecular dynamics simulation JF - Scientific Reports N2 - Tensile strain and compress strain can greatly affect the thermal conductivity of graphene nanoribbons (GNRs). However, the effect of GNRs under shear strain, which is also one of the main strain effect, has not been studied systematically yet. In this work, we employ reverse nonequilibrium molecular dynamics (RNEMD) to the systematical study of the thermal conductivity of GNRs (with model size of 4 nm × 15 nm) under the shear strain. Our studies show that the thermal conductivity of GNRs is not sensitive to the shear strain, and the thermal conductivity decreases only 12–16% before the pristine structure is broken. Furthermore, the phonon frequency and the change of the micro-structure of GNRs, such as band angel and bond length, are analyzed to explore the tendency of thermal conductivity. The results show that the main influence of shear strain is on the in-plane phonon density of states (PDOS), whose G band (higher frequency peaks) moved to the low frequency, thus the thermal conductivity is decreased. The unique thermal properties of GNRs under shear strains suggest their great potentials for graphene nanodevices and great potentials in the thermal managements and thermoelectric applications. KW - Wärmeleitfähigkeit KW - Graphen KW - Schubspannung Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170428-31718 ER - TY - JOUR A1 - Fathi, Sadegh A1 - Sajadzadeh, Hassan A1 - Mohammadi Sheshkal, Faezeh A1 - Aram, Farshid A1 - Pinter, Gergo A1 - Felde, Imre A1 - Mosavi, Amir T1 - The Role of Urban Morphology Design on Enhancing Physical Activity and Public Health JF - International Journal of Environmental Research and Public Health N2 - Along with environmental pollution, urban planning has been connected to public health. The research indicates that the quality of built environments plays an important role in reducing mental disorders and overall health. The structure and shape of the city are considered as one of the factors influencing happiness and health in urban communities and the type of the daily activities of citizens. The aim of this study was to promote physical activity in the main structure of the city via urban design in a way that the main form and morphology of the city can encourage citizens to move around and have physical activity within the city. Functional, physical, cultural-social, and perceptual-visual features are regarded as the most important and effective criteria in increasing physical activities in urban spaces, based on literature review. The environmental quality of urban spaces and their role in the physical activities of citizens in urban spaces were assessed by using the questionnaire tool and analytical network process (ANP) of structural equation modeling. Further, the space syntax method was utilized to evaluate the role of the spatial integration of urban spaces on improving physical activities. Based on the results, consideration of functional diversity, spatial flexibility and integration, security, and the aesthetic and visual quality of urban spaces plays an important role in improving the physical health of citizens in urban spaces. Further, more physical activities, including motivation for walking and the sense of public health and happiness, were observed in the streets having higher linkage and space syntax indexes with their surrounding texture. KW - Morphologie KW - Gesundheitswesen KW - Intelligente Stadt KW - Nachhaltigkeit KW - Gesundheitsinformationssystem KW - urban morphology KW - public health KW - physical activities KW - health KW - public space KW - urban health KW - smart cities KW - sustainability Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200402-41225 UR - https://www.mdpi.com/1660-4601/17/7/2359 VL - 2020 IS - Volume 17, Issue 7, 2359 PB - MDPI CY - Basel ER - TY - JOUR A1 - Işık, Ercan A1 - Büyüksaraç, Aydın A1 - Levent Ekinci, Yunus A1 - Aydın, Mehmet Cihan A1 - Harirchian, Ehsan T1 - The Effect of Site-Specific Design Spectrum on Earthquake-Building Parameters: A Case Study from the Marmara Region (NW Turkey) JF - Applied Sciences N2 - The Marmara Region (NW Turkey) has experienced significant earthquakes (M > 7.0) to date. A destructive earthquake is also expected in the region. To determine the effect of the specific design spectrum, eleven provinces located in the region were chosen according to the Turkey Earthquake Building Code updated in 2019. Additionally, the differences between the previous and updated regulations of the country were investigated. Peak Ground Acceleration (PGA) and Peak Ground Velocity (PGV) were obtained for each province by using earthquake ground motion levels with 2%, 10%, 50%, and 68% probability of exceedance in 50-year periods. The PGA values in the region range from 0.16 to 0.7 g for earthquakes with a return period of 475 years. For each province, a sample of a reinforced-concrete building having two different numbers of stories with the same ground and structural characteristics was chosen. Static adaptive pushover analyses were performed for the sample reinforced-concrete building using each province’s design spectrum. The variations in the earthquake and structural parameters were investigated according to different geographical locations. It was determined that the site-specific design spectrum significantly influences target displacements for performance-based assessments of buildings due to seismicity characteristics of the studied geographic location. KW - Erdbeben KW - earthquake KW - site-specific spectrum KW - Marmara Region KW - seismic hazard analysis KW - adaptive pushover KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20201022-42758 UR - https://www.mdpi.com/2076-3417/10/20/7247 VL - 2020 IS - Volume 10, issue 20, article 7247 PB - MDPI CY - Basel ER - TY - JOUR A1 - Ghazvinei, Pezhman Taherei A1 - Darvishi, Hossein Hassanpour A1 - Mosavi, Amir A1 - Yusof, Khamaruzaman bin Wan A1 - Alizamir, Meysam A1 - Shamshirband, Shahaboddin A1 - Chau, Kwok-Wing T1 - Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network JF - Engineering Applications of Computational Fluid Mechanics N2 - Management strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and an approach based on whole systems analysis is essential to bring about beneficial change to industry and the community. The application of this approach to water management, environmental management and cane supply management is outlined, where the literature indicates that the application of extreme learning machine (ELM) has never been explored in this realm. Consequently, the leading objective of the current research was set to filling this gap by applying ELM to launch swift and accurate model for crop production data-driven. The key learning has been the need for innovation both in the technical aspects of system function underpinned by modelling of sugarcane growth. Therefore, the current study is an attempt to establish an integrate model using ELM to predict the concluding growth amount of sugarcane. Prediction results were evaluated and further compared with artificial neural network (ANN) and genetic programming models. Accuracy of the ELM model is calculated using the statistics indicators of Root Means Square Error (RMSE), Pearson Coefficient (r), and Coefficient of Determination (R2) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalization ability in addition to faster learning curve. Thus, proficiency of the ELM for supplementary work on advancement of prediction model for sugarcane growth was approved with promising results. KW - Künstliche Intelligenz KW - Sustainable production KW - ELM KW - prediction KW - machine learning KW - sugarcane KW - estimation KW - growth mode KW - extreme learning machine KW - OA-Publikationsfonds2018 Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20181017-38129 UR - https://www.tandfonline.com/doi/full/10.1080/19942060.2018.1526119 VL - 2018 IS - 12,1 SP - 738 EP - 749 PB - Taylor & Francis ER - TY - THES A1 - Mai, Luu T1 - Structural Control Systems in High-speed Railway Bridges N2 - Structural vibration control of high-speed railway bridges using tuned mass dampers, semi-active tuned mass dampers, fluid viscous dampers and magnetorheological dampers to reduce resonant structural vibrations is studied. In this work, the addressed main issues include modeling of the dynamic interaction of the structures, optimization of the parameters of the dampers and comparison of their efficiency. A new approach to optimize multiple tuned mass damper systems on an uncertain model is proposed based on the H-infinity optimization criteria and the DK iteration procedure with norm-bounded uncertainties in frequency domain. The parameters of tuned mass dampers are optimized directly and simultaneously on different modes contributing significantly to the multi-resonant peaks to explore the different possible combinations of parameters. The effectiveness of the present method is also evaluated through comparison with a previous method. In the case of semi-active tuned mass dampers, an optimization algorithm is derived to control the magnetorheological damper in these semi-active damping systems. The use of the proposed algorithm can generate various combinations of control gains and state variables. This can lead to the improvement of the ability of MR dampers to track the desired control forces. An uncertain model to reduce detuning effects is also considered in this work. Next, for fluid viscous dampers, in order to tune the optimal parameters of fluid viscous dampers to the vicinity of the exact values, analytical formulae which can include structural damping are developed based on the perturbation method. The proposed formulae can also be considered as an improvement of the previous analytical formulae, especially for bridge beams with large structural damping. Finally, a new combination of magnetorheological dampers and a double-beam system to improve the performance of the primary structure vibration is proposed. An algorithm to control magnetorheological dampers in this system is developed by using standard linear matrix inequality techniques. Weight functions as a loop shaping procedure are also introduced in the feedback controllers to improve the tracking ability of magnetorheological damping forces. To this end, the effectiveness of magnetorheological dampers controlled by the proposed scheme, along with the effects of the uncertain and time-delay parameters on the models, are evaluated through numerical simulations. Additionally, a comparison of the dampers based on their performance is also considered in this work. T3 - ISM-Bericht // Institut für Strukturmechanik, Bauhaus-Universität Weimar - 2014,3 KW - High-speed railway bridge KW - Control system KW - Passive damper KW - Semi-active damper KW - Railway bridges Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20141223-23391 SN - 1610-7381 ER - TY - THES A1 - Khademi Zahedi, Reza T1 - Stress Distribution in Buried Defective PE Pipes and Crack Propagation in Nanosheets N2 - Buried PE pipelines are the main choice for transporting hazardous hydrocarbon fluids and are used in urban gas distribution networks. Molecular dynamics (MD) simulations used to investigate material behavior at nanoscale. KW - Gasleitung KW - gas pipes KW - Riss KW - Defekt KW - defects KW - nanosheets KW - crack KW - maximum stress Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210803-44814 ER - TY - THES A1 - Vu, Bac Nam T1 - Stochastic uncertainty quantification for multiscale modeling of polymeric nanocomposites N2 - Nanostructured materials are extensively applied in many fields of material science for new industrial applications, particularly in the automotive, aerospace industry due to their exceptional physical and mechanical properties. Experimental testing of nanomaterials is expensive, timeconsuming,challenging and sometimes unfeasible. Therefore,computational simulations have been employed as alternative method to predict macroscopic material properties. The behavior of polymeric nanocomposites (PNCs) are highly complex. The origins of macroscopic material properties reside in the properties and interactions taking place on finer scales. It is therefore essential to use multiscale modeling strategy to properly account for all large length and time scales associated with these material systems, which across many orders of magnitude. Numerous multiscale models of PNCs have been established, however, most of them connect only two scales. There are a few multiscale models for PNCs bridging four length scales (nano-, micro-, meso- and macro-scales). In addition, nanomaterials are stochastic in nature and the prediction of macroscopic mechanical properties are influenced by many factors such as fine-scale features. The predicted mechanical properties obtained by traditional approaches significantly deviate from the measured values in experiments due to neglecting uncertainty of material features. This discrepancy is indicated that the effective macroscopic properties of materials are highly sensitive to various sources of uncertainty, such as loading and boundary conditions and material characteristics, etc., while very few stochastic multiscale models for PNCs have been developed. Therefore, it is essential to construct PNC models within the framework of stochastic modeling and quantify the stochastic effect of the input parameters on the macroscopic mechanical properties of those materials. This study aims to develop computational models at four length scales (nano-, micro-, meso- and macro-scales) and hierarchical upscaling approaches bridging length scales from nano- to macro-scales. A framework for uncertainty quantification (UQ) applied to predict the mechanical properties of the PNCs in dependence of material features at different scales is studied. Sensitivity and uncertainty analysis are of great helps in quantifying the effect of input parameters, considering both main and interaction effects, on the mechanical properties of the PNCs. To achieve this major goal, the following tasks are carried out: At nano-scale, molecular dynamics (MD) were used to investigate deformation mechanism of glassy amorphous polyethylene (PE) in dependence of temperature and strain rate. Steered molecular dynamics (SMD)were also employed to investigate interfacial characteristic of the PNCs. At mico-scale, we developed an atomistic-based continuum model represented by a representative volume element (RVE) in which the SWNT’s properties and the SWNT/polymer interphase are modeled at nano-scale, the surrounding polymer matrix is modeled by solid elements. Then, a two-parameter model was employed at meso-scale. A hierarchical multiscale approach has been developed to obtain the structure-property relations at one length scale and transfer the effect to the higher length scales. In particular, we homogenized the RVE into an equivalent fiber. The equivalent fiber was then employed in a micromechanical analysis (i.e. Mori-Tanaka model) to predict the effective macroscopic properties of the PNC. Furthermore, an averaging homogenization process was also used to obtain the effective stiffness of the PCN at meso-scale. Stochastic modeling and uncertainty quantification consist of the following ingredients: - Simple random sampling, Latin hypercube sampling, Sobol’ quasirandom sequences, Iman and Conover’s method (inducing correlation in Latin hypercube sampling) are employed to generate independent and dependent sample data, respectively. - Surrogate models, such as polynomial regression, moving least squares (MLS), hybrid method combining polynomial regression and MLS, Kriging regression, and penalized spline regression, are employed as an approximation of a mechanical model. The advantage of the surrogate models is the high computational efficiency and robust as they can be constructed from a limited amount of available data. - Global sensitivity analysis (SA) methods, such as variance-based methods for models with independent and dependent input parameters, Fourier-based techniques for performing variance-based methods and partial derivatives, elementary effects in the context of local SA, are used to quantify the effects of input parameters and their interactions on the mechanical properties of the PNCs. A bootstrap technique is used to assess the robustness of the global SA methods with respect to their performance. In addition, the probability distribution of mechanical properties are determined by using the probability plot method. The upper and lower bounds of the predicted Young’s modulus according to 95 % prediction intervals were provided. The above-mentioned methods study on the behaviour of intact materials. Novel numerical methods such as a node-based smoothed extended finite element method (NS-XFEM) and an edge-based smoothed phantom node method (ES-Phantom node) were developed for fracture problems. These methods can be used to account for crack at macro-scale for future works. The predicted mechanical properties were validated and verified. They show good agreement with previous experimental and simulations results. KW - Polymere KW - nanocomposite KW - Nanoverbundstruktur KW - stochastic KW - multiscale Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20160322-25551 ER - TY - THES A1 - Ghasemi, Hamid T1 - Stochastic optimization of fiber reinforced composites considering uncertainties N2 - Briefly, the two basic questions that this research is supposed to answer are: 1. Howmuch fiber is needed and how fibers should be distributed through a fiber reinforced composite (FRC) structure in order to obtain the optimal and reliable structural response? 2. How do uncertainties influence the optimization results and reliability of the structure? Giving answer to the above questions a double stage sequential optimization algorithm for finding the optimal content of short fiber reinforcements and their distribution in the composite structure, considering uncertain design parameters, is presented. In the first stage, the optimal amount of short fibers in a FRC structure with uniformly distributed fibers is conducted in the framework of a Reliability Based Design Optimization (RBDO) problem. Presented model considers material, structural and modeling uncertainties. In the second stage, the fiber distribution optimization (with the aim to further increase in structural reliability) is performed by defining a fiber distribution function through a Non-Uniform Rational BSpline (NURBS) surface. The advantages of using the NURBS surface as a fiber distribution function include: using the same data set for the optimization and analysis; high convergence rate due to the smoothness of the NURBS; mesh independency of the optimal layout; no need for any post processing technique and its non-heuristic nature. The output of stage 1 (the optimal fiber content for homogeneously distributed fibers) is considered as the input of stage 2. The output of stage 2 is the Reliability Index (b ) of the structure with the optimal fiber content and distribution. First order reliability method (in order to approximate the limit state function) as well as different material models including Rule of Mixtures, Mori-Tanaka, energy-based approach and stochastic multi-scales are implemented in different examples. The proposed combined model is able to capture the role of available uncertainties in FRC structures through a computationally efficient algorithm using all sequential, NURBS and sensitivity based techniques. The methodology is successfully implemented for interfacial shear stress optimization in sandwich beams and also for optimization of the internal cooling channels in a ceramic matrix composite. Finally, after some changes and modifications by combining Isogeometric Analysis, level set and point wise density mapping techniques, the computational framework is extended for topology optimization of piezoelectric / flexoelectric materials. T3 - ISM-Bericht // Institut für Strukturmechanik, Bauhaus-Universität Weimar - 2016,1 KW - Optimization KW - Fiber Reinforced Composite KW - Finite Element Method KW - Isogeometric Analysis KW - Flexoelectricity KW - Finite-Elemente-Methode KW - Optimierung Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20161117-27042 ER - TY - THES A1 - Liu, Bokai T1 - Stochastic multiscale modeling of polymeric nanocomposites using Data-driven techniques N2 - In recent years, lightweight materials, such as polymer composite materials (PNCs) have been studied and developed due to their excellent physical and chemical properties. Structures composed of these composite materials are widely used in aerospace engineering structures, automotive components, and electrical devices. The excellent and outstanding mechanical, thermal, and electrical properties of Carbon nanotube (CNT) make it an ideal filler to strengthen polymer materials’ comparable properties. The heat transfer of composite materials has very promising engineering applications in many fields, especially in electronic devices and energy storage equipment. It is essential in high-energy density systems since electronic components need heat dissipation functionality. Or in other words, in electronic devices the generated heat should ideally be dissipated by light and small heat sinks. Polymeric composites consist of fillers embedded in a polymer matrix, the first ones will significantly affect the overall (macroscopic) performance of the material. There are many common carbon-based fillers such as single-walled carbon nanotubes (SWCNT), multi-walled carbon nanotubes (MWCNT), carbon nanobuds (CNB), fullerene, and graphene. Additives inside the matrix have become a popular subject for researchers. Some extraordinary characters, such as high-performance load, lightweight design, excellent chemical resistance, easy processing, and heat transfer, make the design of polymeric nanotube composites (PNCs) flexible. Due to the reinforcing effects with different fillers on composite materials, it has a higher degree of freedom and can be designed for the structure according to specific applications’ needs. As already stated, our research focus will be on SWCNT enhanced PNCs. Since experiments are timeconsuming, sometimes expensive and cannot shed light into phenomena taking place for instance at the interfaces/interphases of composites, they are often complemented through theoretical and computational analysis. While most studies are based on deterministic approaches, there is a comparatively lower number of stochastic methods accounting for uncertainties in the input parameters. In deterministic models, the output of the model is fully determined by the parameter values and the initial conditions. However, uncertainties in the input parameters such as aspect ratio, volume fraction, thermal properties of fiber and matrix need to be taken into account for reliable predictions. In this research, a stochastic multiscale method is provided to study the influence of numerous uncertain input parameters on the thermal conductivity of the composite. Therefore, a hierarchical multi-scale method based on computational homogenization is presented in to predict the macroscopic thermal conductivity based on the fine-scale structure. In order to study the inner mechanism, we use the finite element method and employ surrogate models to conduct a Global Sensitivity Analysis (GSA). The SA is performed in order to quantify the influence of the conductivity of the fiber, matrix, Kapitza resistance, volume fraction and aspect ratio on the macroscopic conductivity. Therefore, we compute first-order and total-effect sensitivity indices with different surrogate models. As stochastic multiscale models are computational expensive, surrogate approaches are commonly exploited. With the emergence of high performance computing and artificial intelligence, machine learning has become a popular modeling tool for numerous applications. Machine learning (ML) is commonly used in regression and maps data through specific rules with algorithms to build input and output models. They are particularly useful for nonlinear input-output relationships when sufficient data is available. ML has also been used in the design of new materials and multiscale analysis. For instance, Artificial neural networks and integrated learning seem to be ideally for such a task. They can theoretically simulate any non-linear relationship through the connection of neurons. Mapping relationships are employed to carry out data-driven simulations of inputs and outputs in stochastic modeling. This research aims to develop a stochastic multi-scale computational models of PNCs in heat transfer. Multi-scale stochastic modeling with uncertainty analysis and machine learning methods consist of the following components: -Uncertainty Analysis. A surrogate based global sensitivity analysis is coupled with a hierarchical multi-scale method employing computational homogenization. The effect of the conductivity of the fibers and the matrix, the Kapitza resistance, volume fraction and aspect ratio on the ’macroscopic’ conductivity of the composite is systematically studied. All selected surrogate models yield consistently the conclusions that the most influential input parameters are the aspect ratio followed by the volume fraction. The Kapitza Resistance has no significant effect on the thermal conductivity of the PNCs. The most accurate surrogate model in terms of the R2 value is the moving least square (MLS). -Hybrid Machine Learning Algorithms. A combination of artificial neural network (ANN) and particle swarm optimization (PSO) is applied to estimate the relationship between variable input and output parameters. The ANN is used for modeling the composite while PSO improves the prediction performance through an optimized global minimum search. The thermal conductivity of the fibers and the matrix, the kapitza resistance, volume fraction and aspect ratio are selected as input parameters. The output is the macroscopic (homogenized) thermal conductivity of the composite. The results show that the PSO significantly improves the predictive ability of this hybrid intelligent algorithm, which outperforms traditional neural networks. -Stochastic Integrated Machine Learning. A stochastic integrated machine learning based multiscale approach for the prediction of the macroscopic thermal conductivity in PNCs is developed. Seven types of machine learning models are exploited in this research, namely Multivariate Adaptive Regression Splines (MARS), Support Vector Machine (SVM), Regression Tree (RT), Bagging Tree (Bag), Random Forest (RF), Gradient Boosting Machine (GBM) and Cubist. They are used as components of stochastic modeling to construct the relationship between the variable of the inputs’ uncertainty and the macroscopic thermal conductivity of PNCs. Particle Swarm Optimization (PSO) is used for hyper-parameter tuning to find the global optimal values leading to a significant reduction in the computational cost. The advantages and disadvantages of various methods are also analyzed in terms of computing time and model complexity to finally give a recommendation for the applicability of different models. T3 - ISM-Bericht // Institut für Strukturmechanik, Bauhaus-Universität Weimar - 2022,3 KW - Polymere KW - Nanoverbundstruktur KW - multiscale KW - nanocomposite KW - stochastic KW - Data-driven Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220503-46379 ER - TY - JOUR A1 - Rabczuk, Timon A1 - Guo, Hongwei A1 - Zhuang, Xiaoying A1 - Chen, Pengwan A1 - Alajlan, Naif T1 - Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media JF - Engineering with Computers N2 - We present a stochastic deep collocation method (DCM) based on neural architecture search (NAS) and transfer learning for heterogeneous porous media. We first carry out a sensitivity analysis to determine the key hyper-parameters of the network to reduce the search space and subsequently employ hyper-parameter optimization to finally obtain the parameter values. The presented NAS based DCM also saves the weights and biases of the most favorable architectures, which is then used in the fine-tuning process. We also employ transfer learning techniques to drastically reduce the computational cost. The presented DCM is then applied to the stochastic analysis of heterogeneous porous material. Therefore, a three dimensional stochastic flow model is built providing a benchmark to the simulation of groundwater flow in highly heterogeneous aquifers. The performance of the presented NAS based DCM is verified in different dimensions using the method of manufactured solutions. We show that it significantly outperforms finite difference methods in both accuracy and computational cost. KW - Maschinelles Lernen KW - Neuronales Lernen KW - Fehlerabschätzung KW - deep learning KW - neural architecture search KW - randomized spectral representation Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220209-45835 UR - https://link.springer.com/article/10.1007/s00366-021-01586-2 VL - 2022 SP - 1 EP - 26 PB - Springer CY - London ER - TY - THES A1 - Almasi, Ashkan T1 - Stochastic Analysis of Interfacial Effects on the Polymeric Nanocomposites N2 - The polymeric clay nanocomposites are a new class of materials of which recently have become the centre of attention due to their superior mechanical and physical properties. Several studies have been performed on the mechanical characterisation of these nanocomposites; however most of those studies have neglected the effect of the interfacial region between the clays and the matrix despite of its significant influence on the mechanical performance of the nanocomposites. There are different analytical methods to calculate the overall elastic material properties of the composites. In this study we use the Mori-Tanaka method to determine the overall stiffness of the composites for simple inclusion geometries of cylinder and sphere. Furthermore, the effect of interphase layer on the overall properties of composites is calculated. Here, we intend to get ounds for the effective mechanical properties to compare with the analytical results. Hence, we use linear displacement boundary conditions (LD) and uniform traction boundary conditions (UT) accordingly. Finally, the analytical results are compared with numerical results and they are in a good agreement. The next focus of this dissertation is a computational approach with a hierarchical multiscale method on the mesoscopic level. In other words, in this study we use the stochastic analysis and computational homogenization method to analyse the effect of thickness and stiffness of the interfacial region on the overall elastic properties of the clay/epoxy nanocomposites. The results show that the increase in interphase thickness, reduces the stiffness of the clay/epoxy naocomposites and this decrease becomes significant in higher clay contents. The results of the sensitivity analysis prove that the stiffness of the interphase layer has more significant effect on the final stiffness of nanocomposites. We also validate the results with the available experimental results from the literature which show good agreement. KW - Homogenization KW - Multiscale modeling KW - Nanocomposite materials KW - Stochastic analysis Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20150709-24339 ER - TY - JOUR A1 - Dehghani, Majid A1 - Salehi, Somayeh A1 - Mosavi, Amir A1 - Nabipour, Narjes A1 - Shamshirband, Shahaboddin A1 - Ghamisi, Pedram T1 - Spatial Analysis of Seasonal Precipitation over Iran: Co-Variation with Climate Indices JF - ISPRS, International Journal of Geo-Information N2 - Temporary changes in precipitation may lead to sustained and severe drought or massive floods in different parts of the world. Knowing the variation in precipitation can effectively help the water resources decision-makers in water resources management. Large-scale circulation drivers have a considerable impact on precipitation in different parts of the world. In this research, the impact of El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) on seasonal precipitation over Iran was investigated. For this purpose, 103 synoptic stations with at least 30 years of data were utilized. The Spearman correlation coefficient between the indices in the previous 12 months with seasonal precipitation was calculated, and the meaningful correlations were extracted. Then, the month in which each of these indices has the highest correlation with seasonal precipitation was determined. Finally, the overall amount of increase or decrease in seasonal precipitation due to each of these indices was calculated. Results indicate the Southern Oscillation Index (SOI), NAO, and PDO have the most impact on seasonal precipitation, respectively. Additionally, these indices have the highest impact on the precipitation in winter, autumn, spring, and summer, respectively. SOI has a diverse impact on winter precipitation compared to the PDO and NAO, while in the other seasons, each index has its special impact on seasonal precipitation. Generally, all indices in different phases may decrease the seasonal precipitation up to 100%. However, the seasonal precipitation may increase more than 100% in different seasons due to the impact of these indices. The results of this study can be used effectively in water resources management and especially in dam operation. KW - Maschinelles Lernen KW - Machine learning KW - spatiotemporal database KW - spatial analysis KW - seasonal precipitation KW - spearman correlation coefficient Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200128-40740 UR - https://www.mdpi.com/2220-9964/9/2/73 VL - 2020 IS - Volume 9, Issue 2, 73 PB - MDPI ER -