@phdthesis{Vu,
  author    = {Vu, Bac Nam},
  title     = {Stochastic uncertainty quantification for multiscale modeling of polymeric nanocomposites},
  doi       = {10.25643/bauhaus-universitaet.2555},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20160322-25551},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  pages     = {265},
  abstract  = {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.},
  subject      = {Polymere},
  language  = {en}
}
@phdthesis{Abeltshauser,
  author    = {Abeltshauser, Rainer},
  title     = {Identification and separation of physical effects of coupled systems by using defined model abstractions},
  doi       = {10.25643/bauhaus-universitaet.2860},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170314-28600},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  abstract  = {The thesis investigates at the computer aided simulation process for operational vibration analysis of complex coupled systems. As part of the internal methods project "Absolute Values" of the BMW Group, the thesis deals with the analysis of the structural dynamic interactions and excitation interactions. The overarching aim of the methods project is to predict the operational vibrations of engines. Simulations are usually used to analyze technical aspects (e. g. operational vibrations, strength, ...) of single components in the industrial development. The boundary conditions of submodels are mostly based on experiences. So the interactions with neighboring components and systems are neglected. To get physically more realistic results but still efficient simulations, this work wants to support the engineer during the preprocessing phase by useful criteria. At first suitable abstraction levels based on the existing literature are defined to identify structural dynamic interactions and excitation interactions of coupled systems. So it is possible to separate different effects of the coupled subsystems. On this basis, criteria are derived to assess the influence of interactions between the considered systems. These criteria can be used during the preprocessing phase and help the engineer to build up efficient models with respect to the interactions with neighboring systems. The method was developed by using several models with different complexity levels. Furthermore, the method is proved for the application in the industrial environment by using the example of a current combustion engine.},
  subject      = {Strukturdynamik},
  language  = {en}
}
@phdthesis{Schwedler,
  author    = {Schwedler, Michael},
  title     = {Integrated structural analysis using isogeometric finite element methods},
  doi       = {10.25643/bauhaus-universitaet.2737},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170130-27372},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  pages     = {209},
  abstract  = {The gradual digitization in the architecture, engineering, and construction industry over the past fifty years led to an extremely heterogeneous software environment, which today is embodied by the multitude of different digital tools and proprietary data formats used by the many specialists contributing to the design process in a construction project. Though these projects become increasingly complex, the demands on financial efficiency and the completion within a tight schedule grow at the same time. The digital collaboration of project partners has been identified as one key issue in successfully dealing with these challenges. Yet currently, the numerous software applications and their respective individual views on the design process severely impede that collaboration. An approach to establish a unified basis for the digital collaboration, regardless of the existing software heterogeneity, is a comprehensive digital building model contributed to by all projects partners. This type of data management known as building information modeling (BIM) has many benefits, yet its adoption is associated with many difficulties and thus, proceeds only slowly. One aspect in the field of conflicting requirements on such a digital model is the cooperation of architects and structural engineers. Traditionally, these two disciplines use different abstractions of reality for their models that in consequence lead to incompatible digital representations thereof. The onset of isogeometric analysis (IGA) promised to ease the discrepancy in design and analysis model representations. Yet, that initial focus quickly shifted towards using these methods as a more powerful basis for numerical simulations. Furthermore, the isogeometric representation alone is not capable of solving the model abstraction problem. It is thus the intention of this work to contribute to an improved digital collaboration of architects and engineers by exploring an integrated analysis approach on the basis of an unified digital model and solid geometry expressed by splines. In the course of this work, an analysis framework is developed that utilizes such models to automatically conduct numerical simulations commonly required in construction projects. In essence, this allows to retrieve structural analysis results from BIM models in a fast and simple manner, thereby facilitating rapid design iterations and profound design feedback. The BIM implementation Industry Foundation Classes (IFC) is reviewed with regard to its capabilities of representing the unified model. The current IFC schema strongly supports the use of redundant model data, a major pitfall in digital collaboration. Additionally, it does not allow to describe the geometry by volumetric splines. As the pursued approach builds upon a unique model for both, architectural and structural design, and furthermore requires solid geometry, necessary schema modifications are suggested. Structural entities are modeled by volumetric NURBS patches, each of which constitutes an individual subdomain that, with regard to the analysis, is incompatible with the remaining full model. The resulting consequences for numerical simulation are elaborated in this work. The individual subdomains have to be weakly coupled, for which the mortar method is used. Different approaches to discretize the interface traction fields are implemented and their respective impact on the analysis results is evaluated. All necessary coupling conditions are automatically derived from the related geometry model. The weak coupling procedure leads to a linear system of equations in saddle point form, which, owed to the volumetric modeling, is large in size and, the associated coefficient matrix has, due to the use of higher degree basis functions, a high bandwidth. The peculiarities of the system require adapted solution methods that generally cause higher numerical costs than the standard procedures for symmetric, positive-definite systems do. Different methods to solve the specific system are investigated and an efficient parallel algorithm is finally proposed. When the structural analysis model is derived from the unified model in the BIM data, it does in general initially not meet the requirements on the discretization that are necessary to obtain sufficiently accurate analysis results. The consequently necessary patch refinements must be controlled automatically to allowfor an entirely automatic analysis procedure. For that purpose, an empirical refinement scheme based on the geometrical and possibly mechanical properties of the specific entities is proposed. The level of refinement may be selectively manipulated by the structural engineer in charge. Furthermore, a Zienkiewicz-Zhu type error estimator is adapted for the use with isogeometric analysis results. It is shown that also this estimator can be used to steer an adaptive refinement procedure.},
  subject      = {Finite-Elemente-Methode},
  language  = {en}
}
@phdthesis{Amiri,
  author    = {Amiri, Fatemeh},
  title     = {Computational modelling of fracture with local maximum entropy approximations},
  doi       = {10.25643/bauhaus-universitaet.2631},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20160719-26310},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  pages     = {130},
  abstract  = {The key objective of this research is to study fracture with a meshfree method, local maximum entropy approximations, and model fracture in thin shell structures with complex geometry and topology. This topic is of high relevance for real-world applications, for example in the automotive industry and in aerospace engineering. The shell structure can be described efficiently by meshless methods which are capable of describing complex shapes as a collection of points instead of a structured mesh. In order to find the appropriate numerical method to achieve this goal, the first part of the work was development of a method based on local maximum entropy (LME) shape functions together with enrichment functions used in partition of unity methods to discretize problems in linear elastic fracture mechanics. We obtain improved accuracy relative to the standard extended finite element method (XFEM) at a comparable computational cost. In addition, we keep the advantages of the LME shape functions,such as smoothness and non-negativity. We show numerically that optimal convergence (same as in FEM) for energy norm and stress intensity factors can be obtained through the use of geometric (fixed area) enrichment with no special treatment of the nodes near the crack such as blending or shifting. As extension of this method to three dimensional problems and complex thin shell structures with arbitrary crack growth is cumbersome, we developed a phase field model for fracture using LME. Phase field models provide a powerful tool to tackle moving interface problems, and have been extensively used in physics and materials science. Phase methods are gaining popularity in a wide set of applications in applied science and engineering, recently a second order phase field approximation for brittle fracture has gathered significant interest in computational fracture such that sharp cracks discontinuities are modeled by a diffusive crack. By minimizing the system energy with respect to the mechanical displacements and the phase-field, subject to an irreversibility condition to avoid crack healing, this model can describe crack nucleation, propagation, branching and merging. One of the main advantages of the phase field modeling of fractures is the unified treatment of the interfacial tracking and mechanics, which potentially leads to simple, robust, scalable computer codes applicable to complex systems. In other words, this approximation reduces considerably the implementation complexity because the numerical tracking of the fracture is not needed, at the expense of a high computational cost. We present a fourth-order phase field model for fracture based on local maximum entropy (LME) approximations. The higher order continuity of the meshfree LME approximation allows to directly solve the fourth-order phase field equations without splitting the fourth-order differential equation into two second order differential equations. Notably, in contrast to previous discretizations that use at least a quadratic basis, only linear completeness is needed in the LME approximation. We show that the crack surface can be captured more accurately in the fourth-order model than the second-order model. Furthermore, less nodes are needed for the fourth-order model to resolve the crack path. Finally, we demonstrate the performance of the proposed meshfree fourth order phase-field formulation for 5 representative numerical examples. Computational results will be compared to analytical solutions within linear elastic fracture mechanics and experimental data for three-dimensional crack propagation. In the last part of this research, we present a phase-field model for fracture in Kirchoff-Love thin shells using the local maximum-entropy (LME) meshfree method. Since the crack is a natural outcome of the analysis it does not require an explicit representation and tracking, which is advantageous over techniques as the extended finite element method that requires tracking of the crack paths. The geometric description of the shell is based on statistical learning techniques that allow dealing with general point set surfaces avoiding a global parametrization, which can be applied to tackle surfaces of complex geometry and topology. We show the flexibility and robustness of the present methodology for two examples: plate in tension and a set of open connected pipes.},
  language  = {en}
}
@phdthesis{Harirchian,
  author    = {Harirchian, Ehsan},
  title     = {Improved Rapid Assessment of Earthquake Hazard Safety of Existing Buildings Using a Hierarchical Type-2 Fuzzy Logic Model},
  doi       = {10.25643/bauhaus-universitaet.4396},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210326-43963},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  pages     = {143},
  abstract  = {Although it is impractical to avert subsequent natural disasters, advances in simulation science and seismological studies make it possible to lessen the catastrophic damage. There currently exists in many urban areas a large number of structures, which are prone to damage by earthquakes. These were constructed without the guidance of a national seismic code, either before it existed or before it was enforced. For instance, in Istanbul, Turkey, as a high seismic area, around 90\% of buildings are substandard, which can be generalized into other earthquakeprone regions in Turkey. The reliability of this building stock resulting from earthquake-induced collapse is currently uncertain. Nonetheless, it is also not feasible to perform a detailed seismic vulnerability analysis on each building as a solution to the scenario, as it will be too complicated and expensive. This indicates the necessity of a reliable, rapid, and computationally easy method for seismic vulnerability assessment, commonly known as Rapid Visual Screening (RVS). In RVS methodology, an observational survey of buildings is performed, and according to the data collected during the visual inspection, a structural score is calculated without performing any structural calculations to determine the expected damage of a building and whether the building needs detailed assessment. Although this method might save time and resources due to the subjective/qualitative judgments of experts who performed the inspection, the evaluation process is dominated by vagueness and uncertainties, where the vagueness can be handled adequately through the fuzzy set theory but do not cover all sort of uncertainties due to its crisp membership functions. In this study, a novel method of rapid visual hazard safety assessment of buildings against earthquake is introduced in which an interval type-2 fuzzy logic system (IT2FLS) is used to cover uncertainties. In addition, the proposed method provides the possibility to evaluate the earthquake risk of the building by considering factors related to the building importance and exposure. A smartphone app prototype of the method has been introduced. For validation of the proposed method, two case studies have been selected, and the result of the analysis presents the robust efficiency of the proposed method.},
  subject      = {Fuzzy-Logik},
  language  = {en}
}
@article{AlaladeReichertKoehnetal.,
  author    = {Alalade, Muyiwa and Reichert, Ina and K{\"o}hn, Daniel and Wuttke, Frank and Lahmer, Tom},
  title     = {A Cyclic Multi-Stage Implementation of the Full-Waveform Inversion for the Identification of Anomalies in Dams},
  series = {Infrastructures},
  volume    = {2022},
  journal   = {Infrastructures},
  number    = {Volume 7, issue 12, article 161},
  editor    = {Qu, Chunxu and Gao, Chunxu and Zhang, Rui and Jia, Ziguang and Li, Jiaxiang},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/infrastructures7120161},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20221201-48396},
  pages     = {19},
  abstract  = {For the safe and efficient operation of dams, frequent monitoring and maintenance are required. These are usually expensive, time consuming, and cumbersome. To alleviate these issues, we propose applying a wave-based scheme for the location and quantification of damages in dams. To obtain high-resolution "interpretable" images of the damaged regions, we drew inspiration from non-linear full-multigrid methods for inverse problems and applied a new cyclic multi-stage full-waveform inversion (FWI) scheme. Our approach is less susceptible to the stability issues faced by the standard FWI scheme when dealing with ill-posed problems. In this paper, we first selected an optimal acquisition setup and then applied synthetic data to demonstrate the capability of our approach in identifying a series of anomalies in dams by a mixture of reflection and transmission tomography. The results had sufficient robustness, showing the prospects of application in the field of non-destructive testing of dams.},
  subject      = {Damm},
  language  = {en}
}
@article{ChowdhuryZabel,
  author    = {Chowdhury, Sharmistha and Zabel, Volkmar},
  title     = {Influence of loading sequence on wind induced fatigue assessment of bolts in TV-tower connection block},
  series = {Results in Engineering},
  volume    = {2022},
  journal   = {Results in Engineering},
  number    = {Volume 16, article 100603},
  publisher = {Elsevier},
  address   = {Amsterdam},
  doi       = {10.1016/j.rineng.2022.100603},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20221028-47303},
  pages     = {1 -- 18},
  abstract  = {Bolted connections are widely employed in structures like transmission poles, wind turbines, and television (TV) towers. The behaviour of bolted connections is often complex and plays a significant role in the overall dynamic characteristics of the structure. The goal of this work is to conduct a fatigue lifecycle assessment of such a bolted connection block of a 193 m tall TV tower, for which 205 days of real measurement data have been obtained from the installed monitoring devices. Based on the recorded data, the best-fit stochastic wind distribution for 50 years, the decisive wind action, and the locations to carry out the fatigue analysis have been decided. A 3D beam model of the entire tower is developed to extract the nodal forces corresponding to the connection block location under various mean wind speeds, which is later coupled with a detailed complex finite element model of the connection block, with over three million degrees of freedom, for acquiring stress histories on some pre-selected bolts. The random stress histories are analysed using the rainflow counting algorithm (RCA) and the damage is estimated using Palmgren-Miner's damage accumulation law. A modification is proposed to integrate the loading sequence effect into the RCA, which otherwise is ignored, and the differences between the two RCAs are investigated in terms of the accumulated damage.},
  subject      = {Schadensakkumulation},
  language  = {en}
}
@article{KumariHarirchianLahmeretal.,
  author    = {Kumari, Vandana and Harirchian, Ehsan and Lahmer, Tom and Rasulzade, Shahla},
  title     = {Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings},
  series = {Buildings},
  volume    = {2022},
  journal   = {Buildings},
  number    = {Volume 12, issue 5, article 578},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/buildings12050578},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20220509-46387},
  pages     = {1 -- 23},
  abstract  = {The seismic vulnerability assessment of existing reinforced concrete (RC) buildings is a significant source of disaster mitigation plans and rescue services. Different countries evolved various Rapid Visual Screening (RVS) techniques and methodologies to deal with the devastating consequences of earthquakes on the structural characteristics of buildings and human casualties. Artificial intelligence (AI) methods, such as machine learning (ML) algorithm-based methods, are increasingly used in various scientific and technical applications. The investigation toward using these techniques in civil engineering applications has shown encouraging results and reduced human intervention, including uncertainties and biased judgment. In this study, several known non-parametric algorithms are investigated toward RVS using a dataset employing different earthquakes. Moreover, the methodology encourages the possibility of examining the buildings' vulnerability based on the factors related to the buildings' importance and exposure. In addition, a web-based application built on Django is introduced. The interface is designed with the idea to ease the seismic vulnerability investigation in real-time. The concept was validated using two case studies, and the achieved results showed the proposed approach's potential efficiency},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{HarirchianKumariJadhavetal.,
  author    = {Harirchian, Ehsan and Kumari, Vandana and Jadhav, Kirti and Rasulzade, Shahla and Lahmer, Tom and Raj Das, Rohan},
  title     = {A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings},
  series = {Applied Sciences},
  volume    = {2021},
  journal   = {Applied Sciences},
  number    = {Volume 11, issue 16, article 7540},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/app11167540},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210818-44853},
  pages     = {1 -- 33},
  abstract  = {A vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life of people but also affects the socio-economic stability in the affected area. Therefore, it is necessary to assess such buildings' present vulnerability to make an educated decision regarding risk mitigation by seismic strengthening techniques such as retrofitting. However, it is economically and timely manner not feasible to inspect, repair, and augment every old building on an urban scale. As a result, a reliable rapid screening methods, namely Rapid Visual Screening (RVS), have garnered increasing interest among researchers and decision-makers alike. In this study, the effectiveness of five different Machine Learning (ML) techniques in vulnerability prediction applications have been investigated. The damage data of four different earthquakes from Ecuador, Haiti, Nepal, and South Korea, have been utilized to train and test the developed models. Eight performance modifiers have been implemented as variables with a supervised ML. The investigations on this paper illustrate that the assessed vulnerability classes by ML techniques were very close to the actual damage levels observed in the buildings.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{Hanna,
  author    = {Hanna, John},
  title     = {Computational Modelling for the Effects of Capsular Clustering on Fracture of Encapsulation-Based Self-Healing Concrete Using XFEM and Cohesive Surface Technique},
  series = {Applied Sciences},
  volume    = {2022},
  journal   = {Applied Sciences},
  number    = {Volume 12, issue 10, article 5112},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/app12105112},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20220721-46717},
  pages     = {1 -- 17},
  abstract  = {The fracture of microcapsules is an important issue to release the healing agent for healing the cracks in encapsulation-based self-healing concrete. The capsular clustering generated from the concrete mixing process is considered one of the critical factors in the fracture mechanism. Since there is a lack of studies in the literature regarding this issue, the design of self-healing concrete cannot be made without an appropriate modelling strategy. In this paper, the effects of microcapsule size and clustering on the fractured microcapsules are studied computationally. A simple 2D computational modelling approach is developed based on the eXtended Finite Element Method (XFEM) and cohesive surface technique. The proposed model shows that the microcapsule size and clustering have significant roles in governing the load-carrying capacity and the crack propagation pattern and determines whether the microcapsule will be fractured or debonded from the concrete matrix. The higher the microcapsule circumferential contact length, the higher the load-carrying capacity. When it is lower than 25\% of the microcapsule circumference, it will result in a greater possibility for the debonding of the microcapsule from the concrete. The greater the core/shell ratio (smaller shell thickness), the greater the likelihood of microcapsules being fractured.},
  subject      = {Beton},
  language  = {en}
}
@misc{Habtemariam,
  type      = {Master Thesis},
  author    = {Habtemariam, Abinet Kifle},
  title     = {Numerical Demolition Analysis of a Slender Guyed Antenna Mast},
  doi       = {10.25643/bauhaus-universitaet.4460},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210723-44609},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  pages     = {75},
  abstract  = {The main purpose of the thesis is to ensure the safe demolition of old guyed antenna masts that are located in different parts of Germany. The major problem in demolition of this masts is the falling down of the masts in unexpected direction because of buckling problem. The objective of this thesis is development of a numerical models using finite element method (FEM) and assuring a controlled collapse by coming up with different time setups for the detonation of explosives which are responsible for cutting down the cables. The result of this thesis will avoid unexpected outcomes during the demolition processes and prevent risk of collapsing of the mast over near by structures.},
  subject      = {Abbruch},
  language  = {en}
}
@phdthesis{Alkam,
  author    = {Alkam, Feras},
  title     = {Vibration-based Monitoring of Concrete Catenary Poles using Bayesian Inference},
  volume    = {2021},
  publisher = {Bauhaus-Universit{\"a}tsverlag},
  address   = {Weimar},
  doi       = {10.25643/bauhaus-universitaet.4433},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210526-44338},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  pages     = {177},
  abstract  = {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.},
  subject      = {Parameteridentifikation},
  language  = {en}
}
@article{LashkarAraKalantariSheikhKhozanietal.,
  author    = {Lashkar-Ara, Babak and Kalantari, Niloofar and Sheikh Khozani, Zohreh and Mosavi, Amir},
  title     = {Assessing Machine Learning versus a Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel},
  series = {Mathematics},
  volume    = {2021},
  journal   = {Mathematics},
  number    = {Volume 9, Issue 6, Article 596},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/math9060596},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210504-44197},
  pages     = {15},
  abstract  = {One of the most important subjects of hydraulic engineering is the reliable estimation of the transverse distribution in the rectangular channel of bed and wall shear stresses. This study makes use of the Tsallis entropy, genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) methods to assess the shear stress distribution (SSD) in the rectangular channel. To evaluate the results of the Tsallis entropy, GP and ANFIS models, laboratory observations were used in which shear stress was measured using an optimized Preston tube. This is then used to measure the SSD in various aspect ratios in the rectangular channel. To investigate the shear stress percentage, 10 data series with a total of 112 different data for were used. The results of the sensitivity analysis show that the most influential parameter for the SSD in smooth rectangular channel is the dimensionless parameter B/H, Where the transverse coordinate is B, and the flow depth is H. With the parameters (b/B), (B/H) for the bed and (z/H), (B/H) for the wall as inputs, the modeling of the GP was better than the other one. Based on the analysis, it can be concluded that the use of GP and ANFIS algorithms is more effective in estimating shear stress in smooth rectangular channels than the Tsallis entropy-based equations.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{HarirchianKumariJadhavetal.,
  author    = {Harirchian, Ehsan and Kumari, Vandana and Jadhav, Kirti and Raj Das, Rohan and Rasulzade, Shahla and Lahmer, Tom},
  title     = {A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings},
  series = {Applied Sciences},
  volume    = {2020},
  journal   = {Applied Sciences},
  number    = {Volume 10, issue 20, article 7153},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/app10207153},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20201022-42744},
  pages     = {18},
  abstract  = {Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure's performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building's behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings.},
  subject      = {Erdbeben},
  language  = {en}
}
@article{HarirchianJadhavMohammadetal.,
  author    = {Harirchian, Ehsan and Jadhav, Kirti and Mohammad, Kifaytullah and Aghakouchaki Hosseini, Seyed Ehsan and Lahmer, Tom},
  title     = {A Comparative Study of MCDM Methods Integrated with Rapid Visual Seismic Vulnerability Assessment of Existing RC Structures},
  series = {Applied Sciences},
  volume    = {2020},
  journal   = {Applied Sciences},
  number    = {Volume 10, issue 18, article 6411},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/app10186411},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200918-42360},
  pages     = {24},
  abstract  = {Recently, the demand for residence and usage of urban infrastructure has been increased, thereby resulting in the elevation of risk levels of human lives over natural calamities. The occupancy demand has rapidly increased the construction rate, whereas the inadequate design of structures prone to more vulnerability. Buildings constructed before the development of seismic codes have an additional susceptibility to earthquake vibrations. The structural collapse causes an economic loss as well as setbacks for human lives. An application of different theoretical methods to analyze the structural behavior is expensive and time-consuming. Therefore, introducing a rapid vulnerability assessment method to check structural performances is necessary for future developments. The process, as mentioned earlier, is known as Rapid Visual Screening (RVS). This technique has been generated to identify, inventory, and screen structures that are potentially hazardous. Sometimes, poor construction quality does not provide some of the required parameters; in this case, the RVS process turns into a tedious scenario. Hence, to tackle such a situation, multiple-criteria decision-making (MCDM) methods for the seismic vulnerability assessment opens a new gateway. The different parameters required by RVS can be taken in MCDM. MCDM evaluates multiple conflicting criteria in decision making in several fields. This paper has aimed to bridge the gap between RVS and MCDM. Furthermore, to define the correlation between these techniques, implementation of the methodologies from Indian, Turkish, and Federal Emergency Management Agency (FEMA) codes has been done. The effects of seismic vulnerability of structures have been observed and compared.},
  subject      = {Erdbebensicherheit},
  language  = {en}
}
@phdthesis{Oucif,
  author    = {Oucif, Chahmi},
  title     = {Analytical Modeling of Self-Healing and Super Healing in Cementitious Materials},
  doi       = {10.25643/bauhaus-universitaet.4229},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200831-42296},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  pages     = {208},
  abstract  = {Self-healing materials have recently become more popular due to their capability to autonomously and autogenously repair the damage in cementitious materials. The concept of self-healing gives the damaged material the ability to recover its stiffness. This gives a difference in comparing with a material that is not subjected to healing. Once this material is damaged, it cannot sustain loading due to the stiffness degradation. Numerical modeling of self-healing materials is still in its infancy. Multiple experimental researches were conducted in literature to describe the behavior of self-healing of cementitious materials. However, few numerical investigations were undertaken. The thesis presents an analytical framework of self-healing and super healing materials based on continuum damage-healing mechanics. Through this framework, we aim to describe the recovery and strengthening of material stiffness and strength. A simple damage healing law is proposed and applied on concrete material. The proposed damage-healing law is based on a new time-dependent healing variable. The damage-healing model is applied on isotropic concrete material at the macroscale under tensile load. Both autonomous and autogenous self-healing mechanisms are simulated under different loading conditions. These two mechanisms are denoted in the present work by coupled and uncoupled self-healing mechanisms, respectively. We assume in the coupled self-healing that the healing occurs at the same time with damage evolution, while we assume in the uncoupled self-healing that the healing occurs when the material is deformed and subjected to a rest period (damage is constant). In order to describe both coupled and uncoupled healing mechanisms, a one-dimensional element is subjected to different types of loading history. In the same context, derivation of nonlinear self-healing theory is given, and comparison of linear and nonlinear damage-healing models is carried out using both coupled and uncoupled self-healing mechanisms. The nonlinear healing theory includes generalized nonlinear and quadratic healing models. The healing efficiency is studied by varying the values of the healing rest period and the parameter describing the material characteristics. In addition, theoretical formulation of different self-healing variables is presented for both isotropic and anisotropic maerials. The healing variables are defined based on the recovery in elastic modulus, shear modulus, Poisson's ratio, and bulk modulus. The evolution of the healing variable calculated based on cross-section as function of the healing variable calculated based on elastic stiffness is presented in both hypotheses of elastic strain equivalence and elastic energy equivalence. The components of the fourth-rank healing tensor are also obtained in the case of isotropic elasticity, plane stress and plane strain. Recent research revealed that self-healing presents a crucial solution also for the strengthening of the materials. This new concept has been termed ``Super Healing``. Once the stiffness of the material is recovered, further healing can result as a strengthening material. In the present thesis, new theory of super healing materials is defined in isotropic and anisotropic cases using sound mathematical and mechanical principles which are applied in linear and nonlinear super healing theories. Additionally, the link of the proposed theory with the theory of undamageable materials is outlined. In order to describe the super healing efficiency in linear and nonlinear theories, the ratio of effective stress to nominal stress is calculated as function of the super healing variable. In addition, the hypotheses of elastic strain and elastic energy equivalence are applied. In the same context, new super healing matrix in plane strain is proposed based on continuum damage-healing mechanics. In the present work, we also focus on numerical modeling of impact behavior of reinforced concrete slabs using the commercial finite element package Abaqus/Explicit. Plain and reinforced concrete slabs of unconfined compressive strength 41 MPa are simulated under impact of ogive-nosed hard projectile. The constitutive material modeling of the concrete and steel reinforcement bars is performed using the Johnson-Holmquist-2 damage and the Johnson-Cook plasticity material models, respectively. Damage diameters and residual velocities obtained by the numerical model are compared with the experimental results and effect of steel reinforcement and projectile diameter is studied.},
  subject      = {Schaden},
  language  = {en}
}
@article{HarirchianLahmer,
  author    = {Harirchian, Ehsan and Lahmer, Tom},
  title     = {Improved Rapid Visual Earthquake Hazard Safety Evaluation of Existing Buildings Using a Type-2 Fuzzy Logic Model},
  series = {Applied Sciences},
  volume    = {2020},
  journal   = {Applied Sciences},
  number    = {Volume 10, Issue 3, 2375},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/app10072375},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200331-41161},
  pages     = {14},
  abstract  = {Rapid Visual Screening (RVS) is a procedure that estimates structural scores for buildings and prioritizes their retrofit and upgrade requirements. Despite the speed and simplicity of RVS, many of the collected parameters are non-commensurable and include subjectivity due to visual observations. This might cause uncertainties in the evaluation, which emphasizes the use of a fuzzy-based method. This study aims to propose a novel RVS methodology based on the interval type-2 fuzzy logic system (IT2FLS) to set the priority of vulnerable building to undergo detailed assessment while covering uncertainties and minimizing their effects during evaluation. The proposed method estimates the vulnerability of a building, in terms of Damage Index, considering the number of stories, age of building, plan irregularity, vertical irregularity, building quality, and peak ground velocity, as inputs with a single output variable. Applicability of the proposed method has been investigated using a post-earthquake damage database of reinforced concrete buildings from the Bing{\"o}l and D{\"u}zce earthquakes in Turkey.},
  subject      = {Fuzzy-Logik},
  language  = {en}
}
@phdthesis{RadmardRahmani,
  author    = {Radmard Rahmani, Hamid},
  title     = {Artificial Intelligence Approach for Seismic Control of Structures},
  doi       = {10.25643/bauhaus-universitaet.4135},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200417-41359},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  abstract  = {Abstract In the first part of this research, the utilization of tuned mass dampers in the vibration control of tall buildings during earthquake excitations is studied. The main issues such as optimizing the parameters of the dampers and studying the effects of frequency content of the target earthquakes are addressed. Abstract The non-dominated sorting genetic algorithm method is improved by upgrading generic operators, and is utilized to develop a framework for determining the optimum placement and parameters of dampers in tall buildings. A case study is presented in which the optimal placement and properties of dampers are determined for a model of a tall building under different earthquake excitations through computer simulations. Abstract In the second part, a novel framework for the brain learning-based intelligent seismic control of smart structures is developed. In this approach, a deep neural network learns how to improve structural responses during earthquake excitations using feedback control. Abstract Reinforcement learning method is improved and utilized to develop a framework for training the deep neural network as an intelligent controller. The efficiency of the developed framework is examined through two case studies including a single-degree-of-freedom system and a high-rise building under different earthquake excitation records. Abstract The results show that the controller gradually develops an optimum control policy to reduce the vibrations of a structure under an earthquake excitation through a cyclical process of actions and observations. Abstract It is shown that the controller efficiently improves the structural responses under new earthquake excitations for which it was not trained. Moreover, it is shown that the controller has a stable performance under uncertainties.},
  subject      = {Erdbeben},
  language  = {en}
}
@phdthesis{Rabizadeh,
  author    = {Rabizadeh, Ehsan},
  title     = {Goal-oriented A Posteriori Error Estimation and Adaptive Mesh Refinement in 2D/3D Thermoelasticity Problems},
  doi       = {10.25643/bauhaus-universitaet.4286},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20201113-42864},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  abstract  = {In recent years, substantial attention has been devoted to thermoelastic multifield problems and their numerical analysis. Thermoelasticity is one of the important categories of multifield problems which deals with the effect of mechanical and thermal disturbances on an elastic body. In other words, thermoelasticity encompasses the phenomena that describe the elastic and thermal behavior of solids and their interactions under thermo-mechanical loadings. Since providing an analytical solution for general coupled thermoelasticity problems is mathematically complicated, the development of alternative numerical solution techniques seems essential. Due to the nature of numerical analysis methods, presence of error in results is inevitable, therefore in any numerical simulation, the main concern is the accuracy of the approximation. There are different error estimation (EE) methods to assess the overall quality of numerical approximation. In many real-life numerical simulations, not only the overall error, but also the local error or error in a particular quantity of interest is of main interest. The error estimation techniques which are developed to evaluate the error in the quantity of interest are known as "goal-oriented" error estimation (GOEE) methods. This project, for the first time, investigates the classical a posteriori error estimation and goal-oriented a posteriori error estimation in 2D/3D thermoelasticity problems. Generally, the a posteriori error estimation techniques can be categorized into two major branches of recovery-based and residual-based error estimators. In this research, application of both recovery- and residual-based error estimators in thermoelasticity are studied. Moreover, in order to reduce the error in the quantity of interest efficiently and optimally in 2D and 3D thermoelastic problems, goal-oriented adaptive mesh refinement is performed. As the first application category, the error estimation in classical Thermoelasticity (CTE) is investigated. In the first step, a rh-adaptive thermo-mechanical formulation based on goal-oriented error estimation is proposed.The developed goal-oriented error estimation relies on different stress recovery techniques, i.e., the superconvergent patch recovery (SPR), L2-projection patch recovery (L2-PR), and weighted superconvergent patch recovery (WSPR). Moreover, a new adaptive refinement strategy (ARS) is presented that minimizes the error in a quantity of interest and refines the discretization such that the error is equally distributed in the refined mesh. The method is validated by numerous numerical examples where an analytical solution or reference solution is available. After investigating error estimation in classical thermoelasticity and evaluating the quality of presented error estimators, we extended the application of the developed goal-oriented error estimation and the associated adaptive refinement technique to the classical fully coupled dynamic thermoelasticity. In this part, we present an adaptive method for coupled dynamic thermoelasticity problems based on goal-oriented error estimation. We use dimensionless variables in the finite element formulation and for the time integration we employ the acceleration-based Newmark-_ method. In this part, the SPR, L2-PR, and WSPR recovery methods are exploited to estimate the error in the quantity of interest (QoI). By using adaptive refinement in space, the error in the quantity of interest is minimized. Therefore, the discretization is refined such that the error is equally distributed in the refined mesh. We demonstrate the efficiency of this method by numerous numerical examples. After studying the recovery-based error estimators, we investigated the residual-based error estimation in thermoelasticity. In the last part of this research, we present a 3D adaptive method for thermoelastic problems based on goal-oriented error estimation where the error is measured with respect to a pointwise quantity of interest. We developed a method for a posteriori error estimation and mesh adaptation based on dual weighted residual (DWR) method relying on the duality principles and consisting of an adjoint problem solution. Here, we consider the application of the derived estimator and mesh refinement to two-/three-dimensional (2D/3D) thermo-mechanical multifield problems. In this study, the goal is considered to be given by singular pointwise functions, such as the point value or point value derivative at a specific point of interest (PoI). An adaptive algorithm has been adopted to refine the mesh to minimize the goal in the quantity of interest. The mesh adaptivity procedure based on the DWR method is performed by adaptive local h-refinement/coarsening with allowed hanging nodes. According to the proposed DWR method, the error contribution of each element is evaluated. In the refinement process, the contribution of each element to the goal error is considered as the mesh refinement criterion. In this study, we substantiate the accuracy and performance of this method by several numerical examples with available analytical solutions. Here, 2D and 3D problems under thermo-mechanical loadings are considered as benchmark problems. To show how accurately the derived estimator captures the exact error in the evaluation of the pointwise quantity of interest, in all examples, considering the analytical solutions, the goal error effectivity index as a standard measure of the quality of an estimator is calculated. Moreover, in order to demonstrate the efficiency of the proposed method and show the optimal behavior of the employed refinement method, the results of different conventional error estimators and refinement techniques (e.g., global uniform refinement, Kelly, and weighted Kelly techniques) are used for comparison.},
  subject      = {Mesh Refinement},
  language  = {en}
}
@article{IşıkBueyueksaracLeventEkincietal.,
  author    = {I{\c{s}}{\i}k, Ercan and B{\"u}y{\"u}ksara{\c{c}}, Ayd{\i}n and Levent Ekinci, Yunus and Ayd{\i}n, Mehmet Cihan and Harirchian, Ehsan},
  title     = {The Effect of Site-Specific Design Spectrum on Earthquake-Building Parameters: A Case Study from the Marmara Region (NW Turkey)},
  series = {Applied Sciences},
  volume    = {2020},
  journal   = {Applied Sciences},
  number    = {Volume 10, issue 20, article 7247},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/app10207247},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20201022-42758},
  pages     = {23},
  abstract  = {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.},
  subject      = {Erdbeben},
  language  = {en}
}
@unpublished{KhosraviSheikhKhozaniCooper,
  author    = {Khosravi, Khabat and Sheikh Khozani, Zohreh and Cooper, James R.},
  title     = {Predicting stable gravel-bed river hydraulic geometry: A test of novel, advanced, hybrid data mining algorithms},
  volume    = {2021},
  doi       = {10.25643/bauhaus-universitaet.4499},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20211004-44998},
  abstract  = {Accurate prediction of stable alluvial hydraulic geometry, in which erosion and sedimentation are in equilibrium, is one of the most difficult but critical topics in the field of river engineering. Data mining algorithms have been gaining more attention in this field due to their high performance and flexibility. However, an understanding of the potential for these algorithms to provide fast, cheap, and accurate predictions of hydraulic geometry is lacking. This study provides the first quantification of this potential. Using at-a-station field data, predictions of flow depth, water-surface width and longitudinal water surface slope are made using three standalone data mining techniques -, Instance-based Learning (IBK), KStar, Locally Weighted Learning (LWL) - along with four types of novel hybrid algorithms in which the standalone models are trained with Vote, Attribute Selected Classifier (ASC), Regression by Discretization (RBD), and Cross-validation Parameter Selection (CVPS) algorithms (Vote-IBK, Vote-Kstar, Vote-LWL, ASC-IBK, ASC-Kstar, ASC-LWL, RBD-IBK, RBD-Kstar, RBD-LWL, CVPSIBK, CVPS-Kstar, CVPS-LWL). Through a comparison of their predictive performance and a sensitivity analysis of the driving variables, the results reveal: (1) Shield stress was the most effective parameter in the prediction of all geometry dimensions; (2) hybrid models had a higher prediction power than standalone data mining models, empirical equations and traditional machine learning algorithms; (3) Vote-Kstar model had the highest performance in predicting depth and width, and ASC-Kstar in estimating slope, each providing very good prediction performance. Through these algorithms, the hydraulic geometry of any river can potentially be predicted accurately and with ease using just a few, readily available flow and channel parameters. Thus, the results reveal that these models have great potential for use in stable channel design in data poor catchments, especially in developing nations where technical modelling skills and understanding of the hydraulic and sediment processes occurring in the river system may be lacking.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@unpublished{AbbasKavrakovMorgenthaletal.,
  author    = {Abbas, Tajammal and Kavrakov, Igor and Morgenthal, Guido and Lahmer, Tom},
  title     = {Prediction of aeroelastic response of bridge decks using artificial neural networks},
  doi       = {10.25643/bauhaus-universitaet.4097},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200225-40974},
  abstract  = {The assessment of wind-induced vibrations is considered vital for the design of long-span bridges. The aim of this research is to develop a methodological framework for robust and efficient prediction strategies for complex aerodynamic phenomena using hybrid models that employ numerical analyses as well as meta-models. Here, an approach to predict motion-induced aerodynamic forces is developed using artificial neural network (ANN). The ANN is implemented in the classical formulation and trained with a comprehensive dataset which is obtained from computational fluid dynamics forced vibration simulations. The input to the ANN is the response time histories of a bridge section, whereas the output is the motion-induced forces. The developed ANN has been tested for training and test data of different cross section geometries which provide promising predictions. The prediction is also performed for an ambient response input with multiple frequencies. Moreover, the trained ANN for aerodynamic forcing is coupled with the structural model to perform fully-coupled fluid--structure interaction analysis to determine the aeroelastic instability limit. The sensitivity of the ANN parameters to the model prediction quality and the efficiency has also been highlighted. The proposed methodology has wide application in the analysis and design of long-span bridges.},
  subject      = {Aerodynamik},
  language  = {en}
}
@unpublished{SheikhKhozaniKumbhakar,
  author    = {Sheikh Khozani, Zohreh and Kumbhakar, Manotosh},
  title     = {Discussion of "Estimation of one-dimensional velocity distribution by measuring velocity at two points" by Yeganeh and Heidari (2020)},
  doi       = {10.25643/bauhaus-universitaet.4366},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210216-43663},
  abstract  = {The concept of information entropy together with the principle of maximum entropy to open channel flow is essentially based on some physical consideration of the problem under consideration. This paper is a discussion on Yeganeh and Heidari (2020)'s paper, who proposed a new approach for measuring vertical distribution of streamwise velocity in open channels. The discussers argue that their approach is conceptually incorrect and thus leads to a physically unrealistic situation. In addition, the discussers found some wrong mathematical expressions (which are assumed to be typos) written in the paper, and also point out that the authors did not cite some of the original papers on the topic.},
  subject      = {Geschwindigkeit},
  language  = {en}
}
@unpublished{KhosraviSheikhKhozaniMao,
  author    = {Khosravi, Khabat and Sheikh Khozani, Zohreh and Mao, Luka},
  title     = {A comparison between advanced hybrid machine learning algorithms and empirical equations applied to abutment scour depth prediction},
  doi       = {10.25643/bauhaus-universitaet.4388},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210311-43889},
  pages     = {43},
  abstract  = {Complex vortex flow patterns around bridge piers, especially during floods, cause scour process that can result in the failure of foundations. Abutment scour is a complex three-dimensional phenomenon that is difficult to predict especially with traditional formulas obtained using empirical approaches such as regressions. This paper presents a test of a standalone Kstar model with five novel hybrid algorithm of bagging (BA-Kstar), dagging (DA-Kstar), random committee (RC-Kstar), random subspace (RS-Kstar), and weighted instance handler wrapper (WIHWKstar) to predict scour depth (ds) for clear water condition. The dataset consists of 99 scour depth data from flume experiments (Dey and Barbhuiya, 2005) using abutment shapes such as vertical, semicircular and 45◦ wing. Four dimensionless parameter of relative flow depth (h/l), excess abutment Froude number (Fe), relative sediment size (d50/l) and relative submergence (d50/h) were considered for the prediction of relative scour depth (ds/l). A portion of the dataset was used for the calibration (70\%), and the remaining used for model validation. Pearson correlation coefficients helped deciding relevance of the input parameters combination and finally four different combinations of input parameters were used. The performance of the models was assessed visually and with quantitative metrics. Overall, the best input combination for vertical abutment shape is the combination of Fe, d50/l and h/l, while for semicircular and 45◦ wing the combination of the Fe and d50/l is the most effective input parameter combination. Our results show that incorporating Fe, d50/l and h/l lead to higher performance while involving d50/h reduced the models prediction power for vertical abutment shape and for semicircular and 45◦ wing involving h/l and d50/h lead to more error. The WIHW-Kstar provided the highest performance in scour depth prediction around vertical abutment shape while RC-Kstar model outperform of other models for scour depth prediction around semicircular and 45◦ wing.},
  subject      = {maschinelles Lernen},
  language  = {en}
}
@misc{Udrea,
  type      = {Master Thesis},
  author    = {Udrea, Mihai-Andrei},
  title     = {Assessment of Data from Dynamic Bridge Monitoring},
  doi       = {10.25643/bauhaus-universitaet.2174},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20140429-21742},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  abstract  = {The focus of the thesis is to process measurements acquired from a continuous monitoring system at a railway bridge. Temperature, strain and ambient vibration records are analysed and two main directions of investigation are pursued. The first and the most demanding task is to develop processing routines able to extract modal parameters from ambient vibration measurements. For this purpose, reliable experimental models are achieved on the basis of a stochastic system identification(SSI) procedure. A fully automated algorithm based on a three-stage clustering is implemented to perform a modal parameter estimation for every single measurement. After selecting a baseline of modal parameters, the evolution of eigenfrequencies is studied and correlated to environmental and operational factors. The second aspect deals with the structural response to passing trains. Corresponding triggered records of strain and temperature are processed and their assessment is accomplished using the average strains induced by each train as the reference parameter. Three influences due to speed, temperature and loads are distinguished and treated individually. An attempt to estimate the maximum response variation due to each factor is also carried out.},
  subject      = {Messtechnik},
  language  = {en}
}
@misc{Zafar,
  type      = {Master Thesis},
  author    = {Zafar, Usman},
  title     = {Probabilistic Reliability Analysis of Wind Turbines},
  doi       = {10.25643/bauhaus-universitaet.3977},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20240507-39773},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  abstract  = {Renewable energy use is on the rise and these alternative resources of energy can help combat with the climate change. Around 80\% of the world's electricity comes from coal and petroleum however, the renewables are the fastest growing source of energy in the world. Solar, wind, hydro, geothermal and biogas are the most common forms of renewable energy. Among them, wind energy is emerging as a reliable and large-scaled source of power production. The recent research and confidence in the performance has led to the construction of more and bigger wind turbines around the world. As wind turbines are getting bigger, a concern regarding their safety is also in discussion. Wind turbines are expensive machinery to construct and the enormous capital investment is one of the main reasons, why many countries are unable to adopt to the wind energy. Generally, a reliable wind turbine will result in better performance and assist in minimizing the cost of operation. If a wind turbine fails, it's a loss of investment and can be harmful for the surrounding habitat. This thesis aims towards estimating the reliability of an offshore wind turbine. A model of Jacket type offshore wind turbine is prepared by using finite element software package ABAQUS and is compared with the structural failure criteria of the wind turbine tower. UQLab, which is a general uncertainty quantification framework developed at ETH Z{\"u}rich, is used for the reliability analysis. Several probabilistic methods are included in the framework of UQLab, which include Monte Carlo, First Order Reliability Analysis and Adaptive Kriging Monte Carlo simulation. This reliability study is performed only for the structural failure of the wind turbine but it can be extended to many other forms of failures e.g. reliability for power production, or reliability for different component failures etc. It's a useful tool that can be utilized to estimate the reliability of future wind turbines, that could result in more safer and better performance of wind turbines.},
  subject      = {Windturbine},
  language  = {en}
}
@unpublished{RadmardRahmaniKoenke,
  author    = {Radmard Rahmani, Hamid and K{\"o}nke, Carsten},
  title     = {Passive Control of Tall Buildings Using Distributed Multiple Tuned Mass Dampers},
  doi       = {10.25643/bauhaus-universitaet.3859},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20190311-38597},
  pages     = {43},
  abstract  = {The vibration control of the tall building during earthquake excitations is a challenging task due to their complex seismic behavior. This paper investigates the optimum placement and properties of the Tuned Mass Dampers (TMDs) in tall buildings, which are employed to control the vibrations during earthquakes. An algorithm was developed to spend a limited mass either in a single TMD or in multiple TMDs and distribute them optimally over the height of the building. The Non-dominated Sorting Genetic Algorithm (NSGA - II) method was improved by adding multi-variant genetic operators and utilized to simultaneously study the optimum design parameters of the TMDs and the optimum placement. The results showed that under earthquake excitations with noticeable amplitude in higher modes, distributing TMDs over the height of the building is more effective in mitigating the vibrations compared to the use of a single TMD system. From the optimization, it was observed that the locations of the TMDs were related to the stories corresponding to the maximum modal displacements in the lower modes and the stories corresponding to the maximum modal displacements in the modes which were highly activated by the earthquake excitations. It was also noted that the frequency content of the earthquake has significant influence on the optimum location of the TMDs.},
  subject      = {Schwingungsd{\"a}mpfer},
  language  = {en}
}
@phdthesis{Liu,
  author    = {Liu, Bokai},
  title     = {Stochastic multiscale modeling of polymeric nanocomposites using Data-driven techniques},
  doi       = {10.25643/bauhaus-universitaet.4637},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20220503-46379},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  pages     = {134},
  abstract  = {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.},
  subject      = {Polymere},
  language  = {en}
}