@phdthesis{Goswami,
  author    = {Goswami, Somdatta},
  title     = {Phase field modeling of fracture with isogeometric analysis and machine learning methods},
  doi       = {10.25643/bauhaus-universitaet.4384},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210304-43841},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  pages     = {168},
  abstract  = {This thesis presents the advances and applications of phase field modeling in fracture analysis. In this approach, the sharp crack surface topology in a solid is approximated by a diffusive crack zone governed by a scalar auxiliary variable. The uniqueness of phase field modeling is that the crack paths are automatically determined as part of the solution and no interface tracking is required. The damage parameter varies continuously over the domain. But this flexibility comes with associated difficulties: (1) a very fine spatial discretization is required to represent sharp local gradients correctly; (2) fine discretization results in high computational cost; (3) computation of higher-order derivatives for improved convergence rates and (4) curse of dimensionality in conventional numerical integration techniques. As a consequence, the practical applicability of phase field models is severely limited. The research presented in this thesis addresses the difficulties of the conventional numerical integration techniques for phase field modeling in quasi-static brittle fracture analysis. The first method relies on polynomial splines over hierarchical T-meshes (PHT-splines) in the framework of isogeometric analysis (IGA). An adaptive h-refinement scheme is developed based on the variational energy formulation of phase field modeling. The fourth-order phase field model provides increased regularity in the exact solution of the phase field equation and improved convergence rates for numerical solutions on a coarser discretization, compared to the second-order model. However, second-order derivatives of the phase field are required in the fourth-order model. Hence, at least a minimum of C1 continuous basis functions are essential, which is achieved using hierarchical cubic B-splines in IGA. PHT-splines enable the refinement to remain local at singularities and high gradients, consequently reducing the computational cost greatly. Unfortunately, when modeling complex geometries, multiple parameter spaces (patches) are joined together to describe the physical domain and there is typically a loss of continuity at the patch boundaries. This decrease of smoothness is dictated by the geometry description, where C0 parameterizations are normally used to deal with kinks and corners in the domain. Hence, the application of the fourth-order model is severely restricted. To overcome the high computational cost for the second-order model, we develop a dual-mesh adaptive h-refinement approach. This approach uses a coarser discretization for the elastic field and a finer discretization for the phase field. Independent refinement strategies have been used for each field. The next contribution is based on physics informed deep neural networks. The network is trained based on the minimization of the variational energy of the system described by general non-linear partial differential equations while respecting any given law of physics, hence the name physics informed neural network (PINN). The developed approach needs only a set of points to define the geometry, contrary to the conventional mesh-based discretization techniques. The concept of `transfer learning' is integrated with the developed PINN approach to improve the computational efficiency of the network at each displacement step. This approach allows a numerically stable crack growth even with larger displacement steps. An adaptive h-refinement scheme based on the generation of more quadrature points in the damage zone is developed in this framework. For all the developed methods, displacement-controlled loading is considered. The accuracy and the efficiency of both methods are studied numerically showing that the developed methods are powerful and computationally efficient tools for accurately predicting fractures.},
  subject      = {Phasenfeldmodell},
  language  = {en}
}
@phdthesis{Bianco,
  author    = {Bianco, Marcelo Jos{\´e}},
  title     = {Coupling between Shell and Generalized Beam Theory (GBT) elements},
  doi       = {10.25643/bauhaus-universitaet.4391},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210315-43914},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  pages     = {265},
  abstract  = {In the last decades, Finite Element Method has become the main method in statics and dynamics analysis in engineering practice. For current problems, this method provides a faster, more flexible solution than the analytic approach. Prognoses of complex engineer problems that used to be almost impossible to solve are now feasible. Although the finite element method is a robust tool, it leads to new questions about engineering solutions. Among these new problems, it is possible to divide into two major groups: the first group is regarding computer performance; the second one is related to understanding the digital solution. Simultaneously with the development of the finite element method for numerical solutions, a theory between beam theory and shell theory was developed: Generalized Beam Theory, GBT. This theory has not only a systematic and analytical clear presentation of complicated structural problems, but also a compact and elegant calculation approach that can improve computer performance. Regrettably, GBT was not internationally known since the most publications of this theory were written in German, especially in the first years. Only in recent years, GBT has gradually become a fertile research topic, with developments from linear to non-linear analysis. Another reason for the misuse of GBT is the isolated application of the theory. Although recently researches apply finite element method to solve the GBT's problems numerically, the coupling between finite elements of GBT and other theories (shell, solid, etc) is not the subject of previous research. Thus, the main goal of this dissertation is the coupling between GBT and shell/membrane elements. Consequently, one achieves the benefits of both sides: the versatility of shell elements with the high performance of GBT elements. Based on the assumptions of GBT, this dissertation presents how the separation of variables leads to two calculation's domains of a beam structure: a cross-section modal analysis and the longitudinal amplification axis. Therefore, there is the possibility of applying the finite element method not only in the cross-section analysis, but also the development for an exact GBT's finite element in the longitudinal direction. For the cross-section analysis, this dissertation presents the solution of the quadratic eigenvalue problem with an original separation between plate and membrane mechanism. Subsequently, one obtains a clearer representation of the deformation mode, as well as a reduced quadratic eigenvalue problem. Concerning the longitudinal direction, this dissertation develops the novel exact elements, based on hyperbolic and trigonometric shape functions. Although these functions do not have trivial expressions, they provide a recursive procedure that allows periodic derivatives to systematise the development of stiffness matrices. Also, these shape functions enable a single-element discretisation of the beam structure and ensure a smooth stress field. From these developments, this dissertation achieves the formulation of its primary objective: the connection of GBT and shell elements in a mixed model. Based on the displacement field, it is possible to define the coupling equations applied in the master-slave method. Therefore, one can model the structural connections and joints with finite shell elements and the structural beams and columns with GBT finite element. As a side effect, the coupling equations limit the displacement field of the shell elements under the assumptions of GBT, in particular in the neighbourhood of the coupling cross-section. Although these side effects are almost unnoticeable in linear analysis, they lead to cumulative errors in non-linear analysis. Therefore, this thesis finishes with the evaluation of the mixed GBT-shell models in non-linear analysis.},
  subject      = {Biegetheorie},
  language  = {en}
}
@phdthesis{Bielik,
  author    = {Bielik, Martin},
  title     = {FORM-ACTIVITY-MOVEMENT INTERACTION MODEL},
  doi       = {10.25643/bauhaus-universitaet.4397},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210407-43970},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  pages     = {269},
  abstract  = {This dissertation investigates the interactions between urban form, allocation of activities, and pedestrian movement in the context of urban planning. The ability to assess the long-term impact of urban planning decisions on what people do and how they get there is of central importance, with various disciplines addressing this topic. This study focuses on approaches proposed by urban morphologists, urban economists, and transportation planners, each aiming the attention at a different part of the form-activity-movement interaction. Even though there is no doubt about the advantages of these highly focused approaches, it remains unclear what is the cost of ignoring the effect of some interactions while considering others. The general aim of this dissertation is to empirically test the validity of the individual models and quantify the impact of this isolationist approach on their precision and bias. For this purpose, we propose a joined form-activity-movement interaction model and conduct an empirical study in Weimar, Germany. We estimate how the urban form and activities affect movement as well as how movement and urban form affect activities. By estimating these effects in isolation and simultaneously, we assess the bias of the individual models. On the one hand, the empirical study results confirm the significance of all interactions suggested by the individual models. On the other hand, we were able to show that when these interactions are estimated in isolation, the resulting predictions are biased. To conclude, we do not question the knowledge brought by transportation planners, urban morphologists, and urban economists. However, we argue that it might be of little use on its own. We see the relevance of this study as being twofold. On the one hand, we proposed a novel methodological framework for the simultaneous estimation of the form-activity-movement interactions. On the other hand, we provide empirical evidence about the strengths and limitations of current approaches.},
  subject      = {Stadtplanung},
  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}
}
@phdthesis{Sproete,
  author    = {Spr{\"o}te, Daniel},
  title     = {Immobilienportfoliomanagement {\"o}ffentlicher musealer Schl{\"o}sserverwaltungen - Daten zur Investitionssteuerung},
  doi       = {10.25643/bauhaus-universitaet.4426},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210510-44260},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  pages     = {220},
  abstract  = {Die Arbeit leistet einen wissenschaftlichen Beitrag zur Erforschung der Einsatzm{\"o}glichkeiten eines Immobilienportfoliomanagements f{\"u}r {\"o}ffentliche museale Schl{\"o}sserverwaltungen in Deutschland. Insbesondere wird ein f{\"u}r deren Organisation spezifisches Modell zur Investitionssteuerung herausgearbeitet und dessen Anwendbarkeit in der Praxis mit Experten diskutiert.},
  subject      = {Unbewegliche Sache},
  language  = {de}
}
@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{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}
}