@article{SchuchKaps,
  author    = {Schuch, Kai and Kaps, Christian},
  title     = {Maturation and Structure Formation Processes in Binders with Aqueous Alkali-Silicate Solutions},
  doi       = {10.25643/bauhaus-universitaet.3597},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170907-35979},
  pages     = {1 -- 16},
  abstract  = {Maturation and structure formation processes can lead to crack formation in silicate and aluminosilicate binders (e.g. for coating materials...) through restricted deformation, loss of strength and thus to loss of durability. These processes are evaluated with silicate materials with an outlook on aluminosilicate binders.},
  subject      = {Waterglass},
  language  = {en}
}
@article{Zhang,
  author    = {Zhang, Yongzheng},
  title     = {Nonlocal dynamic Kirchhoff plate formulation based on nonlocal operator method},
  series = {Engineering with Computers},
  volume    = {2022},
  journal   = {Engineering with Computers},
  publisher = {Springer},
  address   = {London},
  doi       = {10.1007/s00366-021-01587-1},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20220209-45849},
  pages     = {1 -- 35},
  abstract  = {In this study, we propose a nonlocal operator method (NOM) for the dynamic analysis of (thin) Kirchhoff plates. The nonlocal Hessian operator is derived based on a second-order Taylor series expansion. The NOM does not require any shape functions and associated derivatives as 'classical' approaches such as FEM, drastically facilitating the implementation. Furthermore, NOM is higher order continuous, which is exploited for thin plate analysis that requires C1 continuity. The nonlocal dynamic governing formulation and operator energy functional for Kirchhoff plates are derived from a variational principle. The Verlet-velocity algorithm is used for the time discretization. After confirming the accuracy of the nonlocal Hessian operator, several numerical examples are simulated by the nonlocal dynamic Kirchhoff plate formulation.},
  subject      = {Angewandte Mathematik},
  language  = {en}
}
@article{RabczukGuoZhuangetal.,
  author    = {Rabczuk, Timon and Guo, Hongwei and Zhuang, Xiaoying and Chen, Pengwan and Alajlan, Naif},
  title     = {Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media},
  series = {Engineering with Computers},
  volume    = {2022},
  journal   = {Engineering with Computers},
  publisher = {Springer},
  address   = {London},
  doi       = {10.1007/s00366-021-01586-2},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20220209-45835},
  pages     = {1 -- 26},
  abstract  = {We present a stochastic deep collocation method (DCM) based on neural architecture search (NAS) and transfer learning for heterogeneous porous media. We first carry out a sensitivity analysis to determine the key hyper-parameters of the network to reduce the search space and subsequently employ hyper-parameter optimization to finally obtain the parameter values. The presented NAS based DCM also saves the weights and biases of the most favorable architectures, which is then used in the fine-tuning process. We also employ transfer learning techniques to drastically reduce the computational cost. The presented DCM is then applied to the stochastic analysis of heterogeneous porous material. Therefore, a three dimensional stochastic flow model is built providing a benchmark to the simulation of groundwater flow in highly heterogeneous aquifers. The performance of the presented NAS based DCM is verified in different dimensions using the method of manufactured solutions. We show that it significantly outperforms finite difference methods in both accuracy and computational cost.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{KapsSchuchStaeblein,
  author    = {Kaps, Christian and Schuch, Kai and St{\"a}blein, Stefan},
  title     = {Silicate coatings for concrete components with waterglass systems by means of neutral salt initiation},
  doi       = {10.25643/bauhaus-universitaet.2588},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20160601-25888},
  pages     = {1 -- 14},
  abstract  = {The objective of the investigations was the proof of the use of the neutral salt initiation as a construction material in the protecting silicate coating of concrete components, e.g. factory finished parts or reinforced concrete construction parts, by means of waterglass fused silica suspensions},
  subject      = {Silicate},
  language  = {en}
}
@article{Lahmer,
  author    = {Lahmer, Tom},
  title     = {FEM-Based determination of real and complex elastic, dielectric, and piezoelectric moduli in piezoceramic materials},
  series = {IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control},
  journal   = {IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control},
  doi       = {10.25643/bauhaus-universitaet.3608},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20171030-36083},
  abstract  = {We propose an enhanced iterative scheme for the precise reconstruction of piezoelectric material parameters from electric impedance and mechanical displacement measurements. It is based on finite-element simulations of the full three-dimensional piezoelectric equations, combined with an inexact Newton or nonlinear Landweber iterative inversion scheme. We apply our method to two piezoelectric materials and test its performance. For the first material, the manufacturer provides a full data set; for the second one, no material data set is available. For both cases, our inverse scheme, using electric impedance measurements as input data, performs well.},
  subject      = {Finite-Elemente-Methode},
  language  = {en}
}
@article{AlYasiriMutasharGuerlebecketal.,
  author    = {Al-Yasiri, Zainab Riyadh Shaker and Mutashar, Hayder Majid and G{\"u}rlebeck, Klaus and Lahmer, Tom},
  title     = {Damage Sensitive Signals for the Assessment of the Conditions of Wind Turbine Rotor Blades Using Electromagnetic Waves},
  series = {Infrastructures},
  volume    = {2022},
  journal   = {Infrastructures},
  number    = {Volume 7, Issue 8 (August 2022), article 104},
  editor    = {Shafiullah, GM},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/infrastructures7080104},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20220831-47093},
  pages     = {18},
  abstract  = {One of the most important renewable energy technologies used nowadays are wind power turbines. In this paper, we are interested in identifying the operating status of wind turbines, especially rotor blades, by means of multiphysical models. It is a state-of-the-art technology to test mechanical structures with ultrasonic-based methods. However, due to the density and the required high resolution, the testing is performed with high-frequency waves, which cannot penetrate the structure in depth. Therefore, there is a need to adopt techniques in the fields of multiphysical model-based inversion schemes or data-driven structural health monitoring. Before investing effort in the development of such approaches, further insights and approaches are necessary to make the techniques applicable to structures such as wind power plants (blades). Among the expected developments, further accelerations of the so-called "forward codes" for a more efficient implementation of the wave equation could be envisaged. Here, we employ electromagnetic waves for the early detection of cracks. Because in many practical situations, it is not possible to apply techniques from tomography (characterized by multiple sources and sensor pairs), we focus here on the question of whether the existence of cracks can be determined by using only one source for the sent waves.},
  subject      = {Windkraftwerk},
  language  = {en}
}
@article{Legatiuk,
  author    = {Legatiuk, Dmitrii},
  title     = {Mathematical Modelling by Help of Category Theory: Models and Relations between Them},
  series = {mathematics},
  volume    = {2021},
  journal   = {mathematics},
  number    = {volume 9, issue 16, article 1946},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/math9161946},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210817-44844},
  pages     = {17},
  abstract  = {The growing complexity of modern practical problems puts high demand on mathematical modelling. Given that various models can be used for modelling one physical phenomenon, the role of model comparison and model choice is becoming particularly important. Methods for model comparison and model choice typically used in practical applications nowadays are computationbased, and thus time consuming and computationally costly. Therefore, it is necessary to develop other approaches to working abstractly, i.e., without computations, with mathematical models. An abstract description of mathematical models can be achieved by the help of abstract mathematics, implying formalisation of models and relations between them. In this paper, a category theory-based approach to mathematical modelling is proposed. In this way, mathematical models are formalised in the language of categories, relations between the models are formally defined and several practically relevant properties are introduced on the level of categories. Finally, an illustrative example is presented, underlying how the category-theory based approach can be used in practice. Further, all constructions presented in this paper are also discussed from a modelling point of view by making explicit the link to concrete modelling scenarios.},
  subject      = {Kategorientheorie},
  language  = {en}
}
@article{GhazvineiDarvishiMosavietal.,
  author    = {Ghazvinei, Pezhman Taherei and Darvishi, Hossein Hassanpour and Mosavi, Amir and Yusof, Khamaruzaman bin Wan and Alizamir, Meysam and Shamshirband, Shahaboddin and Chau, Kwok-Wing},
  title     = {Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network},
  series = {Engineering Applications of Computational Fluid Mechanics},
  volume    = {2018},
  journal   = {Engineering Applications of Computational Fluid Mechanics},
  number    = {12,1},
  publisher = {Taylor \& Francis},
  doi       = {10.1080/19942060.2018.1526119},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20181017-38129},
  pages     = {738 -- 749},
  abstract  = {Management strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and an approach based on whole systems analysis is essential to bring about beneficial change to industry and the community. The application of this approach to water management, environmental management and cane supply management is outlined, where the literature indicates that the application of extreme learning machine (ELM) has never been explored in this realm. Consequently, the leading objective of the current research was set to filling this gap by applying ELM to launch swift and accurate model for crop production data-driven. The key learning has been the need for innovation both in the technical aspects of system function underpinned by modelling of sugarcane growth. Therefore, the current study is an attempt to establish an integrate model using ELM to predict the concluding growth amount of sugarcane. Prediction results were evaluated and further compared with artificial neural network (ANN) and genetic programming models. Accuracy of the ELM model is calculated using the statistics indicators of Root Means Square Error (RMSE), Pearson Coefficient (r), and Coefficient of Determination (R2) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalization ability in addition to faster learning curve. Thus, proficiency of the ELM for supplementary work on advancement of prediction model for sugarcane growth was approved with promising results.},
  subject      = {K{\"u}nstliche Intelligenz},
  language  = {en}
}
@article{FaizollahzadehArdabiliNajafiAlizamiretal.,
  author    = {Faizollahzadeh Ardabili, Sina and Najafi, Bahman and Alizamir, Meysam and Mosavi, Amir and Shamshirband, Shahaboddin and Rabczuk, Timon},
  title     = {Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters},
  series = {Energies},
  journal   = {Energies},
  number    = {11, 2889},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/en11112889},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20181025-38170},
  pages     = {1 -- 20},
  abstract  = {The production of a desired product needs an effective use of the experimental model. The present study proposes an extreme learning machine (ELM) and a support vector machine (SVM) integrated with the response surface methodology (RSM) to solve the complexity in optimization and prediction of the ethyl ester and methyl ester production process. The novel hybrid models of ELM-RSM and ELM-SVM are further used as a case study to estimate the yield of methyl and ethyl esters through a trans-esterification process from waste cooking oil (WCO) based on American Society for Testing and Materials (ASTM) standards. The results of the prediction phase were also compared with artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS), which were recently developed by the second author of this study. Based on the results, an ELM with a correlation coefficient of 0.9815 and 0.9863 for methyl and ethyl esters, respectively, had a high estimation capability compared with that for SVM, ANNs, and ANFIS. Accordingly, the maximum production yield was obtained in the case of using ELM-RSM of 96.86\% for ethyl ester at a temperature of 68.48 °C, a catalyst value of 1.15 wt. \%, mixing intensity of 650.07 rpm, and an alcohol to oil molar ratio (A/O) of 5.77; for methyl ester, the production yield was 98.46\% at a temperature of 67.62 °C, a catalyst value of 1.1 wt. \%, mixing intensity of 709.42 rpm, and an A/O of 6.09. Therefore, ELM-RSM increased the production yield by 3.6\% for ethyl ester and 3.1\% for methyl ester, compared with those for the experimental data.},
  subject      = {Biodiesel},
  language  = {en}
}
@article{MorgenthalEickRauetal.,
  author    = {Morgenthal, Guido and Eick, Jan Frederick and Rau, Sebastian and Taraben, Jakob},
  title     = {Wireless Sensor Networks Composed of Standard Microcomputers and Smartphones for Applications in Structural Health Monitoring},
  series = {Sensors - Special Issue Selected Papers from 7th Asia-Pacific Workshop on Structural Health Monitoring},
  volume    = {2019},
  journal   = {Sensors - Special Issue Selected Papers from 7th Asia-Pacific Workshop on Structural Health Monitoring},
  number    = {Volume 19, Issue 9, 2070},
  publisher = {MDPI},
  doi       = {10.3390/s19092070},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20190514-39123},
  pages     = {22},
  abstract  = {Wireless sensor networks have attracted great attention for applications in structural health monitoring due to their ease of use, flexibility of deployment, and cost-effectiveness. This paper presents a software framework for WiFi-based wireless sensor networks composed of low-cost mass market single-board computers. A number of specific system-level software components were developed to enable robust data acquisition, data processing, sensor network communication, and timing with a focus on structural health monitoring (SHM) applications. The framework was validated on Raspberry Pi computers, and its performance was studied in detail. The paper presents several characteristics of the measurement quality such as sampling accuracy and time synchronization and discusses the specific limitations of the system. The implementation includes a complementary smartphone application that is utilized for data acquisition, visualization, and analysis. A prototypical implementation further demonstrates the feasibility of integrating smartphones as data acquisition nodes into the network, utilizing their internal sensors. The measurement system was employed in several monitoring campaigns, three of which are documented in detail. The suitability of the system is evaluated based on comparisons of target quantities with reference measurements. The results indicate that the presented system can robustly achieve a measurement performance commensurate with that required in many typical SHM tasks such as modal identification. As such, it represents a cost-effective alternative to more traditional monitoring solutions.},
  subject      = {Structural Health Monitoring},
  language  = {en}
}