@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}
}
@inproceedings{Lahmer,
  author    = {Lahmer, Tom},
  title     = {HYDRO-MECHANICAL COUPLED FIELD SYSTEM IDENTIFICATION - APPLICATION TO WATER RESERVOIRS},
  editor    = {G{\"u}rlebeck, Klaus and K{\"o}nke, Carsten},
  organization    = {Bauhaus-Universit{\"a}t Weimar},
  issn      = {1611-4086},
  doi       = {10.25643/bauhaus-universitaet.2865},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170314-28650},
  pages     = {14},
  abstract  = {In this paper we present an inverse method which is capable of identifying system components in a hydro-mechanically coupled system, i.e. for fluid flow in porous media. As an example we regard water dams that were constructed more than hundred years ago but which are still in use. Over the time ageing processes have changed the condition of these dams. Within the dams fissures might have grown. The proposed method is designed to locate these fissures out of combined mechanical and hydraulic measurements. In a numerical example the fissures or damaged zones are described by a smeared crack model. The task is now to identify simultaneously the spatial distribution of Young's modulus and the hydraulic permeability due to the fact, that in regions where damages are present, the mechanical stiffness of the system is reduced and the permeability increased. The inversion is shown to be an ill-posed problem. As a consequence regularizing methods have to be applied, where the nonlinear Landweber method (a gradient type method combined with a discrepancy principle) has proven to be an efficient choice.},
  subject      = {Angewandte Informatik},
  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}
}
@inproceedings{LahmerGhorashi,
  author    = {Lahmer, Tom and Ghorashi, Seyed Shahram},
  title     = {XFEM-BASED CRACK IDENTIFICATION APPLYING REGULARIZING METHODS IN A MULTILEVEL APPROACH},
  series = {Digital Proceedings, International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering : July 04 - 06 2012, Bauhaus-University Weimar},
  booktitle = {Digital Proceedings, International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering : July 04 - 06 2012, Bauhaus-University Weimar},
  editor    = {G{\"u}rlebeck, Klaus and Lahmer, Tom and Werner, Frank},
  organization    = {Bauhaus-Universit{\"a}t Weimar},
  issn      = {1611-4086},
  doi       = {10.25643/bauhaus-universitaet.2771},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170314-27717},
  pages     = {9},
  abstract  = {Many structures in different engineering applications suffer from cracking. In order to make reliable prognosis about the serviceability of those structures it is of utmost importance to identify cracks as precisely as possible by non-destructive testing. A novel approach (XIGA), which combines the Isogeometric Analysis (IGA) and the Extended Finite Element Method (XFEM) is used for the forward problem, namely the analysis of a cracked material, see [1]. Applying the NURBS (Non-Uniform Rational B-Spline) based approach from IGA together with the XFEM allows to describe effectively arbitrarily shaped cracks and avoids the necessity of remeshing during the crack identification problem. We want to exploit these advantages for the inverse problem of detecting existing cracks by non-destructive testing, see e.g. [2]. The quality of the reconstructed cracks however depends on two major issues, namely the quality of the measured data (measurement error) and the discretization of the crack model. The first one will be taken into account by applying regularizing methods with a posteriori stopping criteria. The second one is critical in the sense that too few degrees of freedom, i.e. the number of control points of the NURBS, do not allow for a precise description of the crack. An increased number of control points, however, increases the number of unknowns in the inverse analysis and intensifies the ill-posedness. The trade-off between accuracy and stability is aimed to be found by applying an inverse multilevel algorithm [3, 4] where the identification is started with short knot vectors which successively will be enlarged during the identification process.},
  subject      = {Angewandte Informatik},
  language  = {en}
}
@article{LizarazuHarirchianShaiketal.,
  author    = {Lizarazu, Jorge and Harirchian, Ehsan and Shaik, Umar Arif and Shareef, Mohammed and Antoni-Zdziobek, Annie and Lahmer, Tom},
  title     = {Application of machine learning-based algorithms to predict the stress-strain curves of additively manufactured mild steel out of its microstructural characteristics},
  series = {Results in Engineering},
  volume    = {2023},
  journal   = {Results in Engineering},
  number    = {Volume 20 (2023)},
  publisher = {Elsevier},
  address   = {Amsterdam},
  doi       = {10.1016/j.rineng.2023.101587},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20231207-65028},
  pages     = {1 -- 12},
  abstract  = {The study presents a Machine Learning (ML)-based framework designed to forecast the stress-strain relationship of arc-direct energy deposited mild steel. Based on microstructural characteristics previously extracted using microscopy and X-ray diffraction, approximately 1000 new parameter sets are generated by applying the Latin Hypercube Sampling Method (LHSM). For each parameter set, a Representative Volume Element (RVE) is synthetically created via Voronoi Tessellation. Input raw data for ML-based algorithms comprises these parameter sets or RVE-images, while output raw data includes their corresponding stress-strain relationships calculated after a Finite Element (FE) procedure. Input data undergoes preprocessing involving standardization, feature selection, and image resizing. Similarly, the stress-strain curves, initially unsuitable for training traditional ML algorithms, are preprocessed using cubic splines and occasionally Principal Component Analysis (PCA). The later part of the study focuses on employing multiple ML algorithms, utilizing two main models. The first model predicts stress-strain curves based on microstructural parameters, while the second model does so solely from RVE images. The most accurate prediction yields a Root Mean Squared Error of around 5 MPa, approximately 1\% of the yield stress. This outcome suggests that ML models offer precise and efficient methods for characterizing dual-phase steels, establishing a framework for accurate results in material analysis.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@inproceedings{NguyenTuanLahmerDatchevaetal.,
  author    = {Nguyen-Tuan, Long and Lahmer, Tom and Datcheva, Maria and Stoimenova, Eugenia and Schanz, Tom},
  title     = {PARAMETER IDENTIFICATION APPLYING IN COMPLEX THERMO-HYDRO-MECHANICAL PROBLEMS LIKE THE DESIGN OF BUFFER ELEMENTS},
  series = {Digital Proceedings, International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering : July 20 - 22 2015, Bauhaus-University Weimar},
  booktitle = {Digital Proceedings, International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering : July 20 - 22 2015, Bauhaus-University Weimar},
  editor    = {G{\"u}rlebeck, Klaus and Lahmer, Tom},
  organization    = {Bauhaus-Universit{\"a}t Weimar},
  issn      = {1611-4086},
  doi       = {10.25643/bauhaus-universitaet.2816},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170314-28162},
  pages     = {6},
  abstract  = {This study contributes to the identification of coupled THM constitutive model parameters via back analysis against information-rich experiments. A sampling based back analysis approach is proposed comprising both the model parameter identification and the assessment of the reliability of identified model parameters. The results obtained in the context of buffer elements indicate that sensitive parameter estimates generally obey the normal distribution. According to the sensitivity of the parameters and the probability distribution of the samples we can provide confidence intervals for the estimated parameters and thus allow a qualitative estimation on the identified parameters which are in future work used as inputs for prognosis computations of buffer elements. These elements play e.g. an important role in the design of nuclear waste repositories.},
  subject      = {Angewandte Informatik},
  language  = {en}
}
@article{ReichertOlneyLahmer,
  author    = {Reichert, Ina and Olney, Peter and Lahmer, Tom},
  title     = {Combined approach for optimal sensor placement and experimental verification in the context of tower-like structures},
  series = {Journal of Civil Structural Health Monitoring},
  volume    = {2021},
  journal   = {Journal of Civil Structural Health Monitoring},
  number    = {volume 11},
  publisher = {Heidelberg},
  address   = {Springer},
  doi       = {10.1007/s13349-020-00448-7},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210804-44701},
  pages     = {223 -- 234},
  abstract  = {When it comes to monitoring of huge structures, main issues are limited time, high costs and how to deal with the big amount of data. In order to reduce and manage them, respectively, methods from the field of optimal design of experiments are useful and supportive. Having optimal experimental designs at hand before conducting any measurements is leading to a highly informative measurement concept, where the sensor positions are optimized according to minimal errors in the structures' models. For the reduction of computational time a combined approach using Fisher Information Matrix and mean-squared error in a two-step procedure is proposed under the consideration of different error types. The error descriptions contain random/aleatoric and systematic/epistemic portions. Applying this combined approach on a finite element model using artificial acceleration time measurement data with artificially added errors leads to the optimized sensor positions. These findings are compared to results from laboratory experiments on the modeled structure, which is a tower-like structure represented by a hollow pipe as the cantilever beam. Conclusively, the combined approach is leading to a sound experimental design that leads to a good estimate of the structure's behavior and model parameters without the need of preliminary measurements for model updating.},
  subject      = {Strukturmechanik},
  language  = {en}
}
@article{SchmidtLahmer,
  author    = {Schmidt, Albrecht and Lahmer, Tom},
  title     = {Efficient domain decomposition based reliability analysis for polymorphic uncertain material parameters},
  series = {Proceedings in Applied Mathematics \& Mechanics},
  volume    = {2021},
  journal   = {Proceedings in Applied Mathematics \& Mechanics},
  number    = {Volume 21, issue 1},
  publisher = {Wiley-VHC},
  address   = {Weinheim},
  doi       = {10.1002/pamm.202100014},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20220112-45563},
  pages     = {1 -- 4},
  abstract  = {Realistic uncertainty description incorporating aleatoric and epistemic uncertainties can be described within the framework of polymorphic uncertainty, which is computationally demanding. Utilizing a domain decomposition approach for random field based uncertainty models the proposed level-based sampling method can reduce these computational costs significantly and shows good agreement with a standard sampling technique. While 2-level configurations tend to get unstable with decreasing sampling density 3-level setups show encouraging results for the investigated reliability analysis of a structural unit square.},
  subject      = {Polymorphie},
  language  = {en}
}
@unpublished{SteinerBourinetLahmer,
  author    = {Steiner, Maria and Bourinet, Jean-Marc and Lahmer, Tom},
  title     = {An adaptive sampling method for global sensitivity analysis based on least-squares support vector regression},
  doi       = {10.25643/BAUHAUS-UNIVERSITAET.3832},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20181218-38320},
  pages     = {1 -- 33},
  abstract  = {In the field of engineering, surrogate models are commonly used for approximating the behavior of a physical phenomenon in order to reduce the computational costs. Generally, a surrogate model is created based on a set of training data, where a typical method for the statistical design is the Latin hypercube sampling (LHS). Even though a space filling distribution of the training data is reached, the sampling process takes no information on the underlying behavior of the physical phenomenon into account and new data cannot be sampled in the same distribution if the approximation quality is not sufficient. Therefore, in this study we present a novel adaptive sampling method based on a specific surrogate model, the least-squares support vector regresson. The adaptive sampling method generates training data based on the uncertainty in local prognosis capabilities of the surrogate model - areas of higher uncertainty require more sample data. The approach offers a cost efficient calculation due to the properties of the least-squares support vector regression. The opportunities of the adaptive sampling method are proven in comparison with the LHS on different analytical examples. Furthermore, the adaptive sampling method is applied to the calculation of global sensitivity values according to Sobol, where it shows faster convergence than the LHS method. With the applications in this paper it is shown that the presented adaptive sampling method improves the estimation of global sensitivity values, hence reducing the overall computational costs visibly.},
  subject      = {Approximation},
  language  = {en}
}
@inproceedings{TanLahmerSiddappa,
  author    = {Tan, Fengjie and Lahmer, Tom and Siddappa, Manju Gyaraganahalll},
  title     = {SECTION OPTIMIZATION AND RELIABILITY ANALYSIS OF ARCH-TYPE DAMS INCLUDING COUPLED MECHANICAL-THERMAL AND HYDRAULIC FIELDS},
  series = {Digital Proceedings, International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering : July 20 - 22 2015, Bauhaus-University Weimar},
  booktitle = {Digital Proceedings, International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering : July 20 - 22 2015, Bauhaus-University Weimar},
  editor    = {G{\"u}rlebeck, Klaus and Lahmer, Tom},
  organization    = {Bauhaus-Universit{\"a}t Weimar},
  issn      = {1611-4086},
  doi       = {10.25643/bauhaus-universitaet.2821},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170314-28212},
  pages     = {8},
  abstract  = {From the design experiences of arch dams in the past, it has significant practical value to carry out the shape optimization of arch dams, which can fully make use of material characteristics and reduce the cost of constructions. Suitable variables need to be chosen to formulate the objective function, e.g. to minimize the total volume of the arch dam. Additionally a series of constraints are derived and a reasonable and convenient penalty function has been formed, which can easily enforce the characteristics of constraints and optimal design. For the optimization method, a Genetic Algorithm is adopted to perform a global search. Simultaneously, ANSYS is used to do the mechanical analysis under the coupling of thermal and hydraulic loads. One of the constraints of the newly designed dam is to fulfill requirements on the structural safety. Therefore, a reliability analysis is applied to offer a good decision supporting for matters concerning predictions of both safety and service life of the arch dam. By this, the key factors which would influence the stability and safety of arch dam significantly can be acquired, and supply a good way to take preventive measures to prolong ate the service life of an arch dam and enhances the safety of structure.},
  subject      = {Angewandte Informatik},
  language  = {en}
}