@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} } @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{ZhangWangLahmeretal., author = {Zhang, Chao and Wang, Cuixia and Lahmer, Tom and He, Pengfei and Rabczuk, Timon}, title = {A dynamic XFEM formulation for crack identification}, series = {International Journal of Mechanics and Materials in Design}, journal = {International Journal of Mechanics and Materials in Design}, pages = {427 -- 448}, abstract = {A dynamic XFEM formulation for crack identification}, subject = {Angewandte Mathematik}, 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{NguyenTuanLahmerDatchevaetal., author = {Nguyen-Tuan, Long and Lahmer, Tom and Datcheva, Maria and Stoimenova, Eugenia and Schanz, Tom}, title = {A novel parameter identification approach for buffer elements involving complex coupled thermo-hydro-mechanical analyses}, series = {Computers and Geotechnics}, journal = {Computers and Geotechnics}, pages = {23 -- 32}, abstract = {A novel parameter identification approach for buffer elements involving complex coupled thermo-hydro-mechanical analyses}, subject = {Angewandte Mathematik}, language = {en} } @article{AlkamLahmer, author = {Alkam, Feras and Lahmer, Tom}, title = {A robust method of the status monitoring of catenary poles installed along high-speed electrified train tracks}, series = {Results in Engineering}, volume = {2021}, journal = {Results in Engineering}, number = {volume 12, article 100289}, publisher = {Elsevier}, address = {Amsterdam}, doi = {10.1016/j.rineng.2021.100289}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20211011-45212}, pages = {1 -- 8}, abstract = {Electric trains are considered one of the most eco-friendly and safest means of transportation. Catenary poles are used worldwide to support overhead power lines for electric trains. The performance of the catenary poles has an extensive influence on the integrity of the train systems and, consequently, the connected human services. It became a must nowadays to develop SHM systems that provide the instantaneous status of catenary poles in- service, making the decision-making processes to keep or repair the damaged poles more feasible. This study develops a data-driven, model-free approach for status monitoring of cantilever structures, focusing on pre-stressed, spun-cast ultrahigh-strength concrete catenary poles installed along high-speed train tracks. The pro-posed approach evaluates multiple damage features in an unfied damage index, which leads to straightforward interpretation and comparison of the output. Besides, it distinguishes between multiple damage scenarios of the poles, either the ones caused by material degradation of the concrete or by the cracks that can be propagated during the life span of the given structure. Moreover, using a logistic function to classify the integrity of structure avoids the expensive learning step in the existing damage detection approaches, namely, using the modern machine and deep learning methods. The findings of this study look very promising when applied to other types of cantilever structures, such as the poles that support the power transmission lines, antenna masts, chimneys, and wind turbines.}, subject = {Fahrleitung}, language = {en} } @article{VuBacLahmerZhuangetal., author = {Vu-Bac, N. and Lahmer, Tom and Zhuang, Xiaoying and Nguyen-Thoi, T. and Rabczuk, Timon}, title = {A software framework for probabilistic sensitivity analysis for computationally expensive models}, series = {Advances in Engineering Software}, journal = {Advances in Engineering Software}, pages = {19 -- 31}, abstract = {A software framework for probabilistic sensitivity analysis for computationally expensive models}, subject = {Angewandte Mathematik}, language = {en} } @article{GhorashiLahmerBagherzadehetal., author = {Ghorashi, Seyed Shahram and Lahmer, Tom and Bagherzadeh, Amir Saboor and Zi, Goangseup and Rabczuk, Timon}, title = {A stochastic computational method based on goal-oriented error estimation for heterogeneous geological materials}, series = {Engineering Geology}, journal = {Engineering Geology}, abstract = {A stochastic computational method based on goal-oriented error estimation for heterogeneous geological materials}, subject = {Angewandte Mathematik}, 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{VuBacSilaniLahmeretal., author = {Vu-Bac, N. and Silani, Mohammad and Lahmer, Tom and Zhuang, Xiaoying and Rabczuk, Timon}, title = {A unified framework for stochastic predictions of Young's modulus of clay/epoxy nanocomposites (PCNs)}, series = {Computational Materials Science}, journal = {Computational Materials Science}, pages = {520 -- 535}, abstract = {A unified framework for stochastic predictions of Young's modulus of clay/epoxy nanocomposites (PCNs)}, subject = {Angewandte Mathematik}, 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} } @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} } @article{HarirchianLahmerKumarietal., author = {Harirchian, Ehsan and Lahmer, Tom and Kumari, Vandana and Jadhav, Kirti}, title = {Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings}, series = {Energies}, volume = {2020}, journal = {Energies}, number = {volume 13, issue 13, 3340}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/en13133340}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200707-41915}, pages = {15}, abstract = {The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 D{\"u}zce Earthquake in Turkey, where the building's data consists of 22 performance modifiers that have been implemented with supervised machine learning.}, subject = {Erdbeben}, language = {en} } @article{LahmerKnabeNikullaetal., author = {Lahmer, Tom and Knabe, Tina and Nikulla, Susanne and Reuter, Markus}, title = {Bewertungsmethoden f{\"u}r Modelle des konstruktiven Ingenieurbaus}, series = {Bautechnik}, journal = {Bautechnik}, pages = {60 -- 64}, abstract = {Bewertungsmethoden f{\"u}r Modelle des konstruktiven Ingenieurbaus}, subject = {Angewandte Mathematik}, language = {de} } @inproceedings{AlaladeKafleWuttkeetal., author = {Alalade, Muyiwa and Kafle, Binod and Wuttke, Frank and Lahmer, Tom}, title = {CALIBRATION OF CYCLIC CONSTITUTIVE MODELS FOR SOILS BY OSCILLATING FUNCTIONS}, 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.2793}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170314-27932}, pages = {6}, abstract = {In order to minimize the probability of foundation failure resulting from cyclic action on structures, researchers have developed various constitutive models to simulate the foundation response and soil interaction as a result of these complex cyclic loads. The efficiency and effectiveness of these model is majorly influenced by the cyclic constitutive parameters. Although a lot of research is being carried out on these relatively new models, little or no details exist in literature about the model based identification of the cyclic constitutive parameters. This could be attributed to the difficulties and complexities of the inverse modeling of such complex phenomena. A variety of optimization strategies are available for the solution of the sum of least-squares problems as usually done in the field of model calibration. However for the back analysis (calibration) of the soil response to oscillatory load functions, this paper gives insight into the model calibration challenges and also puts forward a method for the inverse modeling of cyclic loaded foundation response such that high quality solutions are obtained with minimum computational effort. Therefore model responses are produced which adequately describes what would otherwise be experienced in the laboratory or field.}, subject = {Angewandte Informatik}, language = {en} } @article{FaridmehrTahirLahmer, author = {Faridmehr, Iman and Tahir, Mamood Md. and Lahmer, Tom}, title = {Classification System for Semi-Rigid Beam-to-Column Connections}, series = {LATIN AMERICAN JOURNAL OF SOLIDS AND STRUCTURES 11}, journal = {LATIN AMERICAN JOURNAL OF SOLIDS AND STRUCTURES 11}, doi = {10.1590/1679-78252595}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170401-30988}, pages = {2152 -- 2175}, abstract = {The current study attempts to recognise an adequate classification for a semi-rigid beam-to-column connection by investigating strength, stiffness and ductility. For this purpose, an experimental test was carried out to investigate the moment-rotation (M-theta) features of flush end-plate (FEP) connections including variable parameters like size and number of bolts, thickness of end-plate, and finally, size of beams and columns. The initial elastic stiffness and ultimate moment capacity of connections were determined by an extensive analytical procedure from the proposed method prescribed by ANSI/AISC 360-10, and Eurocode 3 Part 1-8 specifications. The behaviour of beams with partially restrained or semi-rigid connections were also studied by incorporating classical analysis methods. The results confirmed that thickness of the column flange and end-plate substantially govern over the initial rotational stiffness of of flush end-plate connections. The results also clearly showed that EC3 provided a more reliable classification index for flush end-plate (FEP) connections. The findings from this study make significant contributions to the current literature as the actual response characteristics of such connections are non-linear. Therefore, such semirigid behaviour should be used to for an analysis and design method.}, subject = {Tragf{\"a}higkeit}, 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{MarzbanLahmer, author = {Marzban, Samira and Lahmer, Tom}, title = {Conceptual implementation of the variance-based sensitivity analysis for the calculation of the first-order effects}, series = {Journal of Statistical Theory and Practice}, journal = {Journal of Statistical Theory and Practice}, pages = {589 -- 611}, abstract = {Conceptual implementation of the variance-based sensitivity analysis for the calculation of the first-order effects}, subject = {Angewandte Mathematik}, language = {en} } @article{Lahmer, author = {Lahmer, Tom}, title = {Crack identification in hydro-mechanical systems with applications to gravity water dams}, series = {Inverse Problems in Science and Engineering}, journal = {Inverse Problems in Science and Engineering}, pages = {1083 -- 1101}, abstract = {Crack identification in hydro-mechanical systems with applications to gravity water dams}, subject = {Angewandte Mathematik}, language = {en} } @article{AlaladeNguyenTuanWuttkeetal., author = {Alalade, Muyiwa and Nguyen-Tuan, Long and Wuttke, Frank and Lahmer, Tom}, title = {Damage identification in gravity dams using dynamic coupled hydro-mechanical XFEM}, series = {International Journal of Mechanics and Materials in Design}, journal = {International Journal of Mechanics and Materials in Design}, doi = {10.25643/bauhaus-universitaet.3596}, pages = {1 -- 19}, abstract = {Damage identification in gravity dams using dynamic coupled hydro-mechanical XFEM.}, subject = {Angewandte Mathematik}, language = {en} }