TY - INPR A1 - Steiner, Maria A1 - Bourinet, Jean-Marc A1 - Lahmer, Tom T1 - An adaptive sampling method for global sensitivity analysis based on least-squares support vector regression N2 - 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. KW - Approximation KW - Sensitivitätsanalyse KW - Abtastung KW - Surrogate models KW - Least-squares support vector regression KW - Adaptive sampling method KW - Global sensitivity analysis KW - Sampling Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20181218-38320 N1 - This is the pre-peer reviewed version of the following article: https://www.sciencedirect.com/science/article/pii/S0951832017311808, which has been published in final form at https://doi.org/10.1016/j.ress.2018.11.015. SP - 1 EP - 33 ER - TY - JOUR A1 - Lizarazu, Jorge A1 - Harirchian, Ehsan A1 - Shaik, Umar Arif A1 - Shareef, Mohammed A1 - Antoni-Zdziobek, Annie A1 - Lahmer, Tom T1 - Application of machine learning-based algorithms to predict the stress-strain curves of additively manufactured mild steel out of its microstructural characteristics JF - Results in Engineering N2 - 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. KW - Maschinelles Lernen KW - Baustahl KW - Spannungs-Dehnungs-Beziehung KW - Arc-direct energy deposition KW - Mild steel KW - Dual phase steel KW - Stress-strain curve KW - OA-Publikationsfonds2023 Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20231207-65028 UR - https://www.sciencedirect.com/science/article/pii/S2590123023007144 VL - 2023 IS - Volume 20 (2023) SP - 1 EP - 12 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Harirchian, Ehsan A1 - Lahmer, Tom A1 - Kumari, Vandana A1 - Jadhav, Kirti T1 - Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings JF - Energies N2 - 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 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning. KW - Erdbeben KW - Maschinelles Lernen KW - earthquake vulnerability assessment KW - rapid visual screening KW - machine learning KW - support vector machine KW - buildings KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200707-41915 UR - https://www.mdpi.com/1996-1073/13/13/3340 VL - 2020 IS - volume 13, issue 13, 3340 PB - MDPI CY - Basel ER - TY - JOUR A1 - Lahmer, Tom A1 - Knabe, Tina A1 - Nikulla, Susanne A1 - Reuter, Markus T1 - Bewertungsmethoden für Modelle des konstruktiven Ingenieurbaus BT - Sonderdruck‐DFG Graduiertenkolleg JF - Bautechnik N2 - Bewertungsmethoden für Modelle des konstruktiven Ingenieurbaus KW - Angewandte Mathematik KW - Stochastik KW - Strukturmechanik Y1 - 2011 SP - 60 EP - 64 ER - TY - CHAP A1 - Alalade, Muyiwa A1 - Kafle, Binod A1 - Wuttke, Frank A1 - Lahmer, Tom ED - Gürlebeck, Klaus ED - Lahmer, Tom T1 - CALIBRATION OF CYCLIC CONSTITUTIVE MODELS FOR SOILS BY OSCILLATING FUNCTIONS T2 - Digital Proceedings, International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering : July 20 - 22 2015, Bauhaus-University Weimar N2 - 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. KW - Angewandte Informatik KW - Angewandte Mathematik KW - Building Information Modeling KW - Computerunterstütztes Verfahren KW - Data, information and knowledge modeling in civil engineering; Function theoretic methods and PDE in engineering sciences; Mathematical methods for (robotics and) computer vision; Numerical modeling in engineering; Optimization in engineering applications Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170314-27932 SN - 1611-4086 ER - TY - JOUR A1 - Faridmehr, Iman A1 - Tahir, Mamood Md. A1 - Lahmer, Tom T1 - Classification System for Semi-Rigid Beam-to-Column Connections JF - LATIN AMERICAN JOURNAL OF SOLIDS AND STRUCTURES 11 N2 - 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. KW - Tragfähigkeit KW - Stütze KW - Träger KW - Beam-to-column connection; semi-rigid; flush end-plate connection; moment-rotation curve Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170401-30988 SP - 2152 EP - 2175 ER - TY - JOUR A1 - Reichert, Ina A1 - Olney, Peter A1 - Lahmer, Tom T1 - Combined approach for optimal sensor placement and experimental verification in the context of tower-like structures JF - Journal of Civil Structural Health Monitoring N2 - 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. KW - Strukturmechanik KW - Finite-Elemente-Methode KW - tower-like structures KW - experimental validation KW - mean-squared error KW - fisher-information matrix Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210804-44701 UR - https://link.springer.com/article/10.1007/s13349-020-00448-7 VL - 2021 IS - volume 11 SP - 223 EP - 234 PB - Heidelberg CY - Springer ER - TY - JOUR A1 - Marzban, Samira A1 - Lahmer, Tom T1 - Conceptual implementation of the variance-based sensitivity analysis for the calculation of the first-order effects JF - Journal of Statistical Theory and Practice N2 - Conceptual implementation of the variance-based sensitivity analysis for the calculation of the first-order effects KW - Angewandte Mathematik KW - Stochastik KW - Strukturmechanik Y1 - 2016 SP - 589 EP - 611 ER - TY - JOUR A1 - Lahmer, Tom T1 - Crack identification in hydro-mechanical systems with applications to gravity water dams JF - Inverse Problems in Science and Engineering N2 - Crack identification in hydro-mechanical systems with applications to gravity water dams KW - Angewandte Mathematik KW - Stochastik KW - Strukturmechanik Y1 - 2010 SP - 1083 EP - 1101 ER - TY - JOUR A1 - Alalade, Muyiwa A1 - Nguyen-Tuan, Long A1 - Wuttke, Frank A1 - Lahmer, Tom T1 - Damage identification in gravity dams using dynamic coupled hydro-mechanical XFEM JF - International Journal of Mechanics and Materials in Design N2 - Damage identification in gravity dams using dynamic coupled hydro-mechanical XFEM. KW - Angewandte Mathematik KW - Stochastik KW - Strukturmechanik Y1 - 2017 U6 - http://dx.doi.org/10.25643/bauhaus-universitaet.3596 SP - 1 EP - 19 ER -