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 - Motra, Hem Bahadur A1 - Hildebrand, Jörg A1 - Dimmig-Osburg, Andrea T1 - Assessment of strain measurement techniques to characterise mechanical properties of structural steel JF - Engineering Science and Technology, an International Journal N2 - Strain measurement is important in mechanical testing. A wide variety of techniques exists for measuring strain in the tensile test; namely the strain gauge, extensometer, stress and strain determined by machine crosshead motion, Geometric Moire technique, optical strain measurement techniques and others. Each technique has its own advantages and disadvantages. The purpose of this study is to quantitatively compare the strain measurement techniques. To carry out the tensile test experiments for S 235, sixty samples were cut from the web of the I-profile in longitudinal and transverse directions in four different dimensions. The geometry of samples are analysed by 3D scanner and vernier caliper. In addition, the strain values were determined by using strain gauge, extensometer and machine crosshead motion. Three techniques of strain measurement are compared in quantitative manner based on the calculation of mechanical properties (modulus of elasticity, yield strength, tensile strength, percentage elongation at maximum force) of structural steel. A statistical information was used for evaluating the results. It is seen that the extensometer and strain gauge provided reliable data, however the extensometer offers several advantages over the strain gauge and crosshead motion for testing structural steel in tension. Furthermore, estimation of measurement uncertainty is presented for the basic material parameters extracted through strain measurement. KW - Baustahl KW - Werkstoffprüfung KW - Zugversuch KW - Affecting factors; Measurement uncertainty; Materials testing; Quantitative comparison; Strain comparison; Tensile test Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170425-31540 SP - 260 EP - 269 ER - TY - JOUR A1 - Ataollahi Oshkour, Azim A1 - Talebi, Hossein A1 - Seyed Shirazi, Seyed Farid A1 - Bayat, Mehdi A1 - Yau, Yat Huang A1 - Tarlochan, Faris A1 - Abu Osman, Noor Azuan T1 - Comparison of various functionally graded femoral prostheses by finite element analysis JF - Scientific World Journal N2 - This study is focused on finite element analysis of a model comprising femur into which a femoral component of a total hip replacement was implanted. The considered prosthesis is fabricated from a functionally graded material (FGM) comprising a layer of a titanium alloy bonded to a layer of hydroxyapatite. The elastic modulus of the FGM was adjusted in the radial, longitudinal, and longitudinal-radial directions by altering the volume fraction gradient exponent. Four cases were studied, involving two different methods of anchoring the prosthesis to the spongy bone and two cases of applied loading. The results revealed that the FG prostheses provoked more SED to the bone. The FG prostheses carried less stress, while more stress was induced to the bone and cement. Meanwhile, less shear interface stress was stimulated to the prosthesis-bone interface in the noncemented FG prostheses. The cement-bone interface carried more stress compared to the prosthesis-cement interface. Stair climbing induced more harmful effects to the implanted femur components compared to the normal walking by causing more stress. Therefore, stress shielding, developed stresses, and interface stresses in the THR components could be adjusted through the controlling stiffness of the FG prosthesis by managing volume fraction gradient exponent. KW - Finite-Elemente-Methode KW - Hüftgelenk KW - Funktioneller Gradientenwerkstoff Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170413-31194 ER - TY - JOUR A1 - Aguinaga, José Guillermo De T1 - Error in prediction due to data type availability in a coupled hydro-mechanical model JF - Electronic Journal of Geotechnical Engineering N2 - Different types of data provide different type of information. The present research analyzes the error on prediction obtained under different data type availability for calibration. The contribution of different measurement types to model calibration and prognosis are evaluated. A coupled 2D hydro-mechanical model of a water retaining dam is taken as an example. Here, the mean effective stress in the porous skeleton is reduced due to an increase in pore water pressure under drawdown conditions. Relevant model parameters are identified by scaled sensitivities. Then, Particle Swarm Optimization is applied to determine the optimal parameter values and finally, the error in prognosis is determined. We compare the predictions of the optimized models with results from a forward run of the reference model to obtain the actual prediction errors. The analyses presented here were performed calibrating the hydro-mechanical model to 31 data sets of 100 observations of varying data types. The prognosis results improve when using diversified information for calibration. However, when using several types of information, the number of observations has to be increased to be able to cover a representative part of the model domain. For an analysis with constant number of observations, a compromise between data type availability and domain coverage proves to be the best solution. Which type of calibration information contributes to the best prognoses could not be determined in advance. The error in model prognosis does not depend on the error in calibration, but on the parameter error, which unfortunately cannot be determined in inverse problems since we do not know its real value. The best prognoses were obtained independent of calibration fit. However, excellent calibration fits led to an increase in prognosis error variation. In the case of excellent fits; parameters' values came near the limits of reasonable physical values more often. To improve the prognoses reliability, the expected value of the parameters should be considered as prior information on the optimization algorithm. KW - Sensitivitätsanalyse KW - Damm KW - Embankment, sensitivity analysis, parameter identification, Particle Swarm Optimization KW - Fehlerabschätzung Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170413-31170 UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84868020397&partnerID=40&md5=72c87bb112839303c1ef9a4afa8c6421 SP - 2459 EP - 2471 ER -