نتائج البحث - "Identificación estructural"

المساهمون: Universitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental, Universitat Politècnica de Catalunya. EC - Enginyeria de la Construcció

مصطلحات موضوعية: Àrees temàtiques de la UPC::Enginyeria civil::Materials i estructures::Tipologies estructurals, Bridges--Maintenance and repair, Técnicas de observabilidad, sistemas de identificación estructural, análisis numérico, Ponts -- Manteniment i reparació

وصف الملف: 7 p.

العلاقة: http://www.acct.cl/?p=2006Test; info:eu-repo/grantAgreement/MINECO/PN2013-2016/BIA2013-47290-R; Lozano-Galant, J.A., Nogal, M., Jun, L., Xu, D., Turmo, J., Ramos, G. Evaluación estructural de puentes existentes: técnicas de observabilidad. A: Congreso Internacional de Puentes. "Presentaciones/Papers: Congreso Puentes 2017". Santiago de Chile: Asociación Chilena de Carreteras y Transporte (ACCT), 2017, p. 1-7.; http://hdl.handle.net/2117/112549Test

الإتاحة: http://hdl.handle.net/2117/112549Test

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دورية أكاديمية

Study of vibration period for concrete bridges in Panama ; Estudio del período de vibración de puentes de concreto en Panamá

المؤلفون: Rodríguez, Stephania M, Gallardo, José M, Araúz, Osvaldo A

المصدر: 2219-6714 ; 1680-8894 ; I+D Tecnológico; Vol. 13, Núm. 1 (2017): Revista I+D Tecnológico; 86-93

مصطلحات موضوعية: frequency, structural identification, bridge, vibration, frecuencia, identificación estructural, puente, vibración

وصف الملف: application/pdf

العلاقة: http://revistas.utp.ac.pa/index.php/id-tecnologico/article/view/1441/2004Test; http://revistas.utp.ac.pa/index.php/id-tecnologico/article/view/1441Test; http://ridda2.utp.ac.pa/handle/123456789/1743Test

الإتاحة: http://ridda2.utp.ac.pa/handle/123456789/1743Test
http://revistas.utp.ac.pa/index.php/id-tecnologico/article/view/1441Test

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Modelación de sistemas estructurales histeréticos usando perceptrones multicapa y técnicas de guiado físico

المؤلفون: Delgado Trujillo, Juan Sebastián

المساهمون: Álvarez Marín, Diego Andrés, Bedoya Ruíz, Daniel Alveiro, Ingeniería Sísmica y Sismología, Delgado Trujillo, Juan Sebastián [0000-0001-7135-0460]

مصطلحات موضوعية: Scientific machine learning, Consistencia física, Machine learning científico, Construction engineering, Nonlinear structural identification, Physical consistency, 624 - Ingeniería civil [620 - Ingeniería y operaciones afines], Redes neuronales guiadas por la física, Modelación histerética, Hysteresis modeling, Universal ordinary differential equations, Physics-guided neural networks, Ingeniería de estructuras, Ecuaciones diferenciales ordinarias universales, Identificación estructural no lineal

وصف الملف: xv, 110 páginas; application/pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=od______1326::a00afc2e222388bdb3f80b33e5d76cafTest
https://repositorio.unal.edu.co/handle/unal/83560Test

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رسالة جامعية

Modeling of hysteretic structural systems using multilayer perceptrons and physics-guiding techniques ; Modelación de sistemas estructurales histeréticos usando perceptrones multicapa y técnicas de guiado físico

المؤلفون: Delgado Trujillo, Juan Sebastián

المساهمون: Álvarez Marín, Diego Andrés, Bedoya Ruíz, Daniel Alveiro, Ingeniería Sísmica y Sismología, Delgado Trujillo, Juan Sebastián 0000-0001-7135-0460

مصطلحات موضوعية: 620 - Ingeniería y operaciones afines::624 - Ingeniería civil, Hysteresis modeling, Nonlinear structural identification, Physical consistency, Scientific machine learning, Universal ordinary differential equations, Physics-guided neural networks, Modelación histerética, Identificación estructural no lineal, Consistencia física, Machine learning científico, Ecuaciones diferenciales ordinarias universales, Redes neuronales guiadas por la física, Ingeniería de estructuras, Construction engineering

وصف الملف: xv, 110 páginas; application/pdf

العلاقة: Abbas, Y. and Khan, M. (2016). Influence of fiber properties on shear failure of steel fiber reinforced beams without web reinforcement: ANN modeling. Latin American Journal of Solids and Structures, 13(8):1483–1498. (Cited on page 39.); Abramowitz, M. and Stegun, I. A. (1948). Handbook of mathematical functions with formulas, graphs, and mathematical tables, volume 55. US Government printing office. (Cited on page 94.); Akazawa, T., Nakashima, M., and Sakaguchi, O. (1996). Simple model for simulating hysteretic behavior involving significant strain hardening. In Eleventh World Conference on Earthquake Engineering, Paper, number 264. (Cited on page 37.); Al-Bermani, F., Li, B., Zhu, K., and Kitipornchai, S. (1994). Cyclic and seismic response of flexibly jointed frames. Engineering Structures, 16(4):249–255. (Cited on page 8.); Appelbe, B., Flynn, D., McNamara, H., O’Kane, P., Pimenov, A., Pokrovskii, A., Rachinskii, D., and Zhezherun, A. (2009). Rate-independent hysteresis in terrestrial hydrology. IEEE Control Systems Magazine, 29(1):44–69. (Cited on page 6.); Asteris, P. and Mokos, V. (2020). Concrete compressive strength using artificial neural networks. Neural Computing and Applications, 32(15):11807–11826. (Cited on page 39.); Asteris, P., Tsaris, A., Cavaleri, L., Repapis, C., Papalou, A., Di Trapani, F., and Karypidis, D. (2016). Prediction of the fundamental period of infilled RC frame structures using artificial neural networks. Computational Intelligence and Neuroscience, 2016. (Cited on page 39.); ASTM (2011). Standard test methods for cyclic (reversed) load test for shear resistance of vertical elements of the lateral force resisting systems for buildings. ASTM E2126. (Cited on page 68.); Atalay, M. B. and Penzien, J. (1975). The seismic behavior of critical regions of reinforced concrete components as influenced by moment, shear and axial force. Technical Report EERC 75-19, Earthquake Engineering Research Center, University of California Berkeley. (Cited on page 8.); Aydin, K. and Kisi, O. (2015). Damage diagnosis in beam-like structures by artificial neural networks. Journal of Civil Engineering and Management, 21(5):591–604. (Cited on page 40.); Baber, T. T. and Noori, M. N. (1985). Random vibration of degrading, pinching systems. Journal of Engineering Mechanics, 111(8):1010–1026. (Cited on pages 10, 11, and 46.); Baber, T. T. and Wen, Y.-K. (1981). Random vibration hysteretic, degrading systems. Journal of the Engineering Mechanics Division, 107(6):1069–1087. (Cited on pages 10 and 11.); Bahar, A., Pozo, F., Acho, L., Rodellar, J., and Barbat, A. (2010). Hierarchical semiactive control of base-isolated structures using a new inverse model of magnetorheological dampers. Computers and Structures, 88(7-8):483–496. (Cited on page 37.); Bani-Hani, K. and Sheban, M. (2006). Semi-active neuro-control for base-isolation system using magnetorheological (MR) dampers. Earthquake Engineering and Structural Dynamics, 35(9):1119–1144. (Cited on page 41.); Bedoya-Ruíz, D., Hurtado, J. E., and Pujades, L. (2010). Experimental and analytical research on seismic vulnerability of low-cost ferrocement dwelling houses. Structure and Infrastructure Engineering, 6(1-2):55–62. (Cited on page 65.); Beko, A., Rosko, P., Wenzel, H., Pegon, P., Markovic, D., and Molina, F. (2015). RC shear walls: Full-scale cyclic test, insights and derived analytical model. Engineering Structures, 102:120–131. (Cited on pages 33 and 37.); Bertotti, G. (1998). Hysteresis in magnetism: for physicists, materials scientists, and engineers. Academic Press. (Cited on page 6.); Bezanson, J., Edelman, A., Karpinski, S., and Shah, V. B. (2017). Julia: A fresh approach to numerical computing. SIAM Review, 59(1):65–98. (Cited on pages 2 and 64.); Bills, A., Sripad, S., Fredericks, W. L., Guttenberg, M., Charles, D., Frank, E., and Viswanathan, V. (2020). Universal battery performance and degradation model for electric aircraft. arXiv:2008.01527v2. https://arxiv.org/abs/2008.01527Test. (Cited on page 43.); Blanchard, O. J. and Summers, L. H. (1992). Hysteresis in unemployment. In Economic Models of Trade Unions, chapter Unions and Macroeconimic Performance, pages 235–242. Springer, Dordrecht. (Cited on page 6.); Bojorquez Mora, J., Tolentino, D., Ruíz, S. E., and Bojorquez, E. (2016). Diseño sísmico preliminar de edificios de concreto reforzado usando redes neuronales artificiales. Concreto y Cemento. Investigación y Desarrollo, 7:60 – 78. (Cited on page 41.); Bonnaffé, W., Sheldon, B., and Coulson, T. (2021). Neural ordinary differential equations for ecological and evolutionary time-series analysis. Methods in Ecology and Evolution, 12. (Cited on page 43.); Borja, R. I. (2013). Plasticity: modeling & computation. Springer. (Cited on pages 17 and 18.); Bouc, R. (1971). Modèle mathématique d’hystérésis. Acta Acustica United with Acustica, 24(1):16–25. (Cited on pages 10, 45, and 48.); Bousias, S., Verzeletti, G., Fardis, M., and Gutierrez, E. (1995). Load-path effects in column biaxial bending with axial force. Journal of Engineering Mechanics, 121(5):596–605. (Cited on page 34.); Brewick, P., Masri, S., Carboni, B., and Lacarbonara, W. (2016). Data-based nonlinear identification and constitutive modeling of hysteresis in NiTiNOL and steel strands. Journal of Engineering Mechanics, 142(12). (Cited on page 39.); Bridgman, P. W. (1953). The effect of pressure on the tensile properties of several metals and other materials. Journal of Applied Physics, 24(5):560–570. (Cited on pages 19 and 20.); Bruno, P., Bayreuther, G., Beauvillain, P., Chappert, C., Lugert, G., Renard, D., Renard, J. P., and Seiden, J. (1990). Hysteresis properties of ultrathin ferromagnetic films. Journal of Applied Physics, 68(11):5759–5766. (Cited on page 6.); Cha, Y.-J., Choi, W., and B¨uy¨uk¨ozt¨urk, O. (2017). Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 32(5):361–378. (Cited on page 40.); Chan, R., Albermani, F., and Williams, M. (2009). Evaluation of yielding shear panel device for passive energy dissipation. Journal of Constructional Steel Research, 65(2):260–268. (Cited on pages 36 and 37.); Chan, R., Yuen, J., Lee, E., and Arashpour, M. (2015). Application of nonlinearautoregressive-exogenous model to predict the hysteretic behaviour of passive control systems. Engineering Structures, 85:1–10. (Cited on page 41.); Charalampakis, A. (2015). The response and dissipated energy of Bouc-Wen hysteretic model revisited. Archive of Applied Mechanics, 85(9-10):1209–1223. (Cited on pages 11 and 38.); Charalampakis, A. and Dimou, C. (2010). Identification of Bouc-Wen hysteretic systems using particle swarm optimization. Computers and Structures, 88(21-22):1197–1205. (Cited on page 37.); Charalampakis, A. and Koumousis, V. (2008). Identification of Bouc-Wen hysteretic systems by a hybrid evolutionary algorithm. Journal of Sound and Vibration, 314(3-5):571–585. (Cited on page 37.); Charalampakis, A. and Koumousis, V. (2009). A Bouc-Wen model compatible with plasticity postulates. Journal of Sound and Vibration, 322(4):954–968. (Cited on page 38.); Chassiakos, A. and Masri, S. F. (1991). Identification of the internal forces of structural systems using feedforward multilayer networks. Computing Systems in Engineering, 2(1):125–134. (Cited on page 41.); Chen, G. (2021). Recurrent neural networks (RNNs) learn the constitutive law of viscoelasticity. Computational Mechanics, 67(3):1009–1019. (Cited on page 41.); Chen, R. T. Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D. K. (2018). Neural ordinary differential equations. In Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., and Garnett, R., editors, Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc. (Cited on pages 30 and 42.); Chen, Z., Liu, J., and Yu, Y. (2017). Experimental study on interior connections in modular steel buildings. Engineering Structures, 147:625–638. (Cited on pages 35 and 36.); Chojaczyk, A., Teixeira, A., Neves, L., Cardoso, J., and Guedes Soares, C. (2015). Review and application of artificial neural networks models in reliability analysis of steel structures. Structural Safety, 52(PA):78 – 89. (Cited on page 40.); Chopra, A. K. (2012). Dynamics of structures. Pearson Education. (Cited on page 3.); Clough, W. and Penzien, J. (2003). Dynamics of structures. Computers & Structures, Inc. (Cited on pages 3 and 16.); Couchaux, M., Alhasawi, A., and Ben Larbi, A. (2020). Monotonic and cyclic tests on beam to column bolted connections with thermal insulation layer. Engineering Structures, 204. (Cited on page 35.); Dandekar, R. and Barbastathis, G. (2020). Quantifying the effect of quarantine control in COVID-19 infectious spread using machine learning. medRxiv. (Cited on page 43.); De Brouwer, E., Simm, J., Arany, A., and Moreau, Y. (2019). GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series. arXiv:1905.12374v2. https://arxiv.org/abs/1905.12374Test. (Cited on page 43.); De Jaegher, B., Larumbe, E., De Schepper, W., Verliefde, A., and Nopens, I. (2020). Colloidal fouling in electrodialysis: A neural differential equations model. Separation and Purification Technology, 249. (Cited on page 43.); Ding, Y. and Liu, Y. (2020). Cyclic tests of assembled self-centering buckling-restrained braces with pre-compressed disc springs. Journal of Constructional Steel Research, 172. (Cited on page 36.); Ding, Y. and Zhao, C. (2021). Cyclic tests for assembled X-shaped buckling restrained brace using two unconnected steel plate braces. Journal of Constructional Steel Research, 182. (Cited on page 36.); Dormand, J. and Prince, P. (1980). A family of embedded Runge-Kutta formulae. Journal of Computational and Applied Mathematics, 6(1):19–26. (Cited on page 29.); Dosi, G., Pereira, M. C., Roventini, A., and Virgillito, M. E. (2018). Causes and consequences of hysteresis: aggregate demand, productivity, and employment. Industrial and Corporate Change, 27(6):1015–1044. (Cited on page 6.); Dowell, R., Seible, F., and Wilson, E. (1998). Pivot hysteresis model for reinforced concrete members. ACI Structural Journal, 95(5):607–617. (Cited on page 8.); Drucker, D. C. (1950). Some implications of work hardening and ideal plasticity. Quarterly of Applied Mathematics, 7(4):411–418. (Cited on page 19.); Drucker, D. C. (1957). A definition of stable inelastic material. Technical Report 2, Brown University, Providence, Rhode Island. (Cited on page 19.); Dupont, E., Doucet, A., and Teh, Y. W. (2019). Augmented Neural ODEs. arXiv:1904.01681. https://arxiv.org/abs/1904.01681Test. (Cited on page 49.); Erlicher, S. and Point, N. (2004). Thermodynamic admissibility of Bouc-Wen type hysteresis models. Comptes Rendus Mecanique, 332(1):51–57. (Cited on pages 12 and 38.); Fang, Q., Qiu, H., Sun, J., Dal Lago, B., and Jiang, H. (2021). Performance study of precast reinforced concrete shear walls with steel columns containing friction-bearing devices. Archives of Civil and Mechanical Engineering, 21(3). (Cited on page 33.); Farrokh, M., Dizaji, M., and Joghataie, A. (2015). Modeling hysteretic deteriorating behavior using generalized Prandtl neural network. Journal of Engineering Mechanics, 141(8). (Cited on page 41.); Ferreira, F., Shamass, R., Limbachiya, V., Tsavdaridis, K., and Martins, C. (2022). Lateral-torsional buckling resistance prediction model for steel cellular beams generated by artificial neural networks (ANN). Thin-Walled Structures, 170. (Cited on page 40.); Fiorino, L., Shakeel, S., and Landolfo, R. (2020). Seismic behaviour of a bracing system for LWS suspended ceilings: Preliminary experimental evaluation through cyclic tests. Thin-Walled Structures, 155. (Cited on page 32.); Foliente, G. C. (1995). Hysteresis modeling of wood joints and structural systems. Journal of Structural Engineering, 121(6):1013–1022. (Cited on pages 10 and 11.); Folz, B. and Filiatrault, A. (2001). Cyclic analysis of wood shear walls. Journal of Structural Engineering, 127(4):433–441. (Cited on page 8.); Gharari, S. and Razavi, S. (2018). A review and synthesis of hysteresis in hydrology and hydrological modeling: Memory, path-dependency, or missing physics? Journal of Hydrology, 566:500–519. (Cited on page 7.); Ghobarah, A., Korol, R. M., and Osman, A. (1992). Cyclic behavior of extended endplate joints. Journal of Structural Engineering, 118(5):1333–1353. (Cited on pages 6, 32, and 34.); Guglielmino, E., Sireteanu, T., Stammers, C. W., Ghita, G., and Giuclea, M. (2008). Semi-active suspension control: improved vehicle ride and road friendliness. Springer. (Cited on page 7.); Guo, L., Wang, J., Wang, W., and Wang, C. (2020). Cyclic tests and analyses of extended endplate composite connections to CFDST columns. Journal of Constructional Steel Research, 167. (Cited on pages 35 and 37.); Górski, J., Klepka, A., Dziedziech, K., Mrówka, J., Radecki, R., and Dworakowski, Z. (2020). Identification of the stick and slip motion between contact surfaces using artificial neural networks. Nonlinear Dynamics, 100(1):225–242. (Cited on pages 39 and 41.); Haghighat, E., Raissi, M., Moure, A., Gomez, H., and Juanes, R. (2021). A physicsinformed deep learning framework for inversion and surrogate modeling in solid mechanics. Computer Methods in Applied Mechanics and Engineering, 379. (Cited on page 43.); Haykin, S. (2009). Neural networks and learning machines. Pearson Education India. (Cited on pages 23, 24, 27, and 28.); Herrera, J., Bedoya-Ruíz, D., and Hurtado, J. (2020). Performance-based seismic assessment of precast ferrocement walls for one and two-storey housing. Engineering Structures, 214. (Cited on pages 37 and 69.); Herrera, J. P., Bedoya-Ruíz, D., and Hurtado, J. E. (2018). Seismic behavior of recycled plastic lumber walls: An experimental and analytical research. Engineering Structures, 177:566–578. (Cited on pages 36, 37, 65, and 69.); Hou, H., Wang, C., Qu, B., and Liang, Y. (2021a). Cyclic testing of bolted base connections for wide-flange columns. Engineering Structures, 235. (Cited on page 35.); Hou, H., Yan, X., Qu, B., Du, Z., and Lu, Y. (2021b). Cyclic tests of steel tee energy absorbers for precast exterior wall panels in steel building frames. Engineering Structures, 242. (Cited on page 33.); Hu, Y., Zhao, J., Zhang, D., and Chen, Y. (2019). Seismic behavior of concretefilled double-skin steel tube/moment-resisting frames with beam-only-connected precast reinforced concrete shear walls. Archives of Civil and Mechanical Engineering, 19(4):967–980. (Cited on page 33.); Hu, Y., Zhao, J., Zhang, D., and Zhang, H. (2020). Cyclic tests of fully prefabricated concrete-filled double-skin steel tube/moment-resisting frames with beam-only-connected steel plate shear walls. Thin-Walled Structures, 146. (Cited on page 33.); Huang, C. and Huang, S. (2020). Predicting capacity model and seismic fragility estimation for RC bridge based on artificial neural network. Structures, 27:1930 – 1939. (Cited on page 40.); Huang, G.-B., Zhu, Q.-Y., and Siew, C.-K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70(1):489–501. (Cited on page 91.); Ibarra, L. F., Medina, R. A., and Krawinkler, H. (2005). Hysteretic models that incorporate strength and stiffness deterioration. Earthquake Engineering & Structural Dynamics, 34(12):1489–1511. (Cited on page 8.); Ikhouane, F. and Rodellar, J. (2005a). On the hysteretic Bouc-Wen model part I: Forced limit cycle characterization. Nonlinear Dynamics, 42(1):63–78. (Cited on page 38.); Ikhouane, F. and Rodellar, J. (2005b). Physical consistency of the hysteretic Bouc-Wen model. IFAC Proceedings Volumes, 38(1):874–879. (Cited on pages 12, 15, 16, and 38.); Ikhouane, F. and Rodellar, J. (2007). Systems with hysteresis: analysis, identification and control using the Bouc-Wen model. John Wiley & Sons. (Cited on pages 6, 13, 16, and 38.); Ikhouane, F., Rodellar, J., and Hurtado, J. (2006). Analytical characterization of hysteresis loops described by the Bouc-Wen model. Mechanics of Advanced Materials and Structures, 13(6):463–472. (Cited on page 38.); Il’iushin, A. (1961). On the postulate of plasticity. Journal of Applied Mathematics and Mechanics, 25(3):746–752. (Cited on page 20.); Ismail, M., Ikhouane, F., and Rodellar, J. (2009). The hysteresis Bouc-Wen model, a survey. Archives of Computational Methods in Engineering, 16(2):161–188. (Cited on page 16.); Ismail, M., Rodellar, J., and Ikhouane, F. (2010). An innovative isolation device for aseismic design. Engineering Structures, 32(4):1168–1183. (Cited on page 37.); Jia, X., Willard, J., Karpatne, A., Read, J., Zwart, J., Steinbach, M., and Kumar, V. (2019). Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles, pages 558–566. (Cited on page 43.); Jones, R. E., Frankel, A. L., and Johnson, K. L. (2021). A neural ordinary differential equation framework for modeling inelastic stress response via internal state variables. arXiv:2111.14714v1. https://arxiv.org/abs/2111.14714Test. (Cited on page 43.); Kadeethum, T., Jørgensen, T., and Nick, H. (2020). Physics-informed neural networks for solving nonlinear diffusivity and Biot’s equations. PLoS ONE, 15(5). (Cited on page 43.); Karpatne, A., Watkins, W., Read, J., and Kumar, V. (2017). Physics-guided neural networks (PGNN): An application in lake temperature modeling. arXiv:1710.11431v2. https://arxiv.org/abs/1710.11431v2Test. (Cited on page 31.); Khajekaramodin, A., Rowhanimanesh, A., Akbarzadeh, M., and Haji-Kazemi, H. (2007). Semi-active control of structures using a neuro-inverse model of MR dampers. In First joint congress on fuzzy and intelligent systems. (Cited on pages 39 and 41.); Khalid, M., Yusof, R., Joshani, M., Selamat, H., and Joshani, M. (2014). Nonlinear identification of a magneto-rheological damper based on dynamic neural networks. Computer-Aided Civil and Infrastructure Engineering, 29(3):221–233. (Cited on page 41.); Kim, T. D., Luo, T. Z., Pillow, J. W., and Brody, C. D. (2021). Inferring latent dynamics underlying neural population activity via neural differential equations. In Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 5551–5561. PMLR. (Cited on page 42.); Kim, W., El-Attar, A., and White, N. (1998). Small-scale modeling techniques for reinforced concrete structures subjected to seismic loads. Tecnichal report NCEER-88-0041, National Center for Earthquake Engineering Research. (Cited on page 32.); Kingma, D. P. and Ba, J. (2017). ADAM: A method for stochastic optimization. arXiv:1412.6980v9. https://arxiv.org/abs/1412.6980Test. (Cited on page 59.); Koeppe, A., Bamer, F., Selzer, M., Nestler, B., and Markert, B. (2021). Explainable artificial intelligence for mechanics: physics-informing neural networks for constitutive models. arXiv:2104.10683v4. https://arxiv.org/abs/2104.10683Test. (Cited on page 40.); Kramer, B. P. and Fussenegger, M. (2005). Hysteresis in a synthetic mammalian gene network. Proceedings of the National Academy of Sciences, 102(27):9517–9522. (Cited on page 6.); Kunnath, S., Mander, J., and Fang, L. (1997). Parameter identification for degrading and pinched hysteretic structural concrete systems. Engineering Structures, 19(3):224–232. (Cited on page 37.); Kwok, N., Ha, Q., Nguyen, M., Li, J., and Samali, B. (2007). Bouc-Wen model parameter identification for a MR fluid damper using computationally efficient GA. ISA Transactions, 46(2):167–179. (Cited on page 37.); Lai, Z., Mylonas, C., Nagarajaiah, S., and Chatzi, E. (2021). Structural identification with physics-informed neural ordinary differential equations. Journal of Sound and Vibration, 508. (Cited on pages 44 and 73.); Latour, M., D’Aniello, M., Zimbru, M., Rizzano, G., Piluso, V., and Landolfo, R. (2018). Removable friction dampers for low-damage steel beam-to-column joints. Soil Dynamics and Earthquake Engineering, 115:66–81. (Cited on page 36.); Lee, C.-H., Kim, J., Kim, D.-H., Ryu, J., and Ju, Y. (2016). Numerical and experimental analysis of combined behavior of shear-type friction damper and non-uniform strip damper for multi-level seismic protection. Engineering Structures, 114:75–92. (Cited on pages 35 and 37.); Li, C., Bi, K., Hao, H., Zhang, X., and Van Tin, D. (2019a). Cyclic test and numerical study of precast segmental concrete columns with BFRP and TEED. Bulletin of Earthquake Engineering, 17(6):3475–3494. (Cited on pages 34 and 36.); Li, W., Bazant, M., and Zhu, J. (2021). A physics-guided neural network framework for elastic plates: Comparison of governing equations-based and energy-based approaches. Computer Methods in Applied Mechanics and Engineering, 383. (Cited on page 43.); Li, Z., Albermani, F., Chan, R., and Kitipornchai, S. (2011). Pinching hysteretic response of yielding shear panel device. Engineering Structures, 33(3):993–1000. (Cited on page 37.); Li, Z., Shu, G., and Huang, Z. (2019b). Development and cyclic testing of an innovative shear-bending combined metallic damper. Journal of Constructional Steel Research, 158:28–40. (Cited on page 35.); Lim, J. and Psaltis, D. (2022). MaxwellNet: Physics-driven deep neural network training based on Maxwell’s equations. APL Photonics, 7(1). (Cited on page 43.); Lin, Y.-Z., Nie, Z.-H., and Ma, H.-W. (2017). Structural damage detection with automatic feature-extraction through deep learning. Computer-Aided Civil and Infrastructure Engineering, 32(12):1025–1046. (Cited on page 40.); Ma, Y., Dixit, V., Innes, M., Guo, X., and Rackauckas, C. (2021). A comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions. arXiv:1812.01892v2. https://arxiv.org/abs/1812.01892Test. (Cited on page 30.); Mander, J., Priestley, M., and Park, R. (1983). Behaviour of ductile hollow reinforced concrete columns. Bulletin of the New Zealand National Society for Earthquake Engineering, 16(4):273–290. (Cited on page 34.); Masi, F., Stefanou, I., Vannucci, P., and Maffi-Berthier, V. (2021). Thermodynamicsbased artificial neural networks for constitutive modeling. Journal of the Mechanics and Physics of Solids, 147. (Cited on page 40.); Masri, S., Chassiakos, A., and Caughey, T. (1993). Identification of nonlinear dynamic systems using neural networks. Journal of Applied Mechanics, Transactions ASME, 60(1):123 – 133. (Cited on page 41.); Masri, S., Chassiakos, S., and Caughey, T. (1992). Structure-unknown non-linear dynamic systems: identification through neural networks. Smart Materials and Structures, 1(1):45–56. (Cited on page 41.); Massaroli, S., Poli, M., Park, J., Yamashita, A., and Asama, H. (2020). Dissecting Neural ODEs. arXiv.2002.08071v3. https://arxiv.org/abs/2002.08071Test. (Cited on page 49.); Menegotto, M. and Pinto, P. E. (1973). Method of analysis for cyclically loaded R.C. plane frames including changes in geometry and non-elastic behavior of elements under combined normal force and bending. In Proc. of IABSE symposium on resistance and ultimate deformability of structures acted on by well defined repeated loads, pages 15–22. (Cited on page 10.); Montaño, J., Maury, R., Almazán, J. L., Estrella, X., and Guindos, P. (2020). Development of an amplified added stiffening and damping system for wood-frame shear walls. Latin American Journal of Solids and Structures, 17. (Cited on page 35.); Morandi, P., Hak, S., and Magenes, G. (2018). Performance-based interpretation of in-plane cyclic tests on RC frames with strong masonry infills. Engineering Structures, 156:503–521. (Cited on page 32.); Morfidis, K. and Kostinakis, K. (2018). Approaches to the rapid seismic damage prediction of R/C buildings using artificial neural networks. Engineering Structures, 165:120– 141. (Cited on page 40.); Mostaghel, N. (1999). Analytical description of pinching, degrading hysteretic systems. Journal of Engineering Mechanics, 125(2):216–224. (Cited on pages 1 and 8.); Muralidhar, N., Bu, J., Cao, Z., He, L., Ramakrishnan, N., Tafti, D., and Karpatne, A. (2020). Physics-guided deep learning for drag force prediction in dense fluid-particulate systems. Big Data, 8(5):431–449. (Cited on page 43.); Muralidhar, N., Islam, M., Marwah, M., Karpatne, A., and Ramakrishnan, N. (2019). Incorporating prior domain knowledge into deep neural networks. In Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, pages 36–45. (Cited on page 43.); Nguyen, H., Dao, N., and Shin, M. (2021). Prediction of seismic drift responses of planar steel moment frames using artificial neural network and extreme gradient boosting. Engineering Structures, 242. (Cited on page 39.); Nikbakht, S., Anitescu, C., and Rabczuk, T. (2021). Optimizing the neural network hyperparameters utilizing genetic algorithm. Journal of Zhejiang University: Science A, 22(6):407–426. (Cited on page 43.); Ning, C.-L., Wang, L., and Du, W. (2019). A practical approach to predict the hysteresis loop of reinforced concrete columns failing in different modes. Construction and Building Materials, 218:644–656. (Cited on page 39.); Niu, S., Luo, Y., Fei, S., Montagnani, L., Bohrer, G., Janssens, I. A., Gielen, B., Rambal, S., Moors, E., and Matteucci, G. (2011). Seasonal hysteresis of net ecosystem exchange in response to temperature change: patterns and causes. Global Change Biology, 17(10):3102–3114. (Cited on page 6.); Noori, H. R. (2014). Hysteresis phenomena in biology, volume 1. Springer. (Cited on page 6.); Noori, M. (1984). Random vibration of degrading systems with general hysteretic behavior. PhD thesis, University of Virginia. (Cited on pages 7 and 44.); Oldfield, M., Ouyang, H., and Mottershead, J. (2005). Simplified models of bolted joints under harmonic loading. Computers and Structures, 84(1-2):25–33. (Cited on page 37.); Ortiz, G. A., Alvarez, D. A., and Bedoya-Ruíz, D. (2015). Identification of Bouc-Wen type models using the Transitional Markov Chain Monte Carlo method. Computers & Structures, 146:252–269. (Cited on pages 37 and 69.); Pachoumis, D., Galoussis, E., Kalfas, C., and Efthimiou, I. (2010). Cyclic performance of steel moment-resisting connections with reduced beam sections - experimental analysis and finite element model simulation. Engineering Structures, 32(9):2683–2692. (Cited on pages 35 and 36.); Pang, W., Rosowsky, D., Pei, S., and Van de Lindt, J. (2007). Evolutionary parameter hysteretic model for wood shear walls. Journal of Structural Engineering, 133(8):1118–1129. (Cited on page 8.); Park, Y.-J. and Ang, A. H.-S. (1985). Mechanistic seismic damage model for reinforced concrete. Journal of Structural Engineering, 111(4):722–739. (Cited on page 46.); Ortiz, G. A., Alvarez, D. A., and Bedoya-Ruíz, D. (2013). Identification of Bouc-Wen type models using multi-objective optimization algorithms. Computers & Structures, 114-115:121–132. (Cited on pages 37 and 69.); Ozcebe, G. and Saatcioglu, M. (1989). Hysteretic shear model for reinforced concrete members. Journal of Structural Engineering, 115(1):132–148. (Cited on page 8.); Paevere, P. J. (2002). Full-scale testing, modelling and analysis of light-frame structures under lateral loading. PhD thesis, Department of Civil and Environmental Engineering - University of Melbourne. (Cited on page 32.); Park, R. and Thompson, K. J. (1977). Cyclic load tests on prestressed and partially prestressed beam-column joints. PCI Journal, 22(5):84–110. (Cited on page 36.); Pessiki, S. P., Conley, C., Gergely, P., and White, R. N. (1990). Seismic behavior of lightly-reinforced concrete column and beam-column joint details. Technical report NCEER-90-0014, National Center for Earthquake Engineering Research. (Cited on page 32.); Pozo, F. and Zapateiro, M. (2015). On the passivity of hysteretic systems with double hysteretic loops. Materials, 8(12):8414–8422. (Cited on page 14.); Pradhan, N., Paraskeva, T., and Dimitrakopoulos, E. (2020). Quasi-static reversed cyclic testing of multi-culm bamboo members with steel connectors. Journal of Building Engineering, 27. (Cited on page 36.); Qiu, F., Li, W., Pan, P., and Qian, J. (2002). Experimental tests on reinforced concrete columns under biaxial quasi-static loading. Engineering Structures, 24(4):419–428. (Cited on page 34.); Rachedi, M., Matallah, M., and Kotronis, P. (2021). Seismic behavior & risk assessment of an existing bridge considering soil-structure interaction using artificial neural networks. Engineering Structures, 232. (Cited on page 39.); Rackauckas, C., Ma, Y., Martensen, J., Warner, C., Zubov, K., Supekar, R., Skinner, D., and Ramadhan, A. (2020). Universal differential equations for scientific machine learning. arXiv:2001.04385v3. https://arxiv.org/abs/2001.04385Test. (Cited on pages 29 and 64.); Rackauckas, C. and Nie, Q. (2017). Differentialequations.jl-a performant and feature-rich ecosystem for solving differential equations in julia. Journal of Open Research Software, 5(1). (Cited on page 64.); Raissi, M., Perdikaris, P., and Karniadakis, G. E. (2017a). Physics informed deep learning (part I): Data-driven solutions of nonlinear partial differential equations. arXiv:1711.10561v1. https://arxiv.org/abs/1711.10561Test. (Cited on pages 31 and 42.); Raissi, M., Perdikaris, P., and Karniadakis, G. E. (2017b). Physics informed deep learning (part II): Data-driven discovery of nonlinear partial differential equations. arXiv.1711.10566v1. https://arxiv.org/abs/1711.10566Test. (Cited on page 42.); Raissi, M., Yazdani, A., and Karniadakis, G. E. (2018). Hidden fluid mechanics: A Navier-Stokes informed deep learning framework for assimilating flow visualization data. arXiv:1808.04327v1. https://arxiv.org/abs/1808.04327Test. (Cited on pages 39 and 43.); Ramberg, W. and Osgood, W. (1943). Description of stress-strain curves by three parameters. NASA Technical Note 902. (Cited on pages 1 and 10.); Reggiani Manzo, N. and Vassiliou, M. (2022). Cyclic tests of a precast restrained rocking system for sustainable and resilient seismic design of bridges. Engineering Structures, 252. (Cited on page 34.); Roehrl, M., Runkler, T., Brandtstetter, V., Tokic, M., and Obermayer, S. (2020). Modeling system dynamics with physics-informed neural networks based on Lagrangian mechanics. In IFAC-PapersOnLine, volume 53, pages 9195 – 9200. (Cited on page 43.); Sadeghi Eshkevari, S., Takáˇc, M., Pakzad, S., and Jahani, M. (2021). DynNet: Physics based neural architecture design for nonlinear structural response modeling and prediction. Engineering Structures, 229. (Cited on pages 41 and 73.); Saiidi, M. and Sozen, M. A. (1979). Simple and complex models for nonlinear seismic response of reinforced concrete structures. Technical report, University of Illinois. (Cited on page 8.); Samaniego, E., Anitescu, C., Goswami, S., Nguyen-Thanh, V., Guo, H., Hamdia, K., Zhuang, X., and Rabczuk, T. (2020). An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications. Computer Methods in Applied Mechanics and Engineering, 362. (Cited on page 43.); Sarti, F., Palermo, A., and Pampanin, S. (2016). Quasi-static cyclic testing of twothirds scale unbonded posttensioned rocking dissipative timber walls. Journal of Structural Engineering (United States), 142(4). (Cited on page 36.); Sasani, M. and Popov, E. (2001). Seismic energy dissipators for RC panels: Analytical studies. Journal of Engineering Mechanics, 127(8):835–843. (Cited on pages 11, 19, and 38.); Sengupta, P. and Li, B. (2017). Hysteresis modeling of reinforced concrete structures: State of the art. ACI Structural Journal, 114(1):25–38. (Cited on page 37.); Seo, J.-M., Choi, I.-K., and Lee, J.-R. (1999). Static and cyclic behavior of wooden frames with tenon joints under lateral load. Journal of Structural Engineering, 125(3):344–349. (Cited on page 6.); Shen, Y., Liu, X., Li, Y., and Li, J. (2021). Cyclic tests of precast post-tensioned concrete filled steel tubular (PCFT) columns with internal energy-dissipating bars. Engineering Structures, 229. (Cited on page 34.); Shi, J., Guo, L., and Qu, B. (2022). In-plane cyclic tests of double-skin composite walls with concrete-filled steel tube boundary elements. Engineering Structures, 250. (Cited on page 33.); Simpson, T., Dervilis, N., and Chatzi, E. N. (2021). Machine learning approach to model order reduction of nonlinear systems via autoencoder and LSTM networks. arXiv:2109.11213v1. https://arxiv.org/abs/2109.11213Test. (Cited on pages 39 and 41.); Sivaselvan, M. V. and Reinhorn, A. M. (2000). Hysteretic models for deteriorating inelastic structures. Journal of Engineering Mechanics, 126(6):633–640. (Cited on pages 7, 10, and 46.); Stoffel, M., Bamer, F., and Markert, B. (2018). Artificial neural networks and intelligent finite elements in non-linear structural mechanics. Thin-Walled Structures, 131:102–106. (Cited on page 40.); Sucuoglu, H. and Erberik, A. (2004). Energy-based hysteresis and damage models for deteriorating systems. Earthquake Engineering & Structural Dynamics, 33(1):69–88. (Cited on page 8.); Takeda, T., Sozen, M. A., and Nielsen, N. N. (1970). Reinforced concrete response to simulated earthquakes. Journal of the Structural Division, 96(12):2257–2573. (Cited on pages 1 and 8.); Tanabashi, R. and Kaneta, K. (1962). On the relation between the restoring force characteristics of structures and the pattern of earthquake ground motion. In Proceedings of Japan National Symposium of Earthquake Engineering, pages 1085–1104. (Cited on page 8.); Tapia Hernández, E., Santiago Flores, A., Guerrero Bobadilla, H., and Chávez Cano, M. M. (2020). Comportamiento experimental de conexiones de marcos de acero ante demandas sísmicas. Ingeniería Sísmica, 103:37–55. (Cited on page 34.); Tazarv, M. and Saiidi, M. (2015). UHPC-filled duct connections for accelerated bridge construction of RC columns in high seismic zones. Engineering Structures, 99:413–422. (Cited on page 34.); Thornton, S. T. and Marion, J. B. (2004). Classical dynamics of particles and systems. Thomson Brooks/Cole. (Cited on page 12.); Thyagarajan, R. S. (1989). Modeling and analysis of hysteretic structural behavior. PhD thesis, California Institute of Technology, Pasadena, California. (Cited on page 11.); Tsitouras, C. (2011). Runge-Kutta pairs of order 5(4) satisfying only the first column simplifying assumption. Computers & Mathematics with Applications, 62(2):770–775. (Cited on page 29.); Tullini, N. and Minghini, F. (2016). Grouted sleeve connections used in precast reinforced concrete construction - experimental investigation of a column-to-column joint. Engineering Structures, 127:784–803. (Cited on page 34.); Tullini, N. and Minghini, F. (2020). Cyclic test on a precast reinforced concrete columnto-foundation grouted duct connection. Bulletin of Earthquake Engineering, 18(4):1657–1691. (Cited on page 34.); Veletsos, A., Newmark, N., and Chelapati, C. (1965). Deformation spectra for elastic and elastoplastic systems subjected to ground shock and earthquake motions. In Van Roekel, J., editor, Proceedings of the 3rd world conference on earthquake engineering, volume 2, pages 663–682. (Cited on page 8.); Verner, J. H. (2010). Numerically optimal Runge-Kutta pairs with interpolants. Numerical Algorithms, 53(2-3):383–396. (Cited on page 29.); Visintin, A. (1994). Differential models of hysteresis, volume 111. Springer. (Cited on page 6.); Wang, C.-H. and Chang, S.-Y. (2007). Development and validation of a generalized biaxial hysteresis model. Journal of Engineering Mechanics, 133(2):141–152. (Cited on page 37.); Wang, C.-H. and Wen, Y.-K. (2000). Evaluation of pre-Northridge low-rise steel buildings. I: Modeling. Journal of Structural Engineering, 126(10):1160–1168. (Cited on page 37.); Wang, D. and Liao, W. (2005). Modeling and control of magnetorheological fluid dampers using neural networks. Smart Materials and Structures, 14(1):111 – 126. (Cited on page 41.); Wang, J., Wang, W., Xiao, Y., and Yu, B. (2019). Cyclic test and numerical analytical assessment of cold-formed thin-walled steel shear walls using tube truss. Thin-Walled Structures, 134:442–459. (Cited on page 33.); Wang, R. and Yu, R. (2021). Physics-guided deep learning for dynamical systems. arXiv:107.01272v4. https://arxiv.org/abs/2107.01272v4Test. (Cited on page 58.); Wen, Y.-K. (1976). Method for random vibration of hysteretic systems. Journal of the Engineering Mechanics Division, 102(2):249–263. (Cited on pages 1 and 10.); Whalen, E. J. (2021). Enhancing surrogate models of engineering structures with graphbased and physics-informed learning. PhD thesis, Massachusetts Institute of Technology. (Cited on page 43.); Williams, M. and Albermani, F. (2003). Monotonic and cyclic tests on shear diaphragm dissipators for steel frames. Civil engineering research bulletin 23, University of Queensland. (Cited on page 36.); Xie, S., Zhang, Y., Chen, C., and Zhang, X. (2013). Identification of nonlinear hysteretic systems by artificial neural network. Mechanical Systems and Signal Processing, 34(1-2):76–87. (Cited on page 39.); Yan, J.-B., Hu, H.-T., and Wang, T. (2021). Cyclic tests on concrete-filled composite plate shear walls with enhanced C-channels. Journal of Constructional Steel Research, 179. (Cited on page 33.); Yan, J.-B., Yan, Y.-Y., and Wang, T. (2020). Cyclic tests on novel steel-concretesteel sandwich shear walls with boundary CFST columns. Journal of Constructional Steel Research, 164. (Cited on page 33.); Yang, C. and Fan, J. (2021). Artificial neural network-based hysteresis model for circular steel tubes. Structures, 30:418–439. (Cited on page 39.); Yang, T.-S. and Popov, E. P. (1995). Behavior of pre-Northridge moment resisting steel connections. Earthquake Engineering Research Center, University of California. (Cited on pages 34 and 36.); Ye, J., Wang, X., Jia, H., and Zhao, M. (2015). Cyclic performance of cold-formed steel shear walls sheathed with double-layer wallboards on both sides. Thin-Walled Structures, 92:146–159. (Cited on page 33.); Yu, Y., Yao, H., and Liu, Y. (2020). Structural dynamics simulation using a novel physics-guided machine learning method. Engineering Applications of Artificial Intelligence, 96. (Cited on page 41.); Zeynalian, M., Ronagh, H. R., and Dux, P. (2012). Analytical description of pinching, degrading, and sliding in a bilinear hysteretic system. Journal of Engineering Mechanics, 138(11):1381–1387. (Cited on page 38.); Zhang, R., Liu, Y., and Sun, H. (2020a). Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling. Engineering Structures, 215. (Cited on page 44.); Zhang, R., Liu, Y., and Sun, H. (2020b). Physics-informed multi-LSTM networks for metamodeling of nonlinear structures. Computer Methods in Applied Mechanics and Engineering, 369. (Cited on page 44.); Zhao, Y., Noori, M., Altabey, W., and Awad, T. (2019). A comparison of three different methods for the identification of hysterically degrading structures using BWBN model. Frontiers in Built Environment, 4. (Cited on page 43.); https://repositorio.unal.edu.co/handle/unal/83560Test; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.coTest/

الإتاحة: https://repositorio.unal.edu.co/handle/unal/83560Test
https://repositorio.unal.edu.coTest/

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مؤتمر

Aplicación de métodos matemáticos a la identificación de sistemas estructurales

المؤلفون: Lozano Galant, José Antonio, Enrique, Castillo, Nogal Macho, María, Turmo Coderque, José

المساهمون: Universitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental, Universitat Politècnica de Catalunya. EC - Enginyeria de la Construcció

مصطلحات موضوعية: Àrees temàtiques de la UPC::Enginyeria civil::Materials i estructures, Àrees temàtiques de la UPC::Matemàtiques i estadística::Anàlisi numèrica::Mètodes numèrics, Structural engineering--Mathematical models, Método de la observabilidad, Identificación estructural, método matemático, Observability technique, Structural system Identification, Mathematical method, Enginyeria -- Estructures

وصف الملف: 10 p.

العلاقة: LOZANO-GALANT, J., Enrique, C., Nogal, M., Turmo, J. Aplicación de métodos matemáticos a la identificación de sistemas estructurales. A: Congreso de la Asociación Científico-Técnica del Hormigón Estructural. "Congreso de la de la Asociación Científico-Técnica del Hormigón Estructural (ACHE) 2017". La Coruña: 2017, p. 1-10.; http://hdl.handle.net/2117/107511Test

الإتاحة: http://hdl.handle.net/2117/107511Test

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8

Study of vibration period for concrete bridges in Panama

المؤلفون: Rodríguez, Stephania M, Gallardo, José M, Araúz, Osvaldo A

المصدر: I+D Tecnológico; Vol. 13, Núm. 1 (2017): Revista I+D Tecnológico; 86-93
Repositorio Institucional de documento digitales de acceso abierto de la UTP
Universidad Tecnológica de Panamá
instacron:U Tecnológica de Panamá

مصطلحات موضوعية: frequency, structural identification, bridge, vibration, frecuencia, identificación estructural, puente, vibración