https://hdl.handle.net/10481/17563 Fri, 17 Apr 2026 20:11:34 GMT 2026-04-17T20:11:34Z https://hdl.handle.net/10481/109317 Geometría Funicular y Proyectiva: Análisis estructural de construcciones históricas Suárez Medina, Francisco Javier; Boothby, Thomas E.; González Casares, José Antonio; Olmo García, Juan Carlos This paper presents a new graphic methodology for the structural analysis of domes and other surfaces of revolution, based on the combined use of funicular and projective geometry. The methodology is presented through its application to a hemispherical brick dome with a small thickness. The dome is considered a network of lines of latitude and longitude, and the equilibrium of this network is analyzed in both horizontal and vertical projections. To contrast the results obtained, the methodology has been applied to different structural situations whose analytical solution is known: complete hemisphere, hemisphere with oculus on the top, and hemisphere with lantern. The new methodology has also been applied to the analysis of structural situations different from the previous ones, by combining the following considerations: presence of the backfilling on the extrados of the dome, small inclinations of the reaction in the support, and small variations in the vertical position of the nodes in the simplified model considered. The tests included in the article have been selected to show all the possibilities of this methodology. Multiple solutions for the internal forces are obtained, whose equilibrium is guaranteed by this graphic methodology. https://hdl.handle.net/10481/109317 https://hdl.handle.net/10481/88999 Nonlinear Ultrasonics for Early Damage Detection Muñoz Beltrán, Rafael; Rus Carlborg, Guillermo; Bochud, Nicolás; Barnard, Daniel J.; Melchor Rodríguez, Juan Manuel; Chiachío Ruano, Juan; Chiachío Ruano, Manuel; Cantero Chinchilla, Sergio; Callejas Zafra, Antonio Manuel; Peralta, Laura; Bond, Leonard J. Structural Health Monitoring (SHM) is an emerging discipline that aims at improving the management of the life cycle of industrial components. The scope of this chapter is to present the integration of nonlinear ultrasonics with the Bayesian inverse problem as an appropriate tool to estimate the updated health state of a component taking into account the associated uncertainties. This updated information can be further used by prognostics algorithms to estimate the future damage stages. Nonlinear ultrasonics allows an early detection of damage moving forward the achievement of reliable predictions, while the inverse problem emerges as a rigorous method to extract the slight signature of early damage inside the experimental signals using theoretical models. The Bayesian version of the inverse problem allows measuring the underlying uncertainties, improving the prediction process. This chapter presents the fundamentals of nonlinear ultrasonics, their practical application for SHM, and the Bayesian inverse problem as a method to unveil damage and manage uncertainty. https://hdl.handle.net/10481/88999 https://hdl.handle.net/10481/88998 Model-based damage evaluation of layered CFRP structures Muñoz Beltrán, Rafael; Bochud, Nicolás; Rus Carlborg, Guillermo; Peralta, Laura; Melchor Rodríguez, Juan Manuel; Chiachío Ruano, Juan; Chiachío Ruano, Manuel; Bond, Leonard J. An ultrasonic evaluation technique for damage identification of layered CFRP structures is presented. This approach relies on a model-based estimation procedure that combines experimental data and simulation of ultrasonic damage-propagation interactions. The CFPR structure, a [0/90]4s lay-up, has been tested in an immersion through transmission experiment, where a scan has been performed on a damaged specimen. Most ultrasonic techniques in industrial practice consider only a few features of the received signals, namely, time of flight, amplitude, attenuation, frequency contents, and so forth. In this case, once signals are captured, an algorithm is used to reconstruct the complete signal waveform and extract the unknown damage parameters by means of modeling procedures. A linear version of the data processing has been performed, where only Young modulus has been monitored and, in a second nonlinear version, the first order nonlinear coefficient β was incorporated to test the possibility of detection of early damage. The aforementioned physical simulation models are solved by the Transfer Matrix formalism, which has been extended from linear to nonlinear harmonic generation technique. The damage parameter search strategy is based on minimizing the mismatch between the captured and simulated signals in the time domain in an automated way using Genetic Algorithms. Processing all scanned locations, a C-scan of the parameter of each layer can be reconstructed, obtaining the information describing the state of each layer and each interface. Damage can be located and quantified in terms of changes in the selected parameter with a measurable extension. In the case of the nonlinear coefficient of first order, evidence of higher sensitivity to damage than imaging the linearly estimated Young Modulus is provided. Authors are grateful to Dr. H. Schmutzler from the Institute of Polymers and Composites, TU Hamburg, Germany, for providing us the damaged CFRP plate. https://hdl.handle.net/10481/88998 https://hdl.handle.net/10481/88996 Nonlinear Ultrasonics for Early Damage Detection Muñoz Beltrán, Rafael; Rus Carlborg, Guillermo; Bochud, Nicolás; Barnard, Daniel J.; Melchor Rodríguez, Juan Manuel; Chiachío Ruano, Juan; Chiachío Ruano, Manuel; Cantero Chinchilla, Sergio; Callejas Zafra, Antonio Manuel; Peralta, Laura; Bond, Leonard J. Structural Health Monitoring (SHM) is an emerging discipline that aims at improving the management of the life cycle of industrial components. The scope of this chapter is to present the integration of nonlinear ultrasonics with the Bayesian inverse problem as an appropriate tool to estimate the updated health state of a component taking into account the associated uncertainties. This updated information can be further used by prognostics algorithms to estimate the future damage stages. Nonlinear ultrasonics allows an early detection of damage moving forward the achievement of reliable predictions, while the inverse problem emerges as a rigorous method to extract the slight signature of early damage inside the experimental signals using theoretical models. The Bayesian version of the inverse problem allows measuring the underlying uncertainties, improving the prediction process. This chapter presents the fundamentals of nonlinear ultrasonics, their practical application for SHM, and the Bayesian inverse problem as a method to unveil damage and manage uncertainty. https://hdl.handle.net/10481/88996