Reduction of Petri net maintenance modeling complexity via Approximate Bayesian Computation

This paper is part of the ENHAnCE ITN project (https://www.h2020-enhanceitn.eu/) funded by the European Union's Horizon 2020 research and innovation programme under the Marie SklodowskaCurie grant agreement No. 859957. The authors would like to thank the Lloyd's Register Foundation (LRF), a charitable foundation in the U.K. helping to protect life and property by supporting engineeringrelated education, public engagement, and the application of research. The authors gratefully acknowledge the support of these organizations which have enabled the research reported in this paper.The accurate modeling of engineering systems and processes using Petri nets often results in complex graph representations that are computationally intensive, limiting the potential of this modeling tool in real life applications. This paper presents a methodology to properly define the optimal structure and properties of a reduced Petri net that mimic the output of a reference Petri net model. The methodology is based on Approximate Bayesian Computation to infer the plausible values of the model parameters of the reduced model in a rigorous probabilistic way. Also, the method provides a numerical measure of the level of approximation of the reduced model structure, thus allowing the selection of the optimal reduced structure among a set of potential candidates. The suitability of the proposed methodology is illustrated using a simple illustrative example and a system reliability engineering case study, showing satisfactory results. The results also show that the method allows flexible reduction of the structure of the complex Petri net model taken as reference, and provides numerical justification for the choice of the reduced model structure.European Commission 859957Lloyd's Register Foundation (LRF), a charitable foundation in the U.K

Notas introductorias sobre fiabilidad estructural

Libro de apuntes de apoyo a la docencia para el curso "Fiabilidad y Daño Continuo" del Máster de Estructuras (código M63/56/1​)​.El material que se recoge en este documento está especialmente concebido para sentar las bases teóricas así como para reiterar acerca de los fundamentos matemáticos de la fiabilidad. Al mismo tiempo, los autores pretenden presentar un material que, en un futuro, puede llegar a ser un libro de texto sobre ingeniería de fiabilidad, en el cual se aborde la fiabilidad desde una perspectiva más amplia, con especial atención a la fiabilidad de sistemas. Finalmente, conviene recordar al alumno que debe ampliar y contrastar el contenido a través del material de referencia recomendado en el apartado de bibliografía

Robust optimal sensor configuration using the value of information

This paper is part of the SAFE-FLY project that has received funding from the European Union's Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie (Grant Agreement No. 721455). The authors acknowledge the support acquired by the Brazilian National Council of Research CNPq (Grant Agreement ID: 314168/2020-6).Sensing is the cornerstone of any functional structural health monitoring technology, with sensor number and placement being a key aspect for reliable monitoring. We introduce for the first time a robust methodology for optimal sensor configuration based on the value of information that accounts for (1) uncertainties from updatable and nonupdatable parameters, (2) variability of the objective function with respect to nonupdatable parameters, and (3) the spatial correlation between sensors. The optimal sensor configuration is obtained by maximizing the expected value of information, which leads to a cost-benefit analysis that entails model parameter uncertainties. The proposed methodology is demonstrated on an application of structural health monitoring in plate-like structures using ultrasonic guided waves. We show that accounting for uncertainties is critical for an accurate diagnosis of damage. Furthermore, we provide critical assessment of the role of both the effect of modeling and measurement uncertainties and the optimization algorithm on the resulting sensor placement. The results on the health monitoring of an aluminum plate indicate the effectiveness and efficiency of the proposed methodology in discovering optimal sensor configurations.European Union's Horizon 2020 Research and Innovation Programme 721455Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ) 314168/2020-

Notas sobre mécanica de materiales compuestos

Estas notas han sido preparadas como apoyo a los estudiantes del Máster en Ingeniería de Estructuras de la Universidad de Granada, con el objetivo de que encuentren en ellas un manual básico para adentrarse en el cálculo de materiales compuestos. También van dirigidos a aquellos estudiantes de ingeniería que deciden basar su proyecto fin de carrera en estos materiales

Curso de introducción a estructuras de fibra de carbono

Material de apoyo a la docencia.Actualmente la utilización de materiales avanzados de alta eficacia, como los composites de fibra de carbono o vidrio originarios de la industria aeronáutica y aeroespacial, se presenta como alternativa viable en el diseño de estructuras de ingeniería civil en las que las exigencias de ligereza, durabilidad y tiempo de construcción se convierten en aspectos críticos del diseño. Desde los últimos 10 años se está asistiendo a un aumento importante a nivel mundial de las aplicaciones de materiales avanzados en construcción, y en particular en estructuras de ingeniería y arquitectura civil: puentes, estructuras de arquitectura singular, estructuras offshore, sistemas de alma- cenamiento energético, etc. Estados Unidos, Japón, Suiza, Reino Unido y Dinamarca ente otros países tecnológicamente avanzados, cuentan ya con numerosos puentes y estructuras de ingeniería realizadas íntegramente con estos materiales. Así mismo, en estos países se ha creado una red empresarial en torno a los nuevos materiales que está suponiendo en ciertos casos un importante impulso económico y una revolución tecnológica en el sector de la construcción.Este curso es una introducción a la tecnología y diseño, y se plantea desde un punto de vista divulgativo y práctico de forma que el alumno no solo conozca una nueva tecnología sino además un novedoso sector de la industria con nuevas oportunidades.

Yield displacement charts for performance‑based seismic design

A new tool for seismic design is presented, called Yield Displacement Charts (YDC). As with its predecessors, the Yield Point Spectra (YPS) and the Yield Frequency Spectra (YFS), the YDC concept takes advantage of the simple features of yield displacement (uy), to use uy in a performance-based design instead of a force-based period-dependent approach. A self-contained and comprehensive approach to YPS and YFS is presented, enabling the novel aspect of YDC to be introduced: a tool for a multi-performance objective design that only depends on the location of the structure to be designed. Once the yield displacement chart has been calculated for a particular place, it can be used for the preliminary design of any structure. For a given value of yield displacement, the YFS are obtained from the Yield Displacement Chart. The suitability of the methodology proposed is illustrated by means of a simple case study of a concrete bridge pier.European Commission 82105

Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys

This work was supported by the contract OTRI-4408 between the University of Granada and the Royal Academy of Engineering of Spain financed by Ferrovial S.A. Eugenio Martinez Camara was supported by the Spanish Government fellowship programme Juan de la Cierva Incorporacion (IJC2018-036092-I).The United Nations Agenda 2030 established 17 Sustainable Development Goals (SDGs) as a guideline to guarantee a sustainable worldwide development. Recent advances in artificial intelligence and other digital technologies have already changed several areas of modern society, and they could be very useful to reach these sustainable goals. In this paper we propose a novel decision making model based on surveys that ranks recommendations on the use of different artificial intelligence and related technologies to achieve the SDGs. According to the surveys, our decision making method is able to determine which of these technologies are worth investing in to lead new research to successfully tackle with sustainability challenges.University of Granada - Ferrovial S.A. OTRI-4408Royal Academy of Engineering of Spain - Ferrovial S.A. OTRI-4408Spanish Government fellowship programme Juan de la Cierva Incorporacion IJC2018-036092-

Lamb wave-based damage indicator for plate-like structures

Structural health monitoring based on ultrasonics typically involves complex data analysis. Ultrasound monitoring based on Lamb waves techniques are extensively used nowadays due to their efficiency in exploring large areas with relatively small attenuation. In recent years, baseline based methods have been developed to identify structural damage based on the mismatch between the measured signal and the baseline one. To this end, complex time-frequency transformations are required to obtain signal features such as the time of arrival or the energy content, as indicators of damage onset and growth. Notwithstanding this, on-board applications require highly efficient processing techniques due to information storage and exchange limitations. This paper proposes a very high efficiency signal processing methodology to obtain a novel cumulative damage factor using Lamb wave raw data. The new methodology has been tested using ultrasonic and damage data from a fatigue test in carbon-epoxy composite laminates. The data is taken from NASA Prognostics data repository. In view of the results, the method is able to efficiently detect the onset and extent of damage from early stages of degradation. Moreover, the results demonstrate a remarkable agreement between the growth of delamination area and the predicted cumulative damage factor

Adaptive approximate Bayesian computation by subset simulation for structural model calibration

This work was supported by the SINDE (Research and Development System of the Catholic University of Santiago de Guayaquil, Ecuador) under project Cod. Pres #491/Cod. Int. #170. The first author would also like to thank the University of Granada (Spain) for hosting him during the course of this work. Finally, the authors thank the work of Berry et al. (2004) and Pratap and Pujol (2021) for their valuable set of data.This paper provides a new approximate Bayesian computation (ABC) algorithm with reduced hyper-parameter scaling and its application to nonlinear structural model calibration problems. The algorithm initially takes the ABC-SubSim algorithm structure and sequentially estimates the algorithm hyper-parameter by autonomous adaptation following a Markov chain approach, thus avoiding the error associated to modeler's choice for these hyper-parameters. The resulting algorithm, named A2BC-SubSim, simplifies the application of ABC-SubSim method for new users while ensuring better measure of accuracy in the posterior distribution and improved computational efficiency. A first numerical application example is provided for illustration purposes and to provide a comparative and sensitivity analysis of the algorithm with respect to initial ABC-SubSim algorithm. Moreover, the efficiency of the method is demonstrated in two nonlinear structural calibration case studies where the A2BC-SubSim is used as a tool to infer structural parameters with quantified uncertainty based on test data. The results confirm the suitability of the method to tackle with a real-life damage parameter inference and its superiority in relation to the original ABC-SubSim.SINDE (Research and Development System of the Catholic University of Santiago de Guayaquil, Ecuador) 491/Cod- 17

Training of physics-informed Bayesian neural networks with ABC-SS for prognostic of Li-ion batteries

The current surge in the need for Li-ion batteries to power electric vehicles has also translated in a need for more advanced models that can predict their behavior, but also quantify the uncertainty in their predictions, given the amount of variables involved and the varying operating conditions. This manuscript proposes a new Bayesian physics-informed recurrent neural network, where the battery discharge curve is described using the Nernst and Butler–Volmer equations, the activity correction term within such equations is modeled with two multilayer perceptrons, and approximate Bayesian computation by subset-simulation is used to train the weights, bias and the physical parameters representing the maximum charge available and the internal resistance. The challenges found during the adaptation and implementation of the Bayesian training algorithm to the recurrent physics-informed cell are described, along with the approaches proposed to overcome them. The performance of the Bayesian hybrid model presented in this paper has also been evaluated using data from NASA Ames Prognostics Data Repository, and the results show comparable accuracy to the standard approach with backpropagation, and a flexible and realistic quantification of the uncertainty. Furthermore, the uncertainty related to the physical parameters of the hybrid model can be evaluated in semi-isolation of the weights and bias of the MLPs, providing a sensitivity tool to assess the relative importance between different parameters.ENHAnCE project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 85995