Suchergebnisse

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 859957 ; Lloyd's Register Foundation (LRF), a charitable foundation in the U.K.

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 Skodowska-Curie grant agreement No 721455. The authors are also grateful to the California Institute of Technology for kindly hosting the first author. ; This paper presents OptiSens, a computational platform in Python and Matlab, that provides optimal sensor and actuator configurations for structural health monitoring applications using ultrasonic guided-waves. This software formulates a convex entropy-based objective function, which aims at minimizing the uncertainty while maximizing the expected accuracy of the monitoring system in localizing structural damage. The platform is specialized for two types of different materials, namely isotropic and composite (anisotropic) materials. The effectiveness and efficiency of this software are demonstrated using two plate-like structures made of different materials. ; European Union's Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant 721455

This work presents a digital twin framework for structural engineering. The digital twin is conceptualised and mathematically idealised within the context of structural integrity, and includes the main attributes to behave as a functional digital twin, namely simulation, learning, and management. The manuscript makes special emphasis on the autonomous interactions between the physical and digital counterparts along with on the workflow modelling of the digital twin, which are both missing aspects in the majority of use cases found in the literature, specially within the civil and structural engineering domain. The proposed framework is demonstrated in a proof of concept using a laboratory scale test structure monitored using internet-of-things-based sensors and actuators. The results reveal that the virtual counterpart can respond in real-time with self-adaptability in liaison to the performance of the physical counterpart. Moreover, the tests show that the digital twin is able to provide automated decision making for structural integrity. ; European Union's Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie 859957 ; European Union's Horizon 2020 research and innovation programme 821054 ; Universidad de Granada/CBUA

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-4408 ; Royal Academy of Engineering of Spain - Ferrovial S.A. OTRI-4408 ; Spanish Government fellowship programme Juan de la Cierva Incorporacion IJC2018-036092-I