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Masonry arch bridges are essential elements of the road and railway networks of Europe, as they pertain to cultural heritage and play an important role in transportation. The integrity and safety of these centuries-old structures must be ensured. Therefore, there is great interest in understanding the behavior of masonry arch bridges and monitoring their health. Efficient methods for assessing the condition of structures, including inspections and monitoring, constitute a fundamental element of structural health monitoring (SHM) applications. Given the multitude of surveying techniques available, it becomes crucial to establish cost-effective sensor placement strategies capable of detecting structural failures. These strategies can involve identifying particularly sensitive areas, optimizing the number of sensors used, as well as refining the number and duration of monitoring setups, among other considerations. As part of the PONT3 project, which aims to develop a comprehensive and cost-effective approach to anticipate failure propagation in ageing bridges, this research focusses on developing interoperability-driven inspection and monitoring strategies to provide data that will allow anticipating the evolution of possible failure scenarios. In this regard, an extensive summary of the key characteristics of inspection and monitoring technologies, and typical system configurations in existing literature for masonry arch bridges are provided. Then, the measurable physical parameters of interest and applicable monitoring techniques for each of the physical parameters is discussed. Monitoring requirements in terms of measurement locations, accuracy, range, and temporal resolution and the aspects of developing cost functions are evaluated. The recommendations for interoperability-driven data collection to exchange and employ acquired information for the purpose of structural diagnosis concludes the framework. The study finally discusses a future outlook on the developmen, Peer Reviewed, Postprint (published version)

Masonry arch bridges are a significant component of Europe's road and railway networks, contributing to cultural heritage and playing a crucial role in transportation. Ensuring the integrity and safety of these centuries-old structures is of paramount importance. Dynamic identification methods offer an effective approach to assess the performance of masonry arch bridges and update Finite Element (FE) models, thereby reducing uncertainties. However, practical limitations often restrict measurements to a few discrete locations, necessitating the identification of optimal sensor locations to capture crucial structural behaviour. This study focuses on the Joncadella Viaduct in Spain, a unique hybrid arch bridge comprising both masonry and concrete spans. As part of the PONT3 Project, which aims to develop a comprehensive and cost-effective approach to anticipate failure propagation in aging bridges, this research focuses on the masonry section of the bridge that comprises four of the thirteen spans. By creating 3D FE models of the masonry portion and the entire bridge, an Optimum Sensor Placement (OSP) study is conducted using the effective independence method (EFI) for varying numbers of sensors and dynamic identification test setups. The study also involves calibrating the boundary conditions of the partial FE model to match the dynamic properties of the full bridge model. The obtained results will guide field measurements to update the FE model of the partial arch bridge, thereby reducing uncertainties related to boundary conditions and material properties., Peer Reviewed, Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura::9.1 - Desenvolupar infraestructures fiables, sostenibles, resilients i de qualitat, incloent infraestructures regionals i transfrontereres, per tal de donar suport al desenvolupament econòmic i al benestar humà, amb especial atenció a l’accés assequible i equitatiu per a totes les persones, Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles, Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles::11.4 - Redoblar els esforços per a protegir i salvaguardar el patrimoni cultural i natural del món, Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura, Objectius de Desenvolupament Sostenible::13 - Acció per al Clima::13.1 - Enfortir la resiliència i la capacitat d’adaptació als riscos relacionats amb el clima i els desastres naturals a tots els països, Objectius de Desenvolupament Sostenible::13 - Acció per al Clima, Postprint (published version)

Damage detection algorithms employing the conventional acceleration measurements and the associated modal features may underperform due to the limited number of sensors used in the monitoring and the smoothing effect of spline functions used to increase the spatial resolution. The effectiveness of such algorithms could be increased if a more accurate estimate of mode shapes were provided. This study presents a hybrid structural health monitoring method for vibration-based damage detection of bridge-type structures. The proposed method is based on the fusion of data from conventional accelerometers and computer vision-based measurements. The most commonly used mode shape-based damage measures, namely, the mode shape curvature method, the modal strain energy method, and the modal flexibility method, are used for damage detection. The accuracy of these parameters used together with the conventional sparse sensor setups and the proposed hybrid approach is investigated in numerical case studies, with damage scenarios simulated on a simply-supported bridge. The simulations involve measuring the acceleration response of the bridge to ambient vibrations and train crossings and then processing the data to determine the modal frequencies and mode shapes. The efficiency and accuracy of the proposed hybrid health monitoring methodology are demonstrated in case studies involving scenarios in which conventional acceleration measurements fail to detect and locate damage. The robustness of the proposed method against various levels of noise is shown as well. In the studies considered, damage as small as 10% decrease in flexural stiffness of the bridge and localized in less than 1% of the span-length of the bridge is reliably detected even with very high levels of measurement noise. Finally, a modified modal flexibility damage parameter is derived and used to alleviate the shortcomings of the modal flexibility damage parameter., Peer Reviewed, Postprint (published version)

Structural Health Monitoring (SHM) in bridges is an important field, which implements increasingly innovative damage detection strategies for both theoretical developments and laboratory-scale implementations. However, is very important to consider the studies of bridges in real conditions. This study presents the damage identification of a real bridge using Hilbert-Huang transform and the most recent advance in the empirical mode decomposition method (EMD) called Improvements on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). This technique is used to decompose vibration data into intrinsic mode functions (IMF). Then, the Marginal Hilbert spectrum and phase difference were two damage features studied on a bridge where artificial damage was imposed. These artificial damage levels were applied sequentially and the vibration data was obtained by the passage of a recreation vehicle. As results, the ICEEMDAN method and proposed damage indicators demonstrate good performance to detect and locate damages under transient vibration loads on a real bridge., The first author acknowledges the support received from Ministry of Education of Peru with the National Scholarship and Educational Loan Program PRONABEC - President of the Republic Scholarship. The authors wish to express their gratitude to professor Woo Kim of the Department of Civil and Earth Resources Engineering, Kyoto University, Kyoto, Japan for the generous sharing of the steel truss bridge data assessed within this study., Postprint (published version)

Los puentes pueden llegar a colapsar bajo ciertas circunstancias, por ejemplo, por desastres naturales, sobrecarga de tráfico, procesos de deterioro o falta de mantenimiento, lo que genera grandes costes de renovación y reemplazo y, en el peor de los casos, pérdidas humanas. Con el fin de evitar fallas catastróficas es necesaria la evaluación de la estabilidad estructural de los puentes durante su vida útil de diseño. Por este motivo, en las últimas décadas se han investigado y adoptado numerosas técnicas de monitoreo de salud estructural (SHM). En particular, los métodos basados en vibraciones (VBM) se han utilizado ampliamente para la detección de daños y la evaluación del estado de las estructuras de puentes debido a su alta eficiencia. Estos métodos se basan en el principio de que un cambio en la respuesta dinámica de la estructura puede deberse a un posible daño estructural. Sin embargo, en el caso de los puentes, estos cambios también pueden deberse a la variabilidad operativa (efecto del tráfico real en el puente) y ambiental (variaciones de temperatura y humedad). De hecho, esto dificulta la labor de identificación de daños, ya que el puente puede permanecer seguro, pero aún tener cambios en la respuesta dinámica si los efectos de tráfico y condiciones ambientales no se han eliminado adecuadamente de la respuesta dinámica. Por lo tanto, se activarían falsos positivos de alarma por daño. Desde este punto de vista, el objetivo de esta tesis es evaluar métodos y algoritmos adecuados para detectar, localizar y cuantificar diferentes tipos de escenarios de daños eliminando los efectos de variabilidad operativa y ambiental. Sin embargo, debido a la dificultad de obtener datos reales en puentes sin daño y posteriormente dañados, y a la vez influenciados por el tráfico y los cambios de temperatura, los métodos propuestos se verifican mediante un modelo numérico de referencia generada como parte de la Acción COST TU1402 sobre la cuantificación del valor de la informa, Bridges are prone to collapse under certain circumstances, for instance, by natural disasters, traffic overloading, deterioration processes or lack of maintenance, resulting in huge renovation and replacement costs and, in the worst cases, human losses. To prevent catastrophic failures, the assessment of the structural stability of bridges during their design lifetime is required. Consequently, numerous Structural Health Monitoring (SHM) techniques have been investigated and adopted over the past few decades. Particularly, vibration-based methods (VBM) have been widely used for damage detection and condition assessment of bridge structures due to its high efficiency. These methods are based on the concept that a potential structural damage can be inferred from a change in the dynamic response of the structure. However, in the case of bridges, these changes can also be due to the operational (traffic load effects) and environmental (temperature and humidity variations) variability. Indeed, this hinders the task of damage identification since the bridge can stay safe, but still has changes in the dynamic response if the effects of traffic and environment have not been properly deleted from the dynamic response. Therefore, a false positive alarm would be triggered. From this perspective, the aim of this thesis is to verify suitable methods and algorithms that can be used to detect, locate and quantify damage by removing the effects of operational and environmental variability. However, due to the difficulty of obtaining real data on undamaged and later damaged bridges, and simultaneously influenced by traffic and changes in temperature, the proposed methods are verified via a numerical benchmark structure generated as part of COST Action TU1402 on Quantifying the Value of Information. The benchmark model consists of a two-span steel girder bridge and includes six levels of damage simulated in two different locations. The vertical acceleration response is obtained from

Damage caused in bridges can be hidden by the service conditions mainly due to environment effects as temperature and traffic loading. The literature review indicates that the majority of investigations are limited since they do not consider these effects or they are applied under laboratory conditions and not to real bridges. The temperature effects on the natural frequencies of bridges have shown to be non-linear. In this context, this article presents a damage detection strategy considering a more robust kernel-based method (KPCA) which is the nonlinear extension of the principal component analysis (PCA) to take into account the environmental conditions of the bridge such as the non-linearity of the temperature effects in the natural frequencies. This method is combined with a clustering analysis in order to group the data with similar features both for undamaged and damaged conditions, and in this way the structural damages can be identified. The main contribution is a novel damage detection methodology based on advance statistical and machine learning algorithms to account for the non-linear environmental effects. The method is checked using measurements from the Z24 bridge, which was subjected to progressive structural damages while monitored for almost a year. The results show good performance of the proposed algorithm and highly reducing the computational cost., Postprint (published version)