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This study systematically assesses the efficacy of the vehicle scanning methods (VSM) in accurately estimating the mode shapes of bridges seated on elastic supports. Three state-of-the-art VSM methods are employed to obtain the mode shapes of bridges using the vehicle data during its travel. Two of the evaluated methods use a signal decomposition technique to extract the modal components of the bridge from the contact point of the response while the third one relies on the segmentation of the measured signals along the bridge deck and applying an operational modal analysis tool to each segmented signal to estimate the mode shapes. Numerical analyses are conducted on one single- and one two-span bridge, considering smooth and rough road profiles, different vehicle speeds, and presence of lead vehicle. The accuracy of the numerical models used in developing and assessing vehicle scanning models is tested, and the results of the study demonstrate the method using a half-car vehicle model and signal decomposition technique shows robustness against increasing vehicle speeds and road roughness while the method applying the segmentation of the measured signals provides relatively accurate mode shape estimates at the bridge edges at low speed, although the three methods have their limitations. It is also observed that simplified bridge and vehicle models can hide potential challenges that arise from the complexity of actual vehicle and bridge systems. Considering that a significant number of bridges worldwide are built on elastic supports, the practical success of vehicle scanning methods depends on their ability to handle elastic boundary conditions with reliability. Therefore, the article provides valuable insights into the capabilities and limitations of the current vehicle scanning methods, paving the way for further advancements and refinements in these techniques., Peer Reviewed, Postprint (published version)

El pont de Bac de Roda, conegut també pel nom del seu dissenyador, Santiago Calatrava, fou construït entre 1985 i 1987. Passa per sobre de las vies del tren, unint els carrers de Bac de Roda i Felip II; conectant així el barri de Sant Martí de Provençals amb el de la Sagrera. Emplaçament: carrer Felip II / carrer Bac de Roda

Afra is an Eclipse-based tool for the modeling and model checking of Rebeca family models. Together with the standard enriched editor, easy to trace counter-example viewer, modular temporal property definition, exporting a model and its transition system to some other formats facilities are features of Afra. Rebeca family provides actor-based modeling languages which are designed to bridge the gap between formal methods and software engineering. Faithfulness to the system being modeled, and the usability of Rebeca family languages help in ease of modeling and analysis of the model, together with the synthesis of the system based on the model. In this paper, architectural decisions and design strategies we made in the development of Afra are presented. This makes Afra an extensible and reusable application for the modeling and analysis of Rebeca family models. Here, we show how different compilers can be developed for the family of languages which are the same in general language constructs but have some minor differences. Then we show how the model checking engine for these different languages is designed. Despite the fact that Afra has a layered object-oriented design and is developed in Java technology, we use C++ codes for developing its model checking for the performance purposes. This decision made the design of the application even harder.

Geofenced heavy trucks to protect bridges at crossings allowing higher weight on frozen roads Winter is our friend. When the road body is deep frozen it can handle more weight than during the rest of the year. However, the bridges are not affected by the cold weather, and they are therefore still vulnerable to increased loads. How can we allow increased loads on frozen roads while ensuring protection of the bridges? In this report, we share our insights from a project with the idea of using geofencing to protect the bridges. The geofencing technology ensures that the truck drives at a lower speed over the bridge and the bridge can withstand loads up to 74 tons since decreased speed reduces dynamic loads. If the road keeper can get guarantees that all heavy trucks drive at a low speed over the bridge, heavier traffic can be accommodated. This technology would of course also be beneficial to use across bridges in Europe regardless of the climate. ' The project “Frozen roads and 74 tons”, paid by the Swedish Transport Administration, consisted of three parts. One part was a pilot study during winter 22/23 demonstrating trucks from AB Volvo and Scania loaded with 74 tons using geofencing when the trucks passed over weak bridges. A speed limit, i.e. 50 km/h, was imposed in a zone around each bridge, whose coordinates were stored in the digital map accessible through the trucks’ Fleet Management System. Two different geofencing technologies were tested: on the one hand Scania’s system with “active” geofencing, where the truck was programmed to maintain the allowed speed over the bridge and calculated and implemented this itself (the driver could, however, override this by pushing the gas pedal to the floor); on the other hand AB Volvo’s system with “passive” geofencing, where the driver received a warning message when approaching the zone and would then slow down if necessary. The drivers were interviewed before and after the pilot about their experience. The results from

A stone bridge goes over the old Champlain Canal. oil; Through Inland Waters: Champlain Canal at 200 juried art show April 2023 in Friends Gallery at Crandall Public Library

Oil painting based on a post card in the Champlain Canal Stories collection- ChamplainCanal051.jpg. Water is flowing over the top of the lock door. Small bridge leads over the canal from the towpath to the 2 buildings on the left. oil; Through Inland Waters: Champlain Canal at 200 juried art show April 2023 in Friends Gallery at Crandall Public Library

Outstanding users (OUs) denote the influential, 'core' or 'bridge' users in online social networks. How to accurately detect and rank them is an important problem for third-party online service providers and researchers. Conventional efforts, ranging from early graph-based algorithms to recent machine learning-based approaches, typically rely on an entire social network's information. However, for privacy-conscious users or newly-registered users, such information is not easily accessible. To address this issue, we present DeepPick, a novel framework that considers both the generalization and specialization in the detection task of OUs. For generalization, we introduce deep neural networks to capture dynamic features of the users. For specialization, we leverage the traditional descriptive features to make use of public information about users. Extensive experiments based on real-world datasets demonstrate that our approach achieves a high efficacy of detection performance against the state-of-the-art., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Information and Communication Technology

La infraestructura, particularment els ponts, és essencial perquè el món modern funcioni sense problemes. Aquestes estructures són crucials per al transport, el comerç i les interaccions socials, i la seva gestió adequada és vital per a garantir la seva longevitat i seguretat. Per tant, és necessari aplicar sistemes de gestió com el Modelatge d'Informació d'Edificacions (BIM, per les seves sigles en anglès) i els Sistemes de Gestió d'Edificacions (BMS, per les seves sigles en anglès). Aquesta tesi té com a objectiu demostrar l'eficàcia d'un BMS basat en BIM per a la gestió de ponts. Les dades de BIM i BMS s'integren mitjançant el desenvolupament d'un esquema de dades, que serveix com a base per a crear elements estructurals detallats. El model paramètric desenvolupat amb el modelatge basat en algorismes i l'alineament en algorismes permet adequar qualsevol projecte de manera més ràpida i eficient. La metodologia desenvolupada per a aquest propòsit garanteix que la informació continguda en l'esquema de dades es tradueixi en elements estructurals precisos i exactes en el model. Aquests elements estructurals detallats s'utilitzen després per a crear el BMS, que proporciona monitoratge en temps real, control i recomanacions de manteniment per als ponts. El BMS desenvolupat en aquesta tesi s'aplica a un sistema pilot de gestió de ponts per a demostrar la seva importància i usabilitat. Els resultats mostren que el BMS pot ajudar a gestionar la infraestructura de manera més eficient, especialment en el cas dels ponts. Pot detectar problemes potencials abans que es tornin greus i permetre que es prenguin accions correctives oportunes, millorant la vida útil i la seguretat., La infraestructura, particularmente los puentes, es esencial para que el mundo moderno funcione sin problemas. Estas estructuras son cruciales para el transporte, el comercio y las interacciones sociales, y su gestión adecuada es vital para garantizar su longevidad y seguridad. Por lo tanto, es necesario aplicar sistemas de gestión como el Modelado de Información de Edificaciones (BIM, por sus siglas en inglés) y los Sistemas de Gestión de Edificaciones (BMS, por sus siglas en inglés). Esta tesis tiene como objetivo demostrar la eficacia de un BMS basado en BIM para la gestión de puentes. Los datos de BIM y BMS se integran mediante el desarrollo de un esquema de datos, que sirve como base para crear elementos estructurales detallados. El modelo paramétrico desarrollado con el modelado basado en algoritmos y el alineamiento en algoritmos permite adecuar cualquier proyecto de manera más rápida y eficiente. La metodología desarrollada para este propósito garantiza que la información contenida en el esquema de datos se traduzca en elementos estructurales precisos y exactos en el modelo. Estos elementos estructurales detallados se utilizan luego para crear el BMS, que proporciona monitoreo en tiempo real, control y recomendaciones de mantenimiento para los puentes. El BMS desarrollado en esta tesis se aplica a un sistema piloto de gestión de puentes para demostrar su importancia y usabilidad. Los resultados muestran que el BMS puede ayudar a gestionar la infraestructura de manera más eficiente, especialmente en el caso de los puentes. Puede detectar problemas potenciales antes de que se vuelvan graves y permitir que se tomen acciones correctivas oportunas, mejorando la vida útil y la seguridad., Infrastructure, particularly bridges, is essential for the modern world to function smoothly. These structures are crucial for transportation, commerce, and social interactions, and their proper management is vital to ensure their longevity and safety. Therefore, it is needed to apply management systems such as Building Information Modeling (BIM) and Building Management Systems (BMS) are needed. This thesis aims to demonstrate the effectiveness of a BMS based on BIM for managing bridges. BIM and BMS data are integrated by developing a data schema, which serves as the foundation for creating detailed structural elements. The parametric model developed with the algorithm-based modeling and the alignment-based algorithm allows adequating any project faster and more efficiently. The methodology developed for this purpose ensures that the information contained in the data schema is translated into accurate and precise structural elements on the model. These detailed structural elements are then utilized to create the BMS, which provides real-time monitoring, control, and maintenance recommendations for bridges. The BMS developed in this thesis is applied to a pilot bridge management system to demonstrate its usability. The results show that the BMS can help manage infrastructure more efficiently, particularly in the case of bridges. It can detect potential problems before they become severe and enable timely corrective actions to be taken, improving the lifespan and safety.

Pre-stressed concrete with post-tensioned (PT) cables historically represented an attractive alternative to ordinary reinforced concrete (RC). A wide portion of the world-wide existing concrete bridges was PT. As the nominal service life of those structures expires, several collapses occurred, highlighting the problem of the inspection. The main issue is that PT cables may be affected by relevant corrosion phenomena related to the presence of air or water inside the duets: such 'hidden' defects need to be determined through reliable testing methodologies. This paper presents the in-progress research aiming to quantify the influence of the damage of the single cable on the entire structure. The case study is an ltalian viaduct composed of simply supported beams connected by an upper slab. An extensive investigation campaign was carried out to locate and quantify the damage. Starting from the data collected on-site a series of damage conditions of cables can be simulated., Peer Reviewed, Postprint (published version)

Structural Health Monitoring (SHM) has emerged as an important area of structural engineering that evaluates existing civil structures as they age over time. With aging, these structures become increasingly susceptible to various human activities, unexpected traffic loads, natural disasters such as earthquakes, and wind effects. These factors can lead to reduced performance, functionality, and residual life. Recent advancements in data science have paved the way for using Machine Learning (ML) and Deep Learning (DL) techniques in SHM. Wireless sensors are crucial for structural health monitoring systems as they collect structural responses to assess structures' load-carrying capacity and serviceability. However, sensor failure during signal transmission can result in data loss, leading to an incorrect diagnosis of the structural health status. Both continuously missing data and random missing data pose critical challenges for damage detection and condition assessment and require significant attention. Conventional methods for lost data imputation, such as correlation techniques, have low efficiency due to their incompact neural network structures. To address this issue, this paper proposes a modified generative adversarial imputation network (GAIN) to recover missing acceleration data collected during ambient vibration of the bridge. The proposed framework utilizes neural networks with a generator-discriminator architecture to extract valuable information from the non-missing portions of faulty sensors and impute the data for the missing portions. The proposed method demonstrates efficient and accurate imputation performance under retrofitting and non-retrofitting stages through an analysis of measured acceleration signals under ambient excitation of the steel bowstring railway bridge in Belgium, KW 51. Comparative results based on performance measure parameters such as root mean squared error (RMSE), mean average error (MAE), correlation factor (R2 ), and accuracy

Bridges play a crucial role in facilitating transportation and economic growth, but their vulnerability to earthquakes necessitates effective damage detection methods. This thesis explores the application of Continuous Wavelet Transforms (CWT) in combination with Vision Transformers (ViTs) for automated detection and assessment of structural damage in bridges. In the conventional approach, analytical fragility curves, which quantify the likelihood of different damage states based on ground shaking intensity, are employed to predict bridge damage. The study addresses the limitations of conventional methods and introduces deep learning techniques to enhance computational efficiency and accuracy. By analyzing a dataset of ground motion records and training the ViT model, the research aims to advance automated bridge inspection techniques and enable early damage detection. The results demonstrate the effectiveness of the CWT-based ViT model in assessing the damage state of bridges. The proposed approach holds promise for proactive maintenance strategies, improving the safety and durability of critical infrastructure. Additionally, the thesis discusses the influence of various factors such as rupture distance on different bridge components and highlights the advantages of employing ViT models in terms of computational cost. Future work involves expanding the dataset to improve prediction accuracy and establishing a more uniform distribution of data for each damage category.

The integration of multicriteria decision-making (MCDM) methods with building information modeling (BIM) provides robust support for decision-making when assessing the sustainability of construction projects. Among the MCDM methods, the Integrated Value Model for Sustainability Assessment (MIVES) has proven to provide a representative and meaningful quantification of sustainability in different areas of civil engineering. Although previous studies have integrated BIM and MIVES, none automated the sustainability assessment process as BIM was only used to define the measurements of the MIVES indicators. This study accordingly developed a new method coupling the MIVES and BIM methodologies to realize automatic sustainability assessment of bridges. The proposed technique was validated using a real viaduct to evaluate sustainability and conduct a sensitivity analysis identifying the indicators with the most influence on sustainability performance. Critically, this methodology is not limited to the sustainability assessment of bridges as it can be readily adapted to other types of infrastructure., The authors are indebted to the projects PID2021-126405OB-C31, and PID2021-126405OB-C32 funded by MICIN/AEI/10.13039/501100011033/ and FEDER funds A way to make Europe., Peer Reviewed, Postprint (published version)

Bridges play a crucial role in facilitating transportation and economic growth, but their vulnerability to earthquakes necessitates effective damage detection methods. This thesis explores the application of Continuous Wavelet Transforms (CWT) in combination with Vision Transformers (ViTs) for automated detection and assessment of structural damage in bridges. In the conventional approach, analytical fragility curves, which quantify the likelihood of different damage states based on ground shaking intensity, are employed to predict bridge damage. The study addresses the limitations of conventional methods and introduces deep learning techniques to enhance computational efficiency and accuracy. By analyzing a dataset of ground motion records and training the ViT model, the research aims to advance automated bridge inspection techniques and enable early damage detection. The results demonstrate the effectiveness of the CWT-based ViT model in assessing the damage state of bridges. The proposed approach holds promise for proactive maintenance strategies, improving the safety and durability of critical infrastructure. Additionally, the thesis discusses the influence of various factors such as rupture distance on different bridge components and highlights the advantages of employing ViT models in terms of computational cost. Future work involves expanding the dataset to improve prediction accuracy and establishing a more uniform distribution of data for each damage category.

Structural Health Monitoring (SHM) has emerged as an important area of structural engineering that evaluates existing civil structures as they age over time. With aging, these structures become increasingly susceptible to various human activities, unexpected traffic loads, natural disasters such as earthquakes, and wind effects. These factors can lead to reduced performance, functionality, and residual life. Recent advancements in data science have paved the way for using Machine Learning (ML) and Deep Learning (DL) techniques in SHM. Wireless sensors are crucial for structural health monitoring systems as they collect structural responses to assess structures' load-carrying capacity and serviceability. However, sensor failure during signal transmission can result in data loss, leading to an incorrect diagnosis of the structural health status. Both continuously missing data and random missing data pose critical challenges for damage detection and condition assessment and require significant attention. Conventional methods for lost data imputation, such as correlation techniques, have low efficiency due to their incompact neural network structures. To address this issue, this paper proposes a modified generative adversarial imputation network (GAIN) to recover missing acceleration data collected during ambient vibration of the bridge. The proposed framework utilizes neural networks with a generator-discriminator architecture to extract valuable information from the non-missing portions of faulty sensors and impute the data for the missing portions. The proposed method demonstrates efficient and accurate imputation performance under retrofitting and non-retrofitting stages through an analysis of measured acceleration signals under ambient excitation of the steel bowstring railway bridge in Belgium, KW 51. Comparative results based on performance measure parameters such as root mean squared error (RMSE), mean average error (MAE), correlation factor (R2 ), and accuracy

Bridges play a crucial role in facilitating transportation and economic growth, but their vulnerability to earthquakes necessitates effective damage detection methods. This thesis explores the application of Continuous Wavelet Transforms (CWT) in combination with Vision Transformers (ViTs) for automated detection and assessment of structural damage in bridges. In the conventional approach, analytical fragility curves, which quantify the likelihood of different damage states based on ground shaking intensity, are employed to predict bridge damage. The study addresses the limitations of conventional methods and introduces deep learning techniques to enhance computational efficiency and accuracy. By analyzing a dataset of ground motion records and training the ViT model, the research aims to advance automated bridge inspection techniques and enable early damage detection. The results demonstrate the effectiveness of the CWT-based ViT model in assessing the damage state of bridges. The proposed approach holds promise for proactive maintenance strategies, improving the safety and durability of critical infrastructure. Additionally, the thesis discusses the influence of various factors such as rupture distance on different bridge components and highlights the advantages of employing ViT models in terms of computational cost. Future work involves expanding the dataset to improve prediction accuracy and establishing a more uniform distribution of data for each damage category.

In the last decades, climate change is causing our environment to change rapidly, unprecedented in recent history. Civil engineering structures are dependent on the deteriorating environment they are situated in. Changes can cause an increase in loading due to, for example, extreme weather events or alter the structure’s resistance by, for instance, accelerated corrosion or an increase in the number of frost days. However, planning for such events depends significantly on the state of climate change, which is not considered in the sequential decision-making optimization for inspecting and maintaining our infrastructures. Sequential decision-making optimization refers to the process of finding an optimal sequence of actions to be performed to keep the structure safe. Optimality often refers to the plan with the lowest costs. Traditional inspection and maintenance plans seek a solution by applying heuristic-decision rules on, for instance, time- or condition constraints and fail to find an optimal solution. During the last decade, a new method called Deep Reinforcement Learning (DRL) has been applied and proven to find an optimal strategy to beat traditional approaches. Especially the ability of DRL to find an optimal solution for partially observable environments makes it a perfect candidate for the problem at hand. This thesis has developed a framework that incorporates partial observability over possible climate scenarios in the decision-making of engineering structures’ inspection and maintenance planning. The framework explains the steps required to translate a physical system towards a Partially Observable Markov Decision Process (POMDP), as the mathematical framework needed to seek an optimal se- quence of inspection and maintenance actions for. Then, it is explained how the POMDP can be used to find an optimal policy with a DRL algorithm, which includes benchmarking and testing the policy. A belief state has been incorporated over the climate, Civil Engineering | Structural Engineering | Structural Mechanics