Assessment and identification of concrete box-girder bridges properties using surrogate model calibration: case study: El Tablazo bridge

Dissertação de mestrado integrado em Engenharia CivilThis work consists in identifying and assessing the properties in a pre-stressed concrete bridge related to material, geometry and physic sources, through a surrogate model. The participation of this mathematical model allows to generate a relationship between bridge properties and its dynamic response, with the purpose of creating a tool to predict the analytical values of the studied properties from measured eigenfrequencies; in this case, it is introduced the identification of damage scenarios, giving the application for validate the generated metamodel (Artificial Neural Network - ANN). A FE model is developed to simulate the studied structure, a Colombian bridge called El Tablazo, one of the higher in the country of this type (box-girder bridge), with a total length of 560 meters, located on the Sogamoso riverbed in the region of Santander - Colombia. Once the damage scenarios are defined, this work allows to indicate the basis for futures plans of structural health monitoring.Este trabalho consiste em identificar e avaliar as propriedades de uma ponte em betão pré-esforçado em relação ao material, geometria e características físicas através de um metamodelo. A participação deste modelo matemático permite gerar uma relação entre as propriedades da ponte e sua resposta dinâmica, com o objetivo de criar uma ferramenta para prever os valores analíticos das propriedades estudadas a partir de frequências próprias medidas; neste caso, é introduzida a identificação de cenários de dano, dando uma aplicação para validar o metamodelo (Rede Neural Artificial - ANN). Um modelo de elemento finito é desenvolvido para simular a estrutura estudada, uma ponte colombiana chamada El Tablazo, uma das que apresenta maior altura do país em seu tipo (pontes em viga-caixão), com um comprimento total de 560 metros, localizada no rio Sogamoso, na região de Santander - Colômbia. Uma vez que os cenários de dano são definidos, a tese permite indicar a base para os planos futuros de monitoramento da saúde estrutural

Developing a comprehensive quality control framework for roadway bridge management: a case study approach using key performance indicators

Transportation infrastructures, especially roadway bridges, play a pivotal role in socioeconomic development. Recently, bridge engineers are increasingly facing the challenge in terms of shifting their strategy from building new facilities to maintaining the existing aging infrastructures, to preserve their service performance during the operational stage. In fact, the infrastructure administrators lack a quality control (QC) strategy for the existing roadway bridges, which leads to the decision-making application and tool being still minor. To overcome those challenging issues, this paper proposes a quality control framework for roadway bridge management using key performance indicators (KPIs). The case study methodology is suggested to be used and then conducted for several bridges, mostly in European countries. In which the performance indicators (PIs) and goals (PGs) are defined, after assessing the bridges and vulnerable zones, the derivation KPIs from those PIs are introduced and developed considering time functions and different maintenance scenarios. Eventually, a two-stage quality control framework will be proposed in which the static stage includes preparatory works, inspection responsibilities, and a quick assessment of KPIs; while the dynamic stage helps the decision maker in estimating the time remaining of the bridge service life, managing the evolution of KPIs as well as planning the best possible maintenance strategy. The selected two case studies are present and curated, which show the excellent potential to develop a long-term strategy for roadway bridge management on a lifecycle level.This research was funded by FCT/MCTES through national funds (PIDDAC) from the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under the reference UIDB/04029/2020, and from the Associate Laboratory Advanced Production and Intelligent Systems ARISE, under the reference LA/P/0112/2020, as well as financial support of the project re-search “B2022-GHA-03” from the Ministry of Education and Training. And The APC was funded by ANI (“Agência Nacional de Inovação”) through the financial support given to the R&D Project “GOA Bridge Management System—Bridge Intelligence”, with reference PO-CI-01-0247- FEDER-069642, which was cofinanced by the European Regional Development Fund (FEDER) through the Operational Competitiveness and Internationalisation Program (POCI).Minh Q. Tran acknowledges the support by the doctoral grant reference PRT/BD/154268/2022, financed by Portuguese Foundation for Science and Technology (FCT), under the MIT Portugal Program (2022 MPP2030-FCT)

Structural assessment based on vibration measurement test combined with an artificial neural network for the steel truss bridge

Damage assessment is one of the most crucial issues for bridge engineers during the operational and maintenance phase, especially for existing steel bridges. Among several methodologies, the vibration measurement test is a typical approach, in which the natural frequency variation of the structure is monitored to detect the existence of damage. However, locating and quantifying the damage is still a big challenge for this method, due to the required human resources and logistics involved. In this regard, an artificial intelligence (AI)-based approach seems to be a potential way of overcoming such obstacles. This study deployed a comprehensive campaign to determine all the dynamic parameters of a predamaged steel truss bridge structure. Based on the results for mode shape, natural frequency, and damping ratio, a finite element model (FEM) was created and updated. The artificial intelligence network’s input data from the damage cases were then analysed and evaluated. The trained artificial neural network model was curated and evaluated to confirm the approach’s feasibility. During the actual operational stage of the steel truss bridge, this damage assessment system showed good performance, in terms of monitoring the structural behaviour of the bridge under some unexpected accidents.This research was funded by FCT/MCTES through national funds (PIDDAC) from the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under the reference UIDB/04029/2020, and from the Associate Laboratory Advanced Production and Intelligent Systems ARISE, under the reference LA/P/0112/2020, as well as financial support of the project research “B2022-GHA-03” from the Ministry of Education and Training. And The APC was funded by ANI (“Agência Nacional de Inovação”) through the financial support given to the R&D Project “GOA Bridge Management System—Bridge Intelligence”, with reference POCI-01-0247-FEDER069642, which was cofinanced by the European Regional Development Fund (FEDER) through the Operational Competitiveness and Internationalisation Program (POCI)

Structural damage identification for robustness assessment of railway infrastructure under flood effects

Tese de doutoramento em Engenharia CivilDevido à importância das infraestruturas ferroviárias na Europa, sejam económicas ou culturais, vários investimentos têm sido feitos para melhorar as atuais redes. Um desses investimentos está no programa Shift2Rail, que tem como objetivo a gestão de ativos de estruturas de alta capacidade com precisão e confiabilidade. Atingir este objetivo principal é priorizado por métodos inovadores para determinar a segurança e prolongar a vida útil da infraestrutura ferroviária. A partir desta perspetiva, esta investigação apresenta uma estrutura de trabalho de uma metodologia baseada em modelos substitutos para uma avaliação de robustez de pontes ferroviárias com o objetivo de analisar o tipo de ponte mais padrão na Europa e a causa comum da falha estrutural, priorizando a simplificação da coleta de dados e cálculos computacionais enquanto se concentra em complexidades específicas da análise da estrutura. Para tal foram desenvolvidas quatro vertentes de trabalho: i) avaliação do risco de inundação, que entre todos os perigos conhecidos para pontes, os efeitos das inundações são a principal causa de colapso para este tipo de estrutura. Por esta razão, a estimativa de risco tem o objetivo de desenvolver um modelo hidráulico alimentado por uma metodologia de machine learning capaz de modelar variáveis hidrológicas considerando as incertezas das mudanças climáticas. ii) A análise de falhas de pontes considera os diferentes efeitos das inundações que causam diferentes mecanismos de colapso. Portanto, uma visão geral da interação ponte-inundação foi feita em relação aos problemas estruturais e geotécnicos para quantificar a causa e o tipo de falha. iii) A análise estrutural não linear tem o objetivo de apresentar uma abordagem de modelagem usando diferentes elementos 2D e 3D do software DIANA FEA, permitindo um alto grau de detalhamento na análise não linear. iv) A avaliação de robustez apresenta uma aplicação de uma metodologia baseada em modelos substitutos para obter a probabilidade de falha na estimativa de um indicador de robustez do perigo estudado. Consequentemente, a estrutura de trabalho proposta é aplicada a uma ponte em arco de pedra existente em Portugal para testar a sua eficiência, precisão e aplicabilidade.Due to the importance of railway infrastructure in Europe, whether economically or culturally, several investments have been made to improve the existing networks. One of these investments is taking place within the Shift2Rail program, which aims at the asset management of high-capacity structures with accuracy and reliability. The achievement of this main objective is prioritized by innovative methods to determine the safety and extend the life of railway infrastructures. From this perspective, this research presents a framework of a surrogate model- based methodology for a robustness assessment of railway bridges, aiming to analyze the most common bridge type and causes of failure in Europe, prioritizing the simplification of data collection and computational effort, while focusing on specific complexities of structural analysis. To achieve this, four workstreams have been developed: i) Flood hazard assessment, which is the main cause of collapse of this type of structures among all known hazards for bridges. For this reason, the hazard assessment aims to develop a hydraulic model fed by a machine learning method capable of modeling hydrological variables, considering the uncertainties of climate change. ii) The analysis of bridge failure considers the different effects of flooding that cause different collapse mechanisms. Therefore, an overview of the interaction between flooding and the bridge in terms of structural and geotechnical problems has been made in order to quantify the cause and nature of the failure. iii) The nonlinear structural analysis aims to present a modeling approach using different 2D and 3D elements of the software DIANA FEA that allows a high level of detail of the structural behavior. iv) Robustness evaluation presents an application of a surrogate model based methodology to obtain the probability of failure and estimate a robustness indicator for the hazard under study. Consequently, the proposed framework is applied to an existing stone arch bridge in Portugal to test its efficiency, accuracy, and applicability.This research was funded by the European Social Fund of the European Union (ESF/EU) through the Foundation for Science and Technology (FCT), with the grant contract "PD/BD/143142/2019" under the doctoral program “Innovation in Railway System and Technologies – iRail”

Arch concrete bridge risk-based assessment using a Portuguese case study

There is an increased need for developing strategies that ensure the long-term quality of the bridges and meet the present and future needs of the users. In this sense, risk-based methodologies are implemented and constantly evolving to obtain better results measuring risk against sudden events and eventually structure deterioration. The present work aims to develop the application of several methodologies as a part of the topic of risk and reliability assessment. This research uses an arch concrete roadway bridge as a case study and it is located in the region of Guarda (Portugal), due to this particular bridge its located in a zone where exist several risks (not only caused by natural events).(undefined

An overview of the project strengthening infrastructure risk management in the Atlantic area (SIRMA)

Most of the transportation of people and goods in Atlantic Area is made through rail and road infrastructures. Their performance is directly affected by extreme natural events and by the strong corrosion processes that result from proximity to the Atlantic Ocean. SIRMA project aims to develop a robust framework for themanagement and mitigation of such risks, by implementing immediate, medium, and long-term measures, and therefore to increase the resilience of transportation infrastructure. SIRMA's core is on longterm recovery and risk mitigation to reduce maintenance and retrofitting costs. This objective will be obtained by adjusting existing deterministic models of infrastructure resilience under the current climate, with probabilistic models considering the uncertainties of future climate and change on the land use and how it affects hazard impact on individual mode components. Thus, probabilistic models will allow for a better adaptation of infrastructure to climate change and consider the uncertainties to develop adequate predictive policies and planning tools to reduce risks of hazards.SIRMA is a project co-financed by the Interreg Atlantic Area Programme through the European Regional Development Fund (ERDF)with the application code: EAPA_826.201