Research
The most updated information on Structural Health Monitoring (SHM) can be found here.
At the Civil Research Group, SHM has been proposed for damage identification in infrastructure (e.g., bridges) and road pavements, and more recently for climate change adaptation. Four main vectors have driven our research: system identification (e.g., dynamic field tests), machine learning, finite element modeling, and sensor node development.
Structural Health Monitoring
The process of implementing a damage identification strategy for existing structures is often referred to as Structural Health Monitoring (SHM). Under that definition, damage is normally defined as changes to the material and/or geometric properties of the structural system, including changes to the boundary conditions and system connectivity, which adversely affect the system’s current or future performance.
The basic idea of SHM is to build up a system similar to the human nervous system, where the brain (computer) processes the information and determines actions (maintenance activities), and the nerves (sensors) feel the pain (damage).
In this context, machine learning algorithms play an importante role, as they can learn from the experience, i.e. from monitoring data. Machine learning is the science of getting computers and algorithms to model the reality without knowing the physical laws of structures.
Therefore, the author poses the SHM process in the context of a statistical pattern recognition (SPR) paradigm. In this paradigm, the process can be broken down into four steps:
Operational evaluation,
Data acquisition,
Feature extraction, and
Statistical modeling development for feature classification.
The SPR paradigm is a way to simplify complex data and information into simple indices and graphical representations of better understanding to draw maintenance actions.
The damage identification should be as detailed as possible in order to describe the damage impact on the system. In a broad sense, developments on damage identification can be broken down into three areas, namely damage detection, damage diagnosis, and damage prognosis. Nonetheless, damage diagnosis can be subdivided in order to better characterize the damage in terms of location, type, and severity. Thus, even though the original guidelines of Rytter assumed four levels, the hierarchical structure of damage identification can be decomposed in five levels that answers the following questions:
Is the damage present in the system (detection)?
Where is the damage (localization)?
What kind of damage is present (type)?
What is the extent of damage (severity)?
How much useful lifetime remains (prognosis)?
Structural Condition Assessment of Bridges
Improved and more continuous condition assessment of bridges has been demanded by our society to better face the challenges presented by aging civil infrastructure. Indeed, the recent collapses of the Hintze Ribeiro Bridge that killed 59 people, in Portugal, and the I-35W Bridge in the US, that killed 13 people, pointed out the need for new and more reliable tools to prevent such catastrophic events. Besides those events, the financial implications and potential impact through optimal bridge management are vast. For instance, the American Society of Civil Engineers reports the cost of eliminating all existing US bridge deficiencies at $850 billion. These values clearly show that planned bridge maintenance can lead to considerable savings.
In the last two decades, bridge condition assessment techniques have been developed independently based on two complementary approaches: Structural Health Monitoring (SHM) and Bridge Management Systems (BMSs). The SHM refers to the process of implementing monitoring systems to measure in real time the structural responses, in order to detect anomalies and/or damage at early stages. On the other hand, BMS is a visual inspection-based decision-support tool developed to analyze engineering and economic factors and to assist the authorities in determining how and when to make decisions regarding maintenance, repair, and rehabilitation of structures.
While the BMS has already been accepted by the bridge owners around the world, even though with inherent limitations posed by the visual inspections, the SHM is becoming increasingly appealing due to its potential ability to detect damage at early stages, with the consequent life-safety and economical benefits.
The author believes that, in an effort to create more robust bridge management, the SHM should be integrated into the BMS in a systematic way. Nowadays, there is a generalized consensus about this integration, but few real applications have been accomplished, mainly because of the lack of interaction between all the participants involved in the bridge management field.
Deep Learning for Damage Detection
The structural health monitoring relies on the continuous observation of a dynamic system over time to identify its actual condition, detect abnormal behaviors, and predict future states. The regular changes in environmental factors have been reported as one of the main challenges for the application of structural health monitoring systems. These influences in the structural responses are in general nonlinear, affecting the damage-sensitive features in the most varied forms. The usual process to remove these normal changes is referred to as data normalization. In that regard, principal component analysis is probably the most studied algorithm in structural health monitoring, having numerous versions to learn strong nonlinear normal changes. However, in most cases, not all variability is properly accounted for via the existing nonlinear principal component analysis approaches, resulting in poor damage detection and quantification performances. In this article, a new paradigm based on deep principal component analysis, rooted in the deep learning field, is presented to overcome these limitations. This approach extracts the most salient underlying feature distributions by stacking multiple feedforward neural networks trained to learn an identity mapping of the input variables, where the network inputs are reproduced into the outputs. Similar to the traditional nonlinear principal component analysis–based approach, our approach identifies a nonlinear output-only model of an undamaged structure by comprising modal features into an internal bottleneck layer, which implicitly represents the independent environmental factors. The proposed technique is validated through the application on a progressively damaged prestressed concrete bridge and a three-span suspension bridge. The experimental results demonstrate that capturing the most slight nonlinear variations in the data can lead to improved data normalization and, consequently, better damage detection and quantification performances. [See]
Bridge Management
The bridge management has been defined as a multidisciplinary field incorporating knowledge from structural engineering, information technology, and economics.
The BMSs are computerized tools, which incorporate that knowledge aiming to optimize maintenance budgets within a stock of existing bridges. The structural condition assessment of bridges is a subset of the structural engineering, concerning exclusively with the assessment of the structure integrity, defined as the capacity of the structure to fulfill the technical requirements for use in serviceability limit states and to fulfill the structural capacity to resist to the ultimate limit states. In general, the outcome of the structural condition assessment is a score that quantifies the operational performance of bridges, which can be subsequently used to support the maintenance programs and to prevent bridge collapses.
Coures and short course on SHM
For more information: https://civilresearchgroup.ulusofona.pt/courses/
Integration of physical modeling, monitoring and machine learning for SHM
In the last decades, the long-term structural health monitoring of civil structures has been mainly performed using two approaches: model- and data-based. The former approach tries to identify damage by relating the monitoring data to the prediction of numerical (e.g., finite element) models of the structure. The latter approach is data- driven, where measured data from a given state condition is compared to the baseline or reference condition. A challenge in both approaches is to make the distinction between the variations of the structural response caused by damage and environmental or operational variability. This research topic intends to promote a hybrid approach that integrates model- and data-based approaches to the structural health monitoring, using machine learning algorithms. Data recorded in situ under regular conditions are combined with data obtained from finite element simulations of more extreme environmental and operational scenarios, and both are input into the training process of machine learning algorithms for damage identification.
State-space Reconstruction
Use of a state space reconstruction to infer the geometrical structure of a deterministic dynamical system from observed time series of the system response at multiple locations. Basically, under a given input, a trajectory will typically evolve towards a subset of the state space, known as an attractor, which has invariant properties such as dimension and stability. However, in real-world cases, one rarely has the ability to measure all the variables needed to describe the dynamics, and so recourse is made to the so-called embedology theorems.
Impact of climate change on the structural health of bridges
Climate change is currently one of the biggest concerns for the health of bridges. Although the uncertainty associated with the magnitude of the change involved is large, the fact that our climate is changing is unequivocal. Bridges tend to involve large investments and must remain in service over long periods of time. As a result, making bridges resilient to climate change is a priority. A well-planned early intervention may save money and lives.
For more information: http://climabridge.ulusofona.pt.
Seminars, Talks, and Courses
“Structural health monitoring of cables with smartphones”, Seminário, 30 janeiro 2024, Universidade Estadual Paulista, Campus de Ilha Solteira, São Paulo, Brasil.
“Monitorização da integridade estrutural de pontes: quando o gémeo não é digital”, Seminário, 27 outubro 2023, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brasil.
“Lições e futuro da rodovia em Portugal”, Keynote, 26 outubro 2023, 1.º Congresso de Infraestrutura dos Transportes da CPLP, CEFET/RJ, Rio de Janeiro, Brasil.
“Does climate change impact structural health monitoring?”, Seminário, 24 outubro 2023, Universidade Estadual Paulista, Campus de Ilha Solteira, São Paulo, Brasil.
“Podemos monitorizar pontes sem dados?”, Seminário, 7 fevereiro 2023, Universidade Estadual Paulista, Campus de Ilha Solteira, São Paulo, Brasil.
“Poderão os telemóveis evitar colapsos de pontes?”, 9 janeiro 2023, Instituto Superior de Engenharia de Lisboa, Lisboa, Portugal.
“Colapsos de pontes: causas e lições”, 17 dezembro 2022, Universidade Federal do Sul e Sudeste do Pará, Marabá, Pará, Brasil (remoto).
“Can structural health monitoring support adaptation of bridges to climate change?”, Seminário, 24 November 2022, Lund University, Lund, Sweden.
“Structural health monitoring”, Seminário, 23 November 2022, Lund University, Lund, Sweden.
“Applicability of smartphones for structural health monitoring”, Seminário, 04 agosto 2022, Universidade Estadual Paulista, Campus de Ilha Solteira, São Paulo, Brasil.
“New trends for structural health monitoring of bridges”, Seminário, 22 julho 2022, Politecnico di Milano, Milão, Itália.
“On the three decades of SHM and how it can prevent the impacts of climate change on bridges”, Seminário, 17 fevereiro 2022, Oslo Metropolitan University, Oslo, Noruega.
“Three decades of pattern recognition and new machine learning trends for SHM of bridges”, Seminário, 8 fevereiro 2022, University of Twente, Enschede, Países Baixos.
“The role and applicability of transfer learning for structural health monitoring”, Seminário, 24 novembro 2021, Universidade Federal do Sul e Sudeste do Pará, Marabá, Pará, Brasil (remoto).
“Structural health monitoring as a tool to keep our bridges safe”, seminário, 24 de novembro de 2021, Silesian University of Technology, Gliwice, Polónia.
“Bridge SHM: From heuristic forms of condition assessment to digital twins”, seminário, 23 de novembro de 2021, Silesian University of Technology, Gliwice, Polónia.
“A Universidade Lusófona, a Faculdade de Engenharia e a Engenharia Civil: Oportunidades, desafios e alguns axiomas”, Semana de Engenharia, Faculdade Lusófona de São Paulo, Brasil, 8 de outubro de 2021 (remoto).
“Short course on Structural Health Monitoring”, 5-7 de Agosto de 2020, Curso de 3 dias, Universidade Federal do Rio Grande do Sul, Rio Grande do Sul, Brasil.
“Introduction to Structural Health Monitoring of bridges”, 28 de Novembro de 2019, Curso de 1 dia, Queen’s University Belfast, Belfast, Irlanda do Norte, Reino Unido.
“A path for supervised learning approaches in SHM of bridges”, Seminário, 27 de novembro de 2019, Queen’s University Belfast, Belfast, Irlanda do Norte, Reino Unido.
“Structural Health Monitoring of Bridges: Where has it come from? Where is it? Where is it going?”, 9 de Maio de 2019, Workshop Topograffi Mileniului II, Technical University of Cluj-Napoca, Roménia.
“Short-course on Structural Health Monitoring”, 15-19 de Outubro de 2018, Curso de 5 dias no Programa de Pós-grduação de Engenharia Mecânica, Universidade Estadual Paulista, Campus de Ilha Solteira, São Paulo, Brasil.
“How machine learning and finite element modeling can be integrated for damage identification in bridges and dams”, 11 de Outubro de 2018, Parque Tecnológico Itaipu, Paraná, Brasil.
“How machine learning and finite element modeling can be integrated for damage identification in bridges and dams”, 9 de Outubro de 2018, Universidade Estadual Paulista, Campus de Ilha Solteira, São Paulo, Brasil.
“Testes dinâmicos na ponte nova sobre o Rio Itacaiúnas em Marabá”, 12 de abril de 2018, Departamento de Engenharia Civil, Universidade Federal do Pará, Belém, Pará, Brasil.
“Combination of machine learning and finite element modeling for structural health monitoring”, 28 de julho de 2017, University of Exeter, College of Engineering, Mathematics and Physical Sciences, Exeter, Inglaterra.
“Merging of model updating and machine learning algorithms for Structural Health Monitoring: applicability, challenges, and opportunities”, 20 de junho de 2017, Katholieke Universite Leuven, Dept. de Engenharia Civil, Leuven, Bélgica.
“Introdução à Monitorização da Integridade Estrutural”, Curso de 2 dias no Programa de Pós-graduação de Engenharia Elétrica (PPGEE) da Universidade Federal do Pará, 20 a 24 de abril de 2017, Belém, Pará, Brasil.
“Monitorização da integridade estrutural e o futuro da manutenção de pontes – uma perspectiva portuguesa”, Seminário na Universidade Federal do Sul e Sudeste do Pará, 27 Agosto 2015, Marabá, Pará, Brasil.
“Paradigma de reconhecimento de padrões para Infraestruturas de Engenharia Civil”, Seminário na Universidade Federal do Sul e Sudeste do Pará, 26 Agosto 2015, Belém, Pará, Brasil.
“Monitoring, SHM, and Pattern Recognition for Bridge Maintenance”, Seminário na Universidade Federal do Pará, Departamento de Engenharia Civil, 19 Agosto 2015, Belém, Pará, Brasil.
“Statistical Pattern Recognition Paradigm for Civil Engineering Infrastructures”, Seminário na Universidade Federal do Pará, Departamento de Engenharia Elétrica e Eletrônica, 10 Agosto 2015, Belém, Pará, Brasil.
“From Structural Health Monitoring to Bridge Management”, Seminário na The University of Sheffield, 20 Outubro 2011, Sheffield, Reino Unido.
“Applicability of the Pattern Recognition Paradigm for Damage Identification in Civil Engineering Infrastructure under Operational and Environmental Conditions”, Seminário na The University of Edinburgh, 11 Fevereiro 2015, Edinburgh, Reino Unido.
“VI Jornadas de Reabilitação de Infraestruturas e de Edifícios”, RIE2017, Universidade Lusófona do Porto, presidente da mesa de uma sessão, 25 de maio 2017, Porto, Portugal.
“Técnica de Reconhecimento de Padrões para Manutenção de Infraestruturas de Engenharia Civil”, RIE2016 – V Jornadas em Reabilitação de Infraestruturas e de Edifícios, Universidade Lusófona do Porto, 12 de Junho 2016, Porto, Portugal.
“O futuro da manutenção de pontes – perspectiva complementar”, RIE2014 – III Jornadas em Reabilitação de Infraestruturas e de Edifícios, Universidade Lusófona do Porto, 12 de Junho 2014, Porto, Portugal.
“Desenvolvimento de uma Plataforma para Integração da Monitorização Estrutural e Sistemas de Gestão de Obras de Arte”, apresentação na Brisa – Estradas de Portugal S.A., 31 de Maio de 2011, Lisboa, Portugal.
“Paradigma de Reconhecimento de Padrões para Infraestruturas de Engenharia Civil“, Seminário, LNEC – Laboratório Nacional de Engenharia Civil, 08 de Novembro de 2010, Lisboa.
“Monitorização de Estruturas“, Palestra na disciplina de Instrumentação e Observação de Obras, ano letivo 2008-2009, curso de Mestrado em Engenharia Civil, FEUP - Faculdade de Engenharia da Universidade do Porto, 3 de Dezembro de 2008, Porto, Portugal.
“Damage Identification in Civil Engineering Infrastructures under Varying Operational and Environmental Conditions“, Conferência LABEST, FEUP - Faculdade de Engenharia da Universidade do Porto, 29 de Maio de 2008, Porto, Portugal.
“Monitorização e Avaliação do Comportamento de Obras de Arte“, Palestra na disciplina de Instrumentação e Observação de Obras, curso de Mestrado Integrado em Engenharia Civil, FEUP - Faculdade de Engenharia da Universidade do Porto, 30 de Abril de 2008, Porto, Portugal.
“Monitorização e Avaliação do Comportamento de Obras de Arte“, Palestra na disciplina de Instrumentação e Observação de Obras, curso de Mestrado em Engenharia Civil, FEUP - Faculdade de Engenharia da Universidade do Porto, 30 de Maio de 2007, Porto, Portugal.