At our group, Structural Health Monitoring (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.
This research topic has been lectured several times in the form of two courses.
As discussed and reviewed in Figueiredo and Brownjohn (2022), the process of implementing a damage identification strategy for existing structures is often referred to as 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.
The SHM process can be posed in the context of a statistical pattern recognition (SPR) paradigm. 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. In this paradigm, the SHM process can be broken down into four steps: Operational evaluation, Data acquisition, Feature extraction and generation, and Statistical modeling development for feature classification.
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)?
The bridge management has been defined as a multidisciplinary field incorporating knowledge from structural engineering, information technology, and economics.
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.
App4SHM is a smartphone application for structural health monitoring (SHM) of bridges or other civil structures to assess their condition after a catastrophic event or when required by authorities and stakeholders. The application interrogates the phone’s internal accelerometer to measure structural accelerations and applies artificial intelligence techniques to detect damage in almost real time.
How does it work? Do you want to know current state condition of your structure? Just place the smartphone on it, record an acceleration time series, and after few seconds you should be able to pick the natural frequencies. Is the structure damaged? After building a reference data set with multiple observations, you should be able to compare a new observation with past ones and raise a flag about the structure condition!
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. As posed in Figueiredo et al. (2019) and Bud et al. (2022), 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.
Can transfer learning be a solution for SHM of bridges? In Figueiredo et al. (2022), we set one of the first publications on transfer learning for bridges. Can monitoring data from one bridge (or small set of bridges) be used to train a classifier that generalizes to another similar bridge? The goal is to create a shared mind built on data sets from several bridges that is superior to individual-bridge minds. In Omori et al. (2023), we laid the foundations of transfer learning for bridges, in which a monitoring data set from one bridge is used to detect damage in another bridge assuming a feature-based transfer learning approach. More recently, in Omori et al. (2023) we proposed a novel approach based on transfer learning in the context of domain adaptation on data sets from two real bridges subjected to retrofit and under-monitoring programs.
Sensor node development is of utmost importance for SHM, as it allows one to bring data acquisition, feature extraction, and statistical modeling for feature classification together into a common framework.
In fact, the importance of data acquisition is paramount, given that assessing structural behavior is the first step in damage identification. Moreover, it sets a foundation for properly tailoring physical models to specific structures, and thereby accurately predicting future behavior. Hence, data collection for informed decision making is fundamental when detecting structural damage and degradation, adapting current structures for climate change, or designing new ones that are out-front more resilient and, therefore, more sustainable. To accomplish these objectives, sensor nodes must be able to provide accurate measurements, in a reliable manner, rapidly enough to allow frequency analysis, and with synchronism to enable the tracking of changes throughout the structure. Finally, they must also be cost-effective to make large deployments, and routine assessments possible.
At this point, two sensor types of nodes are in development: (i) Surface temperature and atmospheric temperature nodes, designed for building and bridge installations, that can be synchronized via time server, upload data in real time, and thereby track exterior/interior heat exchanges in buildings and bridges; (ii) Accelerometer nodes designed for ample structure deployment and rapid measurements with synchronization capabilities via GPS signal, and store data locally for later integration.
Recently, bender element development has been pursued as a non-destructive test performed in soil specimens to determine the small-strain shear modulus of the soil.
As discussed in Figueiredo et al (2023), the SHM process is proposed as an assessment procedure to evaluate permanently the structural condition of bridges and a warning mechanism to trigger adaptation measures as a function of its predicted vulnerability.
The concept of social awareness is implemented to consider different and often conflicting perspectives of stakeholders, users, and legislators over the outcomes of structural health monitoring concerning the maintenance and safety of infrastructure (e.g., bridges).