Structural Health Monitoring: Everything You Need to Know

Structural monitoring (Structural Health Monitoring) is essential to prevent any issues that could undermine public safety. Let’s see what it is and how these activities are developing thanks to the application of new technologies and innovative digital systems for the monitoring and maintenance of structures.

To facilitate your work, a structural health monitoring software is necessary so you can gather all the information needed to ensure integrity and safety for buildings, bridges, roads, and all other types of infrastructure.

Structural Health Monitoring

Elements of a Structural Health Monitoring System

A structural health monitoring system is a set of tools and procedures designed to monitor and assess the condition of a structure, such as a building, bridge, dam, or other infrastructure. It includes sensors, data acquisition hardware, BIM IoT Platform for data analysis and interpretation, and strategies to make decisions based on the acquired data.

These systems are essential for ensuring safety, identifying potential structural issues, and planning maintenance activities:

1. sensors: sensors are the most fundamental elements of a structural monitoring system. They can include accelerometers, inclinometers, strain gauges, temperature sensors, humidity sensors, and many others. These sensors detect and record various physical parameters that may indicate changes in the condition of the structure;
2. data acquisition hardware: this component collects data from the sensors and transmits it to the data processing system. This can include data acquisition modules, data transmission systems, and other devices;
3. data processing software (SHM – Structural Health Monitoring software): this is the heart of the structural monitoring system. This software receives data from the data acquisition hardware, processes it, and produces useful results. These results can include graphs, charts, tables, and other visualizations that make the data easily comprehensible;
4. data interpretation models: these are algorithms or procedures used to interpret the collected data. These models can help identify trends, detect anomalies, and predict possible future issues;
5. response protocols: these are action plans that are implemented when the structural monitoring system detects a problem. These can include maintenance procedures, evacuation, or other interventions.

In conclusion, a structural health monitoring system is a complex set of components that work together to monitor and maintain the integrity of a structure. The success of these systems depends on the quality of the sensors, the effectiveness of the hardware and software for data acquisition and processing, and the ability to interpret and respond appropriately to the collected data.

Structural Monitoring in Civil Engineering

Structural Health Monitoring in civil engineering involves the observation and tracking of a structure’s condition over time, using sensors and measuring instruments, with the goal of identifying potential issues before they become critical. These activities fall within the broader practices of infrastructure monitoring used to ensure safety, preventive maintenance, and asset management.

Civil engineers employ a variety of techniques and tools to monitor structures. These include, but are not limited to, strain gauges, accelerometers, humidity sensors, temperature sensors, displacement sensors, etc. Such instruments detect changes in structural conditions (movements, deformations, changes in temperature or humidity) and transmit information to a data acquisition system.

The collected data is then analyzed using data processing software. These programs can interpret large amounts of data and identify patterns and trends. Through the analysis performed, engineers can determine whether the structure is undergoing stress or damage, and if intervention is necessary.

Another critical aspect of structural monitoring in civil engineering is the planning and implementation of response protocols. When the monitoring system detects a problem, these protocols provide a roadmap for the actions to be taken, which may include further inspections, repairs, structural reinforcements, or, in more severe cases, evacuation and demolition.

Thus, thanks to the use of advanced technologies and the ability to analyze and interpret large amounts of data, civil engineers can prevent structural issues, minimize maintenance costs, and ensure the safety of people and property.

Structural Health Monitoring in civil engineering

Structural Monitoring of Bridges

This system is not limited to the post-construction period but begins as early as the design phase. During this phase, engineers use a series of calculation tools to predict the behavior of the structure under various loads and environmental conditions. However, these models are based on a series of assumptions that may not exactly match reality. Therefore, once the bridge is built, constant monitoring is necessary to verify whether the actual behavior of the structure corresponds to the predictions.

The main objective of structural monitoring of bridges is to ensure the safety of the infrastructure and those who use it. To achieve this, engineers must detect any anomalies in the behavior of the structure, such as excessive movement, unexpected deformations, or damage caused by environmental events or traffic loads. This is done through the use of a series of sensors installed on the bridge (accelerometers, strain gauges, gyroscopes, thermocouples, etc.).

The data collected by the sensors is then analyzed using specialized software, which allows for the identification and interpretation of any variations from expected behavior models. If the data indicate a potential problem, engineers can intervene promptly to carry out repairs or structural reinforcements, thereby preventing accidents or more severe damage.

Moreover, the structural monitoring of bridges also contributes to their maintenance and management in the long term. By analyzing historical data, engineers can better understand how the structure responds over time to various factors such as traffic load, weather conditions, material aging, etc. This allows for more effective maintenance planning and prevention of issues before they occur.

Finally, only through accurate and continuous monitoring is it possible to ensure the safety and efficiency of bridges, safeguarding people’s lives and contributing to the sustainability of our infrastructure.

Structural Monitoring of Buildings

Similarly to bridges, buildings require constant monitoring to ensure their safety and integrity, and can reveal signs of degradation, damage from events such as earthquakes or floods, or simply the natural wear of time.

To provide an accurate assessment of the structural health status of a building, it is important to consider a series of fundamental phases and components that combine with each other:

1. monitoring planning: involves defining the objectives of the monitoring, selecting the parameters to be measured, and designing the monitoring system. Objectives may vary based on the type of structure, its current condition, the use of the building, and other factors. Parameters to monitor may include structural movements, deformations, stresses, temperatures, humidity, vibrations, and more;
2. sensor installation: depending on the selected parameters, various types of sensors are installed in the building. These may include accelerometers to measure vibrations, strain gauges to monitor deformations, thermocouples for temperature, hygrometers for humidity, and so on. The position and configuration of the sensors are chosen based on the structure of the building and the critical points to monitor;
3. data collection: the sensors collect data in real time or at predetermined intervals and send it to a data acquisition system. This system can be based on a local computer or on a cloud platform, allowing for data recording, storage, and processing;
4. data analysis: the collected data is then analyzed using specific software. These tools allow for the identification of any variations from reference values, signaling possible anomalies or structural problems. The analysis can also include modeling and simulation techniques to predict the future behavior of the structure;
5. intervention: based on the results of the analysis, engineers may decide to carry out corrective or maintenance interventions on the structure. These may include structural reinforcements, repairs, modifications to the load or use of the building, and other measures;
6. reporting and communication: finally, the results of the monitoring and the actions taken are documented and communicated to stakeholders. This may include building owners, regulatory authorities, insurers, and others.

Structural Durability and Health Monitoring

What has been said above serves to ensure structural durability through health monitoring, aspects that go hand in hand.

Durability refers to a structure’s ability to withstand the test of time and endure environmental conditions, and it is a fundamental characteristic in structures. It is influenced by a series of factors, including the materials used, construction techniques, environmental conditions, the use of the structure, and maintenance practices. To ensure structural durability, it is necessary to anticipate and manage a series of potential problems and risks, such as material deterioration due to adverse environmental conditions, excessive loads, vibrations, construction defects, and others. Health monitoring, on the other hand, helps to ensure that the structure maintains this durability.

Through an integrated approach that combines quality design and construction with effective monitoring and maintenance, it is possible to ensure the safety, functionality, and durability of structures over time.

Maintenance scheduling

Artificial Intelligence in Structural Monitoring: A Machine Learning Perspective

Artificial Intelligence (AI) and machine learning have greatly changed the field of Structural Health Monitoring. Machine learning algorithms can analyze vast amounts of data collected from sensors, identify patterns and trends, and predict potential structural problems with unprecedented accuracy. This opens up new possibilities for structural monitoring, making our infrastructures even safer and more efficient.

The application of artificial intelligence (AI) in structural monitoring relies on the AI’s ability to process and interpret large amounts of data and learn information from it. AI can therefore help identify and predict structural problems, improving the efficiency and effectiveness of monitoring and maintenance.

Here are some ways AI can be applied in structural monitoring:

1. anomaly detection and diagnosis: machine learning algorithms can be trained to recognize patterns in structural monitoring data, identify anomalies or variations from these patterns, and diagnose potential problems. For example, an algorithm might be trained to detect variations in the vibrations of a bridge that could indicate structural damage;
2. structural deterioration prediction: AI can be used to develop predictive models that estimate when and where structural problems might occur based on various factors, such as historical monitoring data, environmental conditions, and the characteristics of the structure. For instance, corrosion in a steel building could be predicted based on factors like humidity and temperature;
3. maintenance optimization: AI algorithms can help schedule and optimize maintenance operations, for example by determining the optimal time to carry out repairs to minimize costs and impact on operations. This is often referred to as predictive maintenance;
4. image analysis: AI can be used to analyze images or videos of structures obtained through drones or other technologies. Machine learning algorithms can assist in identifying specific features, such as cracks or deformations, that may indicate structural damage.

Just to provide an example, recently some engineering companies have begun using drones equipped with high-resolution cameras and AI algorithms to inspect the structures of bridges and buildings. The algorithms analyzed the images to detect potential problems, such as cracks or corrosion, that are difficult to spot with the naked eye. This allows for faster, safer, and more effective inspections compared to traditional methods.

Structural monitoring is a rapidly evolving field that plays a fundamental role in the safety and efficiency of our infrastructures. With the advent of technologies such as artificial intelligence and machine learning, the possibilities are truly limitless.

The Future of Building Control and Structural Monitoring: The Integration of IoT and Digital Twin

Technological evolution allows for the implementation of continuous and permanent structural monitoring systems, capable of transforming property management from reactive to proactive. This paradigm shift is evident in the results of a recent prototype experiment conducted in Italy on the “Condominio Libertà” in Reggio Calabria.

The project, born from the collaboration between the academic spin-off BIG of the Mediterranea University of Reggio Calabria, Sisdev, ACCA Software, and the company Berna Engineering, demonstrates how the combination of informational modeling and the Internet of Things (IoT) represents the new frontier for safeguarding human life and the resilience of the built environment.

Through this approach, structural monitoring of buildings is no longer an emergency measure but a tool for continuous knowledge of the work. The combination of real-time data and digital models allows for the timely detection of even the slightest signs of deterioration. This drastically reduces the risk of urgent, costly, and impactful interventions, while simultaneously optimizing management costs and accelerating the restoration of activities and usability following catastrophic events.

From Scan-to-BIM Survey to Digital Twin for Structural Health Monitoring

The operational heart of a modern seismic and structural monitoring system lies in the creation of an interactive digital twin. In the mentioned case study, the process started with the survey and digital representation of the building using the Scan-to-BIM methodology. This activity has allowed the generation of a high-precision as-built structural and architectural model, essential for planning the exact deployment plan of sensors. The innovative aspect of digital twin structural health monitoring technology lies in the bidirectional connection between the three-dimensional geometric model (in IFC format) and the IoT management cloud platform. Within the digital twin, each physical sensor is mapped with a unique reference code. In this way, the platform dynamically animates the 3D model, changing the colors and labels of the elements in real-time based on the detected stresses. This flexibility allows for the setting of conditional color rules to visually highlight critical situations where values exceed the established safety thresholds, providing managers with an immediate understanding of the behavior of the structure.

Sensor Technology for Structural Monitoring: Wireless Networks and Multi-Sensor Nodes

The choice of sensors is a crucial piece in designing an efficient structural health monitoring ecosystem, especially when working on existing building stock. Traditional reinforced concrete structures, such as the six-story building subject to the Calabrian testing, require strategic positioning to avoid data redundancy. To govern the seismic response of a building, it is necessary to investigate the two main factors that determine it: mass (usually concentrated in the slabs) and stiffness (represented by load-bearing elements, stairwells, and the nodal points furthest from the center of mass).

To meet these needs without impacting the aesthetics or functionality of living spaces, today’s technology favors the use of wireless multi-sensor nodes. The absence of cables and the use of long-range, low-power transmission protocols, such as LoRaWAN, facilitate installation in any accessible point of the structure. A single node positioned on the structure can simultaneously store and analyze up to ten different parameters, including temperature, inclinometry, acceleration, displacement, and deformation. In static and dynamic structural monitoring, the distinction between the latter two parameters is essential: while displacement sensors monitor the evolution of physical cracks, deformation sensors (known as strain gauges) measure the internal expansions of materials to assess the actual mechanical response of the structure under load or during an earthquake.

Big Data Management and Interactive Dashboards for Predictive Maintenance

The enormous amount of data generated by the numerous measurement points distributed across the various floors of the building requires advanced cloud computing and visualization tools to avoid overwhelming the end user. Modern IoT structural health monitoring platforms overcome this problem by introducing the logic of “structured views,” which allow for isolating specific levels or areas of the building to conduct targeted analyses. In addition to real-time monitoring, the associated software systems allow for structuring interactive dashboards that record and archive data over time in the form of time series. The analysis of these archives, combined with the application of machine learning models and predictive simulations, enables the detection of latent anomalous patterns or long-term deterioration trends. Thus, it moves from simple monitoring to a true tool for preventive and predictive maintenance, capable of generating automatic alerts and providing engineers, builders, and managers with key information to protect people and enhance the resilience of residential and infrastructure assets.

IoT Structural Health Monitoring Case Study: The Integration Between Open BIM and IoT Sensors

Technological evolution in the construction sector is radically redefining the ways in which the safety of our infrastructures is guaranteed. A concrete example of this digital transformation is represented by the research and development project called “Open IBAM Advance Visual Technologies,” conducted in collaboration with the Department of Structures for Engineering and Architecture of the University of Federico II in Naples. This project demonstrated how the application of the Bridge Guidelines issued by the Ministry of Infrastructure and Transport can be optimized and digitized through the use of Open BIM standards and innovative structural monitoring systems. The fundamental goal is to overcome the technological limitations of traditional systems, providing all stakeholders involved with a collaborative, interoperable, and highly precise management of structural safety.

The Regulatory Context and the Centrality of SHM

The 2022 Bridge Guidelines introduce a multi-level approach to assess the safety of existing structures, moving from the acquisition of basic information from the census (Level 0) and visual inspections (Level 1) to the algorithmic calculation of the attention class (Level 2). When significant risk classes or vulnerabilities are detected, the adoption of systems for continuous SHM (structural health monitoring) becomes a fundamental strategic choice for the manager. This type of structural monitoring allows for the acquisition of real-time data streams on the response of the structure to environmental and traffic stresses, guiding the decisions of operators and extending the useful life of the structures. The same methodological approaches and the use of advanced tools for structural monitoring are now applied not only in infrastructures but also in building structural monitoring, offering essential predictive strategies for safeguarding the built heritage.

The Case Study: The Napoli-Salerno Viaduct

The validity of this approach has been tested in the field through a specific case study: a high-arch viaduct located on the Napoli-Salerno highway. The structure in question is made of reinforced concrete and has an overall span of 120 meters, divided into 11 spans of 10 meters each. To accurately identify the structural behavior of the viaduct, a plan for IoT Structural Health Monitoring based on the targeted placement of advanced sensors for structural monitoring was designed. Specifically, the system includes the installation of accelerometric sensors positioned on the decks and pressure sensors distributed on arches and pillars, all connected to data acquisition units.

This configuration enables effective static and dynamic structural monitoring, constantly monitoring both responses to vibrations and changes in pressure and load on the structure.

The Digital Workflow: LOIN and IDS Standards

The true innovation of the case study lies in the fully digitalized information flow management within the usBIM Platform environment. The process began with the definition of the LOIN (Level of Information Need), regulated by European standards also adopted at a national level, which specifies the quality, quantity, and granularity of the geometric and alphanumeric data necessary for information exchange.

The LOIN requirements have been translated into a computer-readable electronic format (XML) using the IDS (Information Delivery Specification) standard developed by BuildingSmart International. The use of IDS has guided the BIM modeler both in the preliminary phase and in the execution phase, inserting the correct properties of the structural health monitoring sensors into the IFC models (such as minimum accuracy requirements or the number of axes).

Additionally, the application has automated the validation of the output model, promptly signaling to the validator any anomalies or deficiencies concerning the requirements established during the commissioning phase.

Towards Real-Time Structural Control and Digital Twins

The native integration of data from IoT sensors within 3D informational models represents the fundamental evolutionary step towards the creation of a digital twin structural health monitoring. Through digital dashboards or management dashboards, the structural elements of the IFC model can dynamically change color based on the measurements taken on-site, providing an immediate visual response regarding the health status of the structure.

This standardized workflow, in addition to optimizing the management processes of highway viaducts, outlines an excellence methodology for the control and structural monitoring of complex or strategic buildings.

Thanks to the transparency of data and the interoperability guaranteed by Open BIM, managers finally have a scientific tool to intercept degradation phenomena at an early stage and plan maintenance investments rationally.

What you can do with a structural health monitoring software

To create an IoT bridge monitoring system based on a BIM model with a structural health monitoring software, you start by developing the IFC model of the infrastructure.

In this model, sensors (accelerometers, inclinometers, temperature sensors, etc.) are integrated, each accompanied by relevant IFC properties. Each sensor is associated with the monitored structural elements, allowing data to be collected and visualized in real-time directly within the model.

The data can be filtered by sensor type or bridge section, such as the deck, facilitating the analysis of the information. A management system allows viewing all infrastructures in a georeferenced environment, with the ability to create specific views, highlight sensors, and analyze historical and real-time data. Additionally, advanced BIM interactions can be activated to optimize the management and analysis of data from monitoring, thus improving predictive maintenance and structural management.

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FAQ Structural Health Monitoring

What is structural health monitoring (Structural Health Monitoring) and what elements does it comprise?

A structural health monitoring system is a set of tools and procedures designed to monitor and assess the conditions of a structure, such as a building, bridge, dam, or other infrastructure. It includes several fundamental elements that work together: sensors (accelerometers, inclinometers, deformation sensors, temperature, humidity, etc.) that detect and record various physical parameters; data acquisition hardware that collects data from the sensors and transmits it to the processing system; data processing software (SHM software) that receives the data, processes it, and produces useful results such as graphs, charts, and tables; data interpretation models, which are algorithms used to identify trends, anomalies, and predict future issues; and finally, response protocols, which are action plans (maintenance, evacuation, or other interventions) implemented when the system detects a problem.

How is Structural Health Monitoring applied in civil engineering?

In civil engineering, it involves observing and tracking the conditions of a structure over time using sensors and measuring instruments to identify potential issues before they become critical. Civil engineers use techniques and tools to detect changes in structural conditions (movements, deformations, thermal or hygrometric variations) and transmit the information to a data acquisition system. This data is analyzed with processing software to determine if the structure is experiencing stress or damage and whether intervention is necessary. Additionally, response protocols are planned and executed, providing a roadmap for actions to be taken, such as further inspections, repairs, structural reinforcements, or, in more severe cases, evacuation and demolition.

What are the peculiarities of structural bridge monitoring?

Bridge monitoring is not limited to just the post-construction period but begins from the design phase using calculation tools to predict the behavior of the structure. Once the bridge is built, continuous monitoring serves to verify whether the actual behavior matches predictions. The primary goal is to ensure the safety of the infrastructure by detecting anomalies (excessive movement, unexpected deformations, damage from environmental events or traffic loads) through installed sensors (accelerometers, deformation sensors, gyroscopes, thermocouples, etc.). The collected data is analyzed by specialized software to identify variations from expected models, allowing for timely repairs and effective long-term management and maintenance based on the analysis of historical data related to material aging and meteorological or traffic factors.

What are the fundamental phases and components of structural monitoring of buildings?

Building monitoring to ensure safety and integrity consists of several phases and components: 1) Monitoring Planning: defining objectives based on the type and use of the building and selecting parameters to measure (movements, deformations, stresses, vibrations, etc.); 2) Sensor Installation: positioning accelerometers, strain gauges, thermocouples, or hydrometers at critical points; 3) Data Collection: sending data in real-time or at predetermined intervals to a local or cloud acquisition system; 4) Data Analysis: using specific software, models, and simulations to identify variations from reference values and report anomalies; 5) Intervention: executing corrective or maintenance measures such as structural reinforcements or load modifications; 6) Reporting and Communication: documenting and sharing results with stakeholders (owners, authorities, insurers).

What is the link between structural durability and structural health monitoring?

Durability refers to a structure’s ability to withstand over time and endure environmental conditions, influenced by materials, construction techniques, use, and maintenance practices. To ensure durability, it is necessary to manage risks such as material deterioration, excessive loads, vibrations, and construction defects. Structural Health Monitoring helps ensure that the structure maintains this durability over time. Through an integrated approach that combines quality design and construction with effective monitoring and maintenance, it is possible to preserve the safety, functionality, and durability of works.

How are artificial intelligence and machine learning applied in Structural Health Monitoring?

Artificial intelligence and machine learning allow for processing and interpreting vast amounts of data from sensors to identify patterns, trends, and predict problems with high precision. They are applied in various ways: in the detection and diagnosis of anomalies (e.g., recognizing variations in the vibrations of a bridge indicating damage); in predicting structural deterioration through predictive models based on historical and environmental data (e.g., estimating the onset of corrosion due to humidity and temperature); in maintenance optimization (predictive maintenance to minimize costs and disruptions); and in analyzing images or videos captured by drones, where algorithms identify cracks or deformations that are difficult to detect with the naked eye, making inspections faster, safer, and more effective.

What does the integration of IoT and Digital Twin entail in the control and structural monitoring of buildings?

The integration of the Internet of Things (IoT) and Digital Twin represents the new frontier for safeguarding human life and the resilience of built environments, transforming real estate asset management from reactive to proactive. As demonstrated in the testing of the ‘Condominio Libertà’ in Reggio Calabria, the process begins with the Scan-to-BIM survey to generate a high-precision as-built structural and architectural model. The Digital Twin technology creates a bidirectional connection between the 3D geometric model (in IFC format) and the IoT cloud platform. Within the digital twin, each physical sensor is mapped with a unique code: the platform dynamically animates the 3D model by modifying colors and labels in real-time based on detected stresses, applying conditional coloring rules when safety thresholds are exceeded.

What technologies and criteria guide the choice of sensors for structural monitoring?

In existing structures, the use of wireless multisensor nodes is preferred to avoid the deployment of cables and facilitate installation through long-range and low-power protocols such as LoRaWAN. A single node can simultaneously analyze up to ten parameters (temperature, inclinometry, acceleration, displacement, and deformation). The choice requires strategic positioning to avoid redundancies, exploring the factors that govern the seismic response: mass (concentrated in the floors) and stiffness (load-bearing elements, stairwells, and nodal vertices). In static and dynamic monitoring, it is essential to distinguish between displacement sensors (which monitor the evolution of physical cracks) and deformation sensors or strain gauges (which measure the internal expansions of materials to assess the mechanical response of the structure under load or during an earthquake).

How is the management of Big Data and the use of interactive dashboards for predictive maintenance carried out?

The huge amount of data generated by sensors is managed through cloud platforms for IoT Structural Health Monitoring that introduce the logic of ‘structured views’, allowing specific levels or areas of the building to be isolated without overloading the visualization. The system allows for the structuring of interactive dashboards that record and archive data over time in the form of time series. The analysis of these historical data, combined with machine learning models and predictive simulations, enables the identification of latent abnormal patterns or deterioration trends over the long term. This transforms monitoring into a tool for preventive and predictive maintenance, capable of generating automatic alerts useful to engineers, builders, and managers to protect people and enhance the resilience of residential and infrastructural assets.

What can be concretely done with integrated structural health monitoring software and BIM?

With structural health monitoring software, it is possible to create an IoT monitoring system based on a BIM model (for example, of a bridge) by developing the IFC model of the infrastructure. In this model, sensors (accelerometers, inclinometers, temperature sensors, etc.) are digitally integrated, equipped with their IFC properties and associated with the monitored structural elements, allowing data to be collected and visualized in real-time directly in the 3D model. Data can be filtered by sensor type or structural section (such as the slab), facilitating analysis. The system also allows visualization of all infrastructures in a georeferenced environment, creating specific views, highlighting sensors, analyzing historical data, and activating advanced BIM interactions to optimize predictive maintenance.