In this article, we will briefly review the fundamentals of structural health monitoring for bridge structures. Bridges and transportation infrastructure are subjected to extreme environmental loading conditions, such as snow, rain, storm, and extreme heat. This could cause expedite deterioration process in the construction materials, such as concrete and steel. Moreover, physical loads (caused by increasing traffic, overloading, and impact by moving trucks, can expedite the ageing and deterioration process. These could negatively impact the load bearing carrying capacity of these structures (Dong et al. 2010).

Statement of Problem

The collapse of the Morandi Bridge in Italy is one of latest disasters occurred in 2018, killing 43 people and injured dozens of others [Wikipedia]. In addition to dramatic consequences of the bridge collapse, the disruption in every nation’s transportation system, especially in the main corridors, has a tremendous impact on the economic growth and daily life of the citizens.

The Minnesota Department of Transportation (Mn/DOT) reported the failure of the I-35W Bridge resulted in an economic loss of $17 million in 2007 and $43 million in 2008. According to this study, the road-user costs due to the detours and unavailability of the river crossing was estimated to be $4,000 per day [Source].

Our understanding of how bridges respond and react to varying operational and environmental demands could allow us to preserve both safety and serviceability of these structures. The recent advances in Structural Health Monitoring (SHM) and computing technology have prompted the deployment of intelligent bridge monitoring systems for proactive maintenance and management.

Structural Health Monitoring for Bridge Structures

Structural Health Monitoring  (SHM) for Bridge Structures is generally referred to the process of design, development, and implementation of a damage detection or characterization strategy for real-time assessment of structural condition. A typical SHM system includes three major components:

  • a sensor network,

  • a data processing system (including data acquisition, transmission, and storage),

  • and a health evaluation system for decision-making support [Housner et al. 1997].

The process of SHM typically involves monitoring of a structure over a certain period of time, whether short- or long-term, using appropriate array of sensors and devices, extraction of damage-sensitive features given by the measurements obtained from the sensors, and analysis of the data to determine the current state of the structure [Tan et al. 2009].

The Advantages of SHM

SHM sensors can seamlessly integrate with automated analysis systems providing 24/7 continuous remote online monitoring of different components in a bridge structure. Collected data from the structure are transmitted back to an on-site acquisition and analysis system that performs all the necessary analysis; When an abnormal response is recorded, warning messages will be created and sent to people in charge of bridge maintenance and monitoring.

Bridge owners and operators can be alerted about potential defects and anomalies through an automated analysis and sequential alarming system. In a very basic SHM setup, alerts regarding the type and location of potential defects will be issued and communicated from the bridge to the bridge owners/managers. In a more advanced setup, information such as the condition ranking, real-time damage location, and early signs of failure can be extracted from the data.

A major concern for most bridge owners is the concern regarding the false alarms. However, recent developments in signal processing and data processing, self-calibration systems, and self-testing functions eliminate or reduce the costly false alarms and unnecessary site visits. In general, a well-designed SHM system has the potential to:

  • Proactively monitor structural performance under operational and environmental variations
  • Extend the remaining life of bridge by reducing failures due to early detection
  • Optimize inspection budgets with real-time condition data
  • Reduce unnecessary maintenance and life-cycle costs
  • Increase confidence in structural integrity and public safety
  • Avoid closures and downtime for routine inspection

State of Practice in Structural Health Monitoring

With recent developments in sensor technology and wireless communication, various structural health monitoring systems have been developed. The following describes three major solutions available for real-time monitoring of bridge structures.

Vibration-based Monitoring

The vibration-based monitoring techniques were among the earliest proposed methods for bridge condition assessment. These techniques are based on the concept that any change in physical properties of a bridge, whether local or distributed, will be captured by evolution in its dynamic properties. In structural engineering, typical dynamic properties refer to modal parameters such as natural frequencies, mode shapes, etc. Since the modal parameters are highly correlated with the structure dynamic properties, the location and the severity of structural defect can be determined by changes in the modal characteristics.To study the dynamic properties of the bridge, the vibration response of the structure should be measured under controlled or ambient excitation. Most of the vibration-based monitoring methods mainly use data obtained from accelerometers for monitoring the bridge response at global level. The accelerometers are commonly used to measure vertical and/or horizontal accelerations of the bridge components at specific locations. Depending on the design of sensor instrumentation layout, the information obtained from these sensors can be used for detection and localization of abnormal events.

Strain-Based Monitoring

While vibration-based monitoring approaches are suitable for monitoring the bridge structural behavior at global level, their efficiency to locally monitor slight changes in structural dynamic properties is questionable. Hence, a more recent trend is to use local monitoring methods to notice negligible changes in mechanical properties of bridge components.

Since damage is usually occur at local level, strain monitoring methods can be used as a means to locally monitor key bridge elements at critical locations. Fiber optic sensors and strain gauges are among the most popular sensors used for strain monitoring. Placing a network of strain sensors can help determine the bridge components subjected to excessive stress or deformation due to an abnormal event such as bridge over loading.

Acoustic Emission Emission (AE) technology is also used for real-time examination of bridge components under stress. AE waves are stress waves that are generated by rapid release of strain energy due to micro structural changes in a material and can travel through the structure. These waves are raised from localized source(s) within a material and have the ability to locate the source initiation.Varying load conditions that exist in bridge components such as steel and concrete can cause these elements to emit energy in the form of elastic waves due to various material-relevant damage mechanisms. These waves can be captured by means of AE sensors placed on the surface of bridge component. Analysis of the signals received from the sensors provides information about the source of emission.

Acoustic Emission remote sensors continuously listen to a specific sound wave or emission in the material; An AE system can be customized to send alerts and notify bridge operator when:

  • a crack initiates or grows
  • a suspension wire or cable breaks
  • an accidental impact occurs
  • dislocation or deterioration takes place
  • active corrosion propagates

Practical Challenges

Structural Health Monitoring for Bridge Structures have the potential to collect significant amount of information about the damage state of various structural components at the global and element level. This massive data can serve as a source of knowledge on the performance history of the structure, something valuable for decision makers.

Modern SHM systems that are fully integrated with statistical models appear to be a promising solution that will reduce the downtime of the civil infrastructure, reduce maintenance cost, and improve worker safety by eliminating high risk activities (i.e. rope access visual inspection, work at height, exposure to radiation, work in confined space). However, implementing a reliable network of sensors, and understanding the collected data remains challenging.

On the hardware front, one of the main challenges is to design an efficient structural sensor network that can collect adequate and reliable information about the state of damages. In this stage, instrumentation engineer is faced with three major questions, that is about the type, location, and number of sensors for instrumenting the structure of interest. Ownership, Security and management of data is another challenge.

On the analysis front, making meaningful interpretation of real-time data, and integrating them into decision-making protocols will be challenging. While there is a wide variety of SHM sensors in the market, less attention has been paid to development of data-driven decision-making support algorithms. An objective decision-making tool does a deep dive into big data and help alert bridge owners of any appreciable change in the current state of the structure. The rise of big data and Cloud computing will help engineers have a better look in real-time data, and efficiently translate those into decision-making outcome for owners and maintenance managers.

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