Australia Is Becoming Hotter: Climate Change and Its Impact on Long-term Monitoring of Structures

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Australia is experiencing significant climate change, which has led to more extreme weather events, posing challenges to monitoring the structural integrity of critical infrastructure. Structural health monitoring systems are essential for detecting early signs of damage in structures such as bridges, buildings, and dams, ensuring their safety and longevity. However, structural health monitoring methods, which rely on dynamic characteristics like natural frequencies and mode shapes, are susceptible to temperature changes. Climate change introduces a new variable that traditional nondestructive techniques may not adequately address, potentially leading to false alarms and impaired damage detection. Despite the critical role of temperature in influencing structural behavior, limited research has focused on its effects on natural frequencies and how to distinguish temperature-induced shifts from actual damage. Researchers at Australian research institutes are well-positioned to construct a large-scale benchmark structure to collect vibration data under various temperature and damage scenarios, thereby contributing to further studies that are needed to fully understand the nature of temperature changes in long-term monitoring of structures. Such initiatives would enhance the accuracy of structural health monitoring systems, foster international collaborations, and ensure the safety and performance of Australia's aging infrastructure in the face of climate change.

Since national records began in 1910, Australia has experienced an average increase in temperature of 1.51 ± 0.23 degrees C, with most of this warming happening after 1950. Each decade since then has consistently been warmer than the one before it. Globally, land areas are experiencing a warming trend that outpaces the oceans. This is particularly evident in Australia, where land temperatures increase about 40% faster than the surrounding waters.

The Sun is Earth's primary source of energy. To keep surface temperatures stable over time, the energy that reaches Earth must be offset by an equivalent amount of heat released back into space. Greenhouse gases like CO2 in the atmosphere hinder the release of this heat, contributing to higher temperatures of the Earth's surface, oceans, and atmosphere. Research conducted in Australia and globally, along with The Intergovernmental Panel on Climate Change (IPCC)’s Sixth Assessment Report, shows that Australia's future climate will keep warming, bringing more extremely hot days and fewer extremely cold ones.

Australia’s susceptibility to extreme weather events, such as cyclones, floods, and bushfires, makes structural health monitoring essential for surveying structural impacts and ensuring resilience during and after such events. Additionally, many of Australia’s critical infrastructure assets, including bridges like the Sydney Harbour Bridge, are decades old. Structural health monitoring ensures these structures remain safe and functional, extending their service life. A group of researchers formed the Australian Network of Structural Health Monitoring (ANSHM) on June 30, 2009, at the inaugural ANSHM Workshop, and the association has developed extensively since then.

Structural health monitoring involves collecting and analyzing data from structures to assess their condition and ensure their safety and performance. The measurements generally are dynamic data like acceleration or static data like strain. Acceleration is measured using sensors known as accelerometers, which enable extraction of dynamic characteristics of the structure, such as natural frequencies and mode shapes. Damage typically changes the natural frequencies and mode shapes. Therefore, tracking changes in natural frequencies and mode shapes can reflect structural damage or degradation. Recent studies have shown that natural frequencies are significantly sensitive to temperature changes. Climate change potentially introduces a new variable beyond the detection capabilities of conventional non-destructive methods, adding another source of uncertainty to structural health monitoring.

If climate change and its associated effects are not considered in the long-term monitoring of structures, the sensitivity of natural frequencies to temperature changes could lead to significant impairment of the effectiveness of structural health monitoring systems and report of false alarms. In other words, temperature variations can cause significant shifts in the natural frequencies of materials, potentially masking or mimicking damage-related changes in vibration patterns. Therefore, it is essential to remove the effects of temperature on the natural frequencies for accurate damage detection.

Despite the critical role temperature plays in influencing the dynamic behavior of structures, relatively limited research specifically focuses on its effects on natural frequencies. The existing body of research has not fully explored how these temperature-induced frequency shifts interact with actual structural degradation or how they can be effectively distinguished from damage signals in real-world structural health monitoring applications.

Benchmark structures for structural health monitoring are designed to evaluate and compare newly developed algorithms and methodologies. Additionally, less thoroughly understood structural behaviors and complex phenomena, such as temperature changes, can be explored by providing controlled, repeatable, and standardized environments that are challenging to analyze in real-world settings. For example, composite materials, such as fiber-reinforced polymers, exhibit behaviors that are less thoroughly understood compared to traditional materials like steel or concrete. Therefore, Stanford Structures and Composites Laboratory, in collaboration with the Prognostics Center of Excellence at NASA Ames Research Center, tested fatigue aging on carbon fiber reinforced polymer (CFRP) composites. This dataset, available from the NASA Prognostics Data Repository, enables researchers to evaluate the performance of newly developed frameworks for fatigue damage propagation in CFRP composites using experimental measurements and study the composite structures’ behavior under cyclic loads.
Another benchmark structure that focuses on less comprehended subjects in structural health monitoring is the Qatar University Grandstand Simulator. This benchmark structure offers researchers an experimental dataset of different joint damage scenarios and enables them to evaluate their damage detection solutions based on a standard dataset. The Qatar University Grandstand Simulator is distinguished from existing benchmark problems in structural health monitoring by introducing joint damage scenarios instead of elemental ones.

Regarding temperature effect on long-term monitoring of structures, very few benchmark structures incorporate temperature to evaluate their impact on structural response and damage detection. For example, the Z24 Bridge, a well-known benchmark in the structural health monitoring community, was a concrete highway bridge in Switzerland monitored extensively before its demolition. It is one of the most widely used full-scale structures for studying environmental effects, including temperature changes, on vibration measurements. Another benchmark structure to study the significant influence of environmental variability on the modal data, specifically natural frequencies, is the Wooden Bridge. Wooden Bridge is a laboratory-scale truss structure, and damage scenarios were designed by adding different point masses. In this example, environmental variations have evidently induced frequency shifts significantly larger than those caused by structural damage.

Built in 1983, a long-span, pre-stressed concrete cable-stayed bridge named Tianjin Yonghe opened to traffic at the end of 1987 in China. The total length of the bridge is 510 meters, with multiple spans, a couple of towers, and several cables. The bridge was closed and repaired between 2005 and 2007 because of several damage patterns detected at the bottom of the mid-span girder and corroded cables. According to the structural health monitoring system implemented at the end of 2007, 14 uniaxial accelerometers were mounted on the bridge deck to continuously measure acceleration. Additionally, an anemoscope to measure the wind speed and a temperature sensor to measure air temperature were attached. In August 2008, periodic inspections identified multiple damage patterns. Researchers found that wind speed and air temperature had substantial effects on the variability in the bridge’s natural frequencies. For example, Sarmadi et al. conducted an operational modal analysis based on fast Fourier decomposition and identified 24 natural frequencies per day as dynamic features. In contrast to the Z24 Bridge, which is influenced by a single dominant environmental factor, the natural frequencies of the Yonghe Bridge are affected by multiple environmental factors, including temperature and wind speed. The Tianjin Yonghe Bridge is not a standard benchmark, and its measured environmental data (air temperature and wind speed) is not available for research purposes. However, the Tianjin Yonghe Bridge is still one of the few structures that researchers can utilize to study the environmental effects on the acceleration responses.

The I-40 bridge over the Rio Grande in Albuquerque, N.M., is one of the well-known benchmark structures in structural health monitoring. A group of researchers from New Mexico State University and Los Alamos National Laboratory obtained the modal properties before it was razed in 1993. Los Alamos National Laboratory has published the natural frequencies and mode shapes of the bridge in healthy and several damage scenarios. Additionally, the group used ABAQUS to assemble a full-scale finite element model of the I-40. Acceleration responses of this structure have not been published, which may restrict researchers from thoroughly studying the impact of temperature changes on the accuracy of structural damage detection methods. Analysis of the experimental data indicates that ambient temperature has a significant influence on the changes in natural frequencies. Given that the natural frequencies of the bridge are directly related to its stiffness, it is anticipated that these frequencies would reduce as damage progresses. However, the findings indicate that the frequency magnitudes actually increase at the first two levels of damage. The increase in the magnitude of the natural frequencies is attributed to a decrease in the ambient temperature.

Designing and constructing a large-scale benchmark structure and collecting an experimental dataset can be useful to fully understand the effect of temperature change in the long-term monitoring of structures if the following recommendations are met.

About the Author

Parsa Ghannadi is an author, researcher, and civil and structural engineer. He works in the Department of Civil Engineering, Ahar Branch, Islamic Azad University, Ahar, Iran, and a BINDT Affiliate Member.

Seyed Sina Kourehli is an associate professor of the Department of Civil Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.

References

1.         Australia's changing climate: Australia's weather and climate including temperature, fire weather, rainfall, heavy rainfall, streamflow, tropical cyclones, snowfall: State of the Climate 2024. 2024; Available from: https://www.csiro.au/en/research/environmental-impacts/climate-change/state-of-the-climate/australias-changing-climate.

2.         Future climate: Explore the changes Australia's climate is projected to experience in the coming decades, including climate extremes: State of the Climate 2024. 2024; Available from: https://www.csiro.au/en/research/environmental-impacts/climate-change/state-of-the-climate/future-climate.

3.         Xu, R., et al., Climate change, environmental extremes, and human health in Australia: challenges, adaptation strategies, and policy gaps. The Lancet Regional Health–Western Pacific, 2023. 40.

4.         Sydney Harbour Bridge. Available from: https://en.wikipedia.org/wiki/Sydney_Harbour_Bridge.

5.         Australian Network of Structural Health Monitoring (ANSHM). 2018; Available from: https://www.anshm.org.au/introduction.html.

6.         Nguyen, A., T.H. Chan, and X. Zhu, Special Issue: Real World Application of SHM in Australia. Structural Health Monitoring, 2019. 18(1): p. 3-4.

7.         Farrar, C., N. Dervilis, and K. Worden, The Past, Present and Future of Structural Health Monitoring: An Overview of Three Ages. Strain, 2025. 61(1): p. e12495.

8.         Ghannadi, P., S.S. Kourehli, and A. Nguyen, Experimental validation of an efficient strategy for FE model updating and damage identification in tubular structures. Nondestructive Testing and Evaluation, 2024: p. 1-40.

9.         Ghannadi, P., et al. Finite element model updating and damage identification using semi-rigidly connected frame element and optimization procedure: An experimental validation. in Structures. 2023. Elsevier.

10.       Entezami, A., H. Sarmadi, and B. Behkamal, Long-term health monitoring of concrete and steel bridges under large and missing data by unsupervised meta learning. Engineering Structures, 2023. 279: p. 115616.

11.       Rădulescu, V.M., et al., Structural Health Monitoring of Bridges under the Influence of Natural Environmental Factors and Geomatic Technologies: A Literature Review and Bibliometric Analysis. Buildings, 2024. 14(9): p. 2811.

12.       Figueiredo, E., et al., Impact of climate change on long-term damage detection for structural health monitoring of bridges. Structural Health Monitoring, 2024: p. 14759217231224254.

13.       Gu, J., M. Gul, and X. Wu, Damage detection under varying temperature using artificial neural networks. Structural Control and Health Monitoring, 2017. 24(11): p. e1998.

14.       Gillich, N., et al., Beam damage assessment using natural frequency shift and machine learning. Sensors, 2022. 22(3): p. 1118.

15.       Liu, C., et al., Deep transfer learning-based damage detection of composite structures by fusing monitoring data with physical mechanism. Engineering Applications of Artificial Intelligence, 2023. 123: p. 106245.

16.       Prognostics Center of Excellence Data Set Repository. Available from: https://www.nasa.gov/intelligent-systems-division/discovery-and-systems-health/pcoe/pcoe-data-set-repository/.

17.       A New Experimental Benchmark Problem for Vibration-Based Structural Health Monitoring (SHM). 2018; Available from: https://www.structuralvibration.com/benchmark/.

18.       Abdeljaber, O., et al., Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. Journal of sound and vibration, 2017. 388: p. 154-170.

19.       Sarmadi, H., et al., Ensemble learning‐based structural health monitoring by Mahalanobis distance metrics. Structural Control and Health Monitoring, 2021. 28(2): p. e2663.

20.       Deraemaeker, A. and K. Worden, A comparison of linear approaches to filter out environmental effects in structural health monitoring. Mechanical systems and signal processing, 2018. 105: p. 1-15.

21.       Sarmadi, H. and A. Karamodin, A novel anomaly detection method based on adaptive Mahalanobis-squared distance and one-class kNN rule for structural health monitoring under environmental effects. Mechanical systems and signal processing, 2020. 140: p. 106495.

22.       Shu, J., et al., A multi-task learning-based automatic blind identification procedure for operational modal analysis. Mechanical Systems and Signal Processing, 2023. 187: p. 109959.

23.       Entezami, A., H. Sarmadi, and B. Behkamal, A novel double-hybrid learning method for modal frequency-based damage assessment of bridge structures under different environmental variation patterns. Mechanical Systems and Signal Processing, 2023. 201: p. 110676.

24.       Farrar, C.R., et al., Dynamic characterization and damage detection in the I-40 bridge over the Rio Grande. 1994, Los Alamos National Lab.(LANL), Los Alamos, NM (United States).

25.       Figueiredo, E., et al., Structural health monitoring algorithm comparisons using standard data sets. 2009, Los Alamos National Laboratory (LANL), Los Alamos, NM (United States).

26.       Meruane, V. and W. Heylen, Structural damage assessment under varying temperature conditions. Structural Health Monitoring, 2012. 11(3): p. 345-357.