PSI - Issue 64

Tahreer M. Fayyad et al. / Procedia Structural Integrity 64 (2024) 708–715 Tahreer M. Fayyad / Structural Integrity Procedia 00 (2019) 000–000

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1. Introduction Structural health monitoring (SHM) is an expanding discipline within civil engineering that plays a crucial role in preserving and enhancing the durability and security of vital civil engineering structures. The deterioration of infrastructure can progressively impact its integrity until it becomes unsafe for use. For instance, the rusting of steel reinforcement bars within concrete tends to accelerate once it begins. Therefore, early detection in structural health is similar to early medical diagnosis, where prompt and precise identification of health issues is crucial for timely and specific interventions, leading to improved outcomes and rapid recovery. Similarly, in the context of civil engineering structures like bridges, early identification of damages can save lives, ensure safety, and facilitate strategic financial planning for repairs and maintenance. Over the past two decades, the world has witnessed over a hundred significant bridge failures (Zhang et al., 2022). In 2018, Italy's Morandi Bridge collapse resulted in 43 fatalities and substantial economic losses (Calvi et al., 2019). Thus, continuous monitoring and analysis of civil engineering structures through SHM can serve as an early alert system, preventing structures from reaching a critical failure point throughout their lifespan (Bao et al., 2019) (Lydon, Taylor, et al., 2019) (Li et al., 2006). Repairing defects close to the critical phase incurs huge costs, yet early prevention allows for proactive decision-making before structures suffer irreparable damage (Zonta et al., 2014). Such decisions also help in allocating limited funds to prioritize maintenance of the most crucial assets. Climate change introduces additional challenges, with more frequent and severe conditions posing increased risks to our infrastructure. Heatwaves, for example, can cause thermal expansion and a higher likelihood of material cracking, jeopardizing structural integrity (Zhou & Yi, 2013) (Hossain et al., 2020). Additionally, construction materials may degrade more rapidly due to flooding, elevating the risk of scour in bridges which could result in catastrophic failures like that of the Morandi Bridge (Calvi et al., 2019). The rising frequency of wildfires, often linked to heatwaves, directly endangers structures(Dennison et al., 2014). As global warming threats grow, early detection and continual monitoring become key to prolonging the lifespan and safety of essential infrastructures. SHM methods offer non-invasive approaches for assessing a structure's physical condition, using diverse sensors and measuring instruments to identify, pinpoint, and evaluate possible damage or wear over time. Methods like vibration analysis, acoustic emission inspection, ultrasonic examination, and thermal imaging are extensively applied in this area, enabling engineers to observe and study structural responses under varied circumstances. The information gathered through these methods can be utilized to evaluate the present state of a structure, predict future problems, and inform decisions regarding maintenance and repairs (Sony et al., 2019). One of the primary challenges in the field of SHM is that no single technology suffices for comprehensive monitoring, necessitating the integration of various technologies. The complexity and variability of structures mean that different damage mechanisms may not be effectively captured by a singular method. For instance, while vibration based techniques are adept at identifying changes in structural dynamics, they may not pinpoint the exact location of minor damages as effectively as vision-based techniques (Poorghasem & Bao, 2022). Consequently, merging vibration-based methods with vision-based technologies, such as digital image correlation, can offer a more holistic view of a structure's health. Moreover, the transition of theoretical models into real-world applications presents another significant barrier. These models often require adjustments to accurately reflect the diverse behaviours of structures under different stages of damage, which can vary greatly depending on materials, design, and environmental conditions. This underscores the need for adaptive and multidisciplinary approaches in SHM, capable of interpreting complex data from varied sources to accurately assess structural integrity. Additionally, challenges such as sensor placement optimization, data management, and the interpretation of vast amounts of data from different technologies into actionable insights further complicate the SHM landscape (Sarmadi et al., 2023) (Sony et al., 2019) (Ye et al., 2016). Addressing these challenges requires ongoing research, interdisciplinary collaboration, and the development of advanced algorithms and software that can seamlessly integrate and analyse data from multiple SHM technologies. Digital image correlation (DIC) is a vision-based method that captures structural deformations by comparing images taken before and after loading, enabling precise measurement of strains and displacements across the surface of a structure (Fayyad & Lees, 2014) (Lydon, Lydon, et al., 2019) (Lydon et al., 2022). Meanwhile, measuring frequency as a dynamic property involves the use of accelerometers in SHM to detect changes in a structure's natural frequencies, which can indicate the presence and progression of damage (Feng et al., 2019). In this paper, both DIC and frequency measurements are leveraged to particularly track damage in reinforced concrete beams. By integrating these two methodologies, it is aimed to monitor the initiation and development of flexural cracks, providing an understanding of