Structural information integration for predicting damages in bridges represents a pioneering approach in the realm of civil engineering, leveraging advanced technologies and multidisciplinary methodologies to enhance the safety, resilience, and longevity of critical infrastructure. Bridges play a vital role in facilitating transportation networks, connecting communities, and supporting economic activity. However, they are susceptible to various forms of deterioration, including corrosion, fatigue, and structural degradation, which can compromise their integrity and pose significant safety risks. By integrating diverse sources of structural information, including sensor data, structural health monitoring systems, historical performance data, and predictive modelling techniques, engineers and researchers can develop more accurate, proactive, and cost-effective strategies for assessing bridge condition, predicting potential damages, and prioritizing maintenance and rehabilitation efforts. At the heart of structural information integration for predicting damages in bridges lies the concept of Structural Health Monitoring (SHM), which involves the continuous monitoring and assessment of structural integrity using sensors, instrumentation, and data analytics techniques. SHM systems collect real-time data on various structural parameters, such as strains, vibrations, accelerations, and environmental conditions, providing insights into the behaviour and performance of bridges under different loading conditions and environmental factors. By analyzing this wealth of data using advanced signal processing, machine learning, and statistical techniques, engineers can detect anomalies, identify potential defects, and predict future performance degradation, enabling proactive maintenance and targeted interventions to prevent catastrophic failures.
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Journal of Civil and Environmental Engineering received 1798 citations as per Google Scholar report
