Concrete bridges are essential connections for transportation and economic activities in different regions worldwide. When these structures are located in coastal areas, they encounter further challenges from the marine environment, including high humidity, exposure to saltwater, and strong winds that impact the structure's lifetime.
To guarantee the safety of travellers, these structures need to be periodically inspected to evaluate the severity of damages and schedule maintenance. Manual inspection of the health of bridges comes with a number of challenges, including:
- Environmental Factors: Coastal areas are exposed to environmental elements such as high humidity, salt spray, and varying temperatures. These conditions can damage equipment and distort sensor readings, impacting inspection data quality. Moreover, winds, foggy weather, and turbulent waves make visual inspection more difficult, especially in hard-to-reach areas.
- Data Reliability: The reliability of health inspections heavily depends on the subjective evaluation and experience of the inspectors. External factors like lighting variations, surface reflections from water bodies, and shadows may affect the interpretation of the structure's health conditions. Detecting cracks or subtle signs of deterioration poses a challenge under such circumstances.
- Challenges in Accessing Remote Areas: Coastal bridges often have sections due to their location above water or rugged landscapes. While drones and other remote sensing technologies offer support to overcome this issue, adverse weather conditions or obstacles like vegetation can hinder their effectiveness in capturing data.
- Identification of Corrosion: The coastal environment accelerates the corrosion process of steel reinforcements within structures. Detecting corrosion using ground-penetrating radar (GPR) or infrared thermography can pose certain challenges. These methods might face issues with how deep they can penetrate, the clarity of the results, or signal disturbances caused by moisture and salt build-up.
- Data Integration and Interpretation: When integrating and understanding data from bridge health inspections, much information is gathered from sources such as detailed images, LiDAR scans, and sensor data. Making sense of this data to derive insights requires a lot of time and effort.
- Cost and Resource Allocation: Adopting inspection technologies involves initial equipment, software and training investment. Coastal regions with logistical challenges may incur deployment and maintenance costs for these more advanced systems. Moreover, constrained budgets could hinder the use and integration of these technologies. This is coupled with the requirements to close the bridges during these inspections, which impact transportation and the economy of the area.
- Skill Gaps and Workforce Training: Inspection methods of concrete structures require a workforce that is highly skilled in identifying and evaluating structure damage while being knowledgeable about the technologies that can be used to support this activity. Since there might be lack of trained workers in certain areas, investing in training programs and skill-building initiatives is essential to filling this gap.
- Long-Term Data Management: structural health inspections produce a lot of data over time that needs to be stored, organised, and analysed. Coastal conditions can change quickly, so regular inspections and updates are necessary. Making sure this data remains reliable, accessible and valuable for analysing trends and planning maintenance is a challenge. Tackling these issues is crucial to making the most of health inspections and ensuring the safety and durability of concrete bridges in coastal settings.
Early damage detection is paramount in coastal areas, where corrosion and structural deterioration due to saltwater are significant concerns. Computer vision systems, with their ability to automatically identify cracks, spalling, and other signs of wear, play a crucial role in this proactive approach.
By analysing images taken over time, these systems can pinpoint changes that may indicate potential structural issues, allowing maintenance teams to focus on the highlighted areas, analyse the changes, and address repairs promptly.
Moreover, computer vision is instrumental in monitoring bridges, providing a safe and efficient way to inspect hard-to-reach areas. Drones with AI-powered cameras can fly beneath and around bridges, capturing images without risking inspectors' safety, a precious feature in regions with harsh weather conditions. Furthermore, climbing robots equipped with cameras can capture all parts of the bridge without the need to close the bridge for traffic.
Computer vision technology significantly enhances bridge maintenance by analysing visual data, historical records, and environmental factors. One of its key contributions is predicting deterioration areas, which prolongs the bridge's lifespan and optimises maintenance costs over time.
Despite the challenges posed by coastal bridge management, ongoing research aims to overcome these obstacles with image processing and machine learning algorithms. AI-driven surveillance ensures bridges' safety, longevity, and functionality in demanding settings, showcasing the impact of innovation in protecting our infrastructure.
The University of Malta together with Zhejiang University are working towards novel solutions to automate inspections of concrete bridges near coastal areas to minimise inconvenience and costs while reducing inspection times and improving efficiency.
The project DiHICS received funding from the Xjenza Malta (formerly Malta Council for Science and Technology (MCST) and the Ministry for Science and Technology of the People’s Republic of China (MOST), through the SINO-MALTA Fund 2023 Call (Science and Technology Cooperation).
