The field of research focuses on Structural Health Monitoring (SHM), a critical area within civil engineering that aims to ensure the safety, integrity, and longevity of bridges. SHM involves the continuous monitoring of bridges using advanced sensor technologies, data analytics, and predictive modelling. This research seeks to enhance our understanding of how bridges respond to various environmental and load conditions, detect early signs of deterioration, and develop strategies for proactive maintenance and repairs. By advancing the capabilities of SHM through integration of new sensor technologies, state-of-the-art data analysis using data fusion and artificial intelligence/machine learning (AI/ML), we aim to revolutionize the way bridges are monitored and maintained, ultimately improving their safety, efficiency, and resilience.

The research student will actively participate in the laboratory testing and subsequent data analysis within this research project. This involves both the design and set-up of the laboratory SHM system, as well as the execution of experiments. In addition, the student will make contributions to the data analysis by employing cutting-edge data analysis techniques and AI/ML methods. The student will gain knowledge and experience in various areas including experimental testing, AI, data science, data analysis, coding, and SHM sensor technology.


Civil and Environmental Engineering

Research Area

Structural health monitoring | Sensor development and integration | Data analytics| Machine learning | Predictive maintenance and prognostics | Communication and data transmission | Decision support tools and visualization | Field testing and validation | Inn

Our research environment is a dynamic and collaborative setting that encourages innovation and interdisciplinary collaboration. We have state-of-the-art laboratories equipped with cutting-edge sensor technologies, data analysis tools, and simulation software. Our team consists of experienced researchers in civil engineering, data science and experts in sensor technology.

The overall research project aims to achieve several key outcomes:

1. Innovative Sensor Technologies: The project will develop and optimize advanced sensor technologies capable of accurately measuring structural parameters such as strain, vibration, temperature, and corrosion. These sensors will be designed for durability, longevity, and ease of installation.

2. Data Analytics Framework: A robust data analytics framework will be established to process and analyse the large volumes of data generated by the sensors.

3. Predictive Maintenance Models: By integrating sensor data and analytics, the project will develop predictive maintenance models.

4. Communication Infrastructure: The research will lead to the development of efficient communication systems that ensure seamless data transmission from sensors to centralized monitoring centres. This will enable timely decision-making based on real-time data.

5. Decision Support Tools: The project will create decision support tools that interpret analysed data and provide actionable insights to engineers and decision-makers. These tools will guide maintenance planning and bridge management strategies.

6. Improved Bridge Safety and Lifespan Extension: Through the implementation of advanced SHM techniques, the project aims to enhance the safety of bridges by detecting and addressing structural issues early. The extension of bridge lifespans will lead to significant cost savings and reduced environmental impact.



opens in a new window

1. Mustapha, S., et al. "Estimation of crowd flow and load on pedestrian bridges using machine learning with sensor fusion." Automation in Construction 112 (2020): 103092.

2. Hassani, Sahar, and Ulrike Dackermann. "A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring." Sensors 23.4 (2023): 2204.


These references provide a comprehensive overview of the research landscape in structural health monitoring for bridges, sensor technologies, data analytics, and related guidelines.