Sanduni Jayasinghe - Smartcrete

Biography

Sanduni Jayasinghe is a civil engineer specialising in structural engineering. She obtained her bachelor degree in 2021 in engineering with a First Class Honours, specialised in civil engineering from University of Peradeniya, Sri Lanka and commenced her PhD the next year at RMIT University. Her research is on developing digital twins for real-time structural integrity assessment of civil infrastructure where she integrates artificial intelligence (AI) techniques with finite element modelling (FEM) to address the limitations of running real-time FE models of complex structures and thereby create intelligent structural health monitoring systems.

Industry Partners

The project is led by RMIT University and supported by local governments, several universities and industry partners including the Victorian Department of Transport and Planning, Transport of New South Wales, Melbourne Water, Australia Curtin University, Western Sydney University, University of Technology Sydney, Beta International Associates, Upward Technology, Ash Development Association, Lastek, Macdonald Lucas and Bentley.

Research organisation

RMIT University

Academic mentor

Dr Mojtaba Mahmoodian

Publications

In the publications list, S.C. Jayasinghe refers to my name Sanduni Chathuprabha Jayasinghe.

S.C. Jayasinghe, M. Mahmoodian, A. Alavi, A. Sidiq, Z. Sun, F. Shahrivar, J. Thangarajah, S. Setunge, (2025) Application of Machine Learning for real-time Structural Integrity Assessment of Bridges, CivilENG, https://doi.org/10.3390/civileng6010002.
Z. Sun, S.C. Jayasinghe, F. Shahrivar, A. Sidiq, M. Mahmoodian, S. Setunge, 2024. Approach Towards the Development of Digital Twin for Structural Health Monitoring of Civil Infrastructure: A Comprehensive Review, Sensors, https://doi.org/10.3390/s25010059.
A. Alavi, S.C. Jayasinghe, Leveraging SPD Matrices on Riemannian Manifolds in Quantum Classical Hybrid Models for Structural Health Monitoring (arXiv preprint arXiv:2406.04055).
S.C. Jayasinghe, M. Mahmoodian, A. Sidiq, T.M. Nanayakkara, A. Alavi, Sam Mazaheri, F. Shahrivar, Z. Sun, S. Setunge, (2024), Innovative Artificial Neural Network for a Digital Twin in Real-time Structural Health Monitoring: A Port Structure Case Study, Ocean Engineering. https://doi.org/10.1016/j.oceaneng.2024.119187.
S.C. Jayasinghe, Z. Sun, A. Sidiq, M. Mahmoodian, F. Shahrivar, S. Setunge, (2024), Digital Twins for real-time structural integrity assessment of Bridges, Structural control and health monitoring. https://doi.org/10.1201/9781003483755-288.
Z. Sun, M.I. Manuel, M. Mahmoodian, A. Sidiq, R.W.K. Chan, S.C. Jayasinghe, F. Shahrivar, S. Setunge, (2024). Optimal Sensor Placement Applications for Bridge Structural Health Monitoring Using the Effective Independence Method IABMAS 202. https://doi.org/10.1201/9781003483755-289.
F. Shahrivar, A. Sidiq, M. Mahmoodian, S.C. Jayasinghe, Z. Sun, S. Setunge, (2025), AI- based Bridge Infrastructure Maintenance Management: A Comprehensive Review, Artificial Intelligence Review, https://doi.org/10.1007/s10462-025-11144-7.
S.C. Jayasinghe, S.A. Abeysinghe, H.D. Yapa, Retrofitting of Impaired Dapped-end beams: A Review, Canadian Journal of Civil Engineering, 00: 1–14 (2023) | dx.doi.org/10.1139/cjce-2021-0627.
S.C. Jayasinghe, N.S. Hansika, H.D. Yapa, Numerical Investigation on Retrofitting of RC Dapped-end Beams, Annual Sessions 2021 – Society of Structural Engineers, Sri Lanka (SSESL), 30th August 2021(Awarded as the best paper presented).
A.P Madusanka, S.C. Jayasinghe, Deep Embedded Shear Retrofitting of RC Beams: Parametric Study, MODULUS – March 2022, Vol. 32, No. 1, Pages 20 to 32.

Project description

Digital twin of reinforced concrete infrastructure for intelligent asset management

This study focuses on developing real-time digital twins for bridges, enabling remote monitoring of excessive stresses and strains to support preventive maintenance and avoid failures. The approach involves instrumenting bridges with sensors (strain, deflection, vibration) to capture real-time structural behavior. A finite element model (FEM) is integrated into the digital twin for comprehensive structural health assessment. The proposed framework enhances real-time structural integrity assessment, providing a robust tool for bridge monitoring and maintenance planning.

What led you to undertake an industry-led research project?

By undertaking an industry-led research project, I can focus on practical applications rather than just theory. This ensures that my research produces beneficial outputs that can be implemented in the real world, directly addressing industry needs and providing effective solutions to real-world problems.

What have been the highlights of your research?

Development of a Real-Time Digital Twin Model – Created an efficient digital twin framework for bridges, integrating finite element modelling (FEM).
Optimisation of Sensor Placement for implementing real-time structural health monitoring systems.
Addressed practical challenges in structural health monitoring, ensuring the research contributes to predictive maintenance strategies and enhances bridge safety.

Once you have completed your PhD, what’s next?

After completing my PhD, I aim to continue working at the intersection of industry and research. Mostly, I would like to work in the industry and apply my knowledge to the industry. I see myself contributing to structural health monitoring and digital twin development, either through industry collaborations or academic research. I want to apply my expertise to real-world infrastructure challenges, improving bridge maintenance, safety, and predictive monitoring.

How will your research benefit Australia’s built environment and concrete ecosystem?

My research enhances Australia’s built environment by improving bridge monitoring, maintenance, and safety. The digital twin framework enables real-time structural assessments, reducing maintenance costs and preventing failures. By integrating machine learning techniques, it supports sustainable infrastructure management, extending the lifespan of concrete structures and ensuring safer, more resilient assets.