Research Projects

The project aims to develop embedded fiber-optic-based smart-skin sensors for long-term hybrid response monitoring of structures. It will involve designing and establishing a scaled laboratory benchmark structure with developed sensor systems for testing under various static and dynamic load scenarios. Novel artificial intelligence algorithms and statistical data processing tools will be implemented for real-time damage assessment for predicting structural disasters. The efficacy and sensitivity of the developed fiber optic sensing system and AI tools in assessing and monitoring the health of critical infrastructure for disaster mitigation will be evaluated. The project will begin with a detailed literature review on the latest sensor technologies, data fusion techniques, conventional damage detection techniques, artificial intelligence, and its application in structural engineering. Suitable fiber optic sensors will be identified for long-term monitoring of structures, and AI algorithms will be developed based on unsupervised learning, supervised learning, and semi-supervised learning. Novelty detection-based algorithms will be required to indicate if the data pertains to a healthy state or damaged state of the structure. Initial studies will be conducted using numerical simulation, creating Finite element models for various healthy and damage scenarios. Lab prototypes will be developed for generating data under various loading and damage scenarios. The project will result in a technology for embeddable smart fiber optic sensors for long-term multi-response monitoring of bridges, novel AI-enabled tools for real-time damage assessment, and a first-of-its-kind system to assess the health of bridges under ambient loading conditions.