Project Details
Description
The field of structural health monitoring (SHM) in bridges has undergone a profound transformation in recent years, characterized by the convergence of multiple disruptive technologies. The integration of low-cost sensors, IoT networks, artificial intelligence (AI), and edge computing has democratized access to sophisticated SHM systems, enabling wider and more economically viable deployments.
The implemented systems demonstrate the technical feasibility and operational benefits of continuous SHM, ranging from fully instrumented bridges like the Dongbao River Xin'an Bridge to innovative IoT-based drive-by solutions. Sensor technologies have diversified, with MEMS accelerometers, optical fiber, and computer vision emerging as complementary pillars for multi-modal structural data capture.
Artificial intelligence, particularly deep learning, has revolutionized SHM data analysis, allowing for automated crack detection with accuracies exceeding 90% and damage evolution forecasting using LSTM models. The adoption of edge computing architectures has addressed critical challenges related to bandwidth and energy consumption, reducing the volume of data transmitted by up to 95% while maintaining real-time alerting capability.
However, significant challenges persist, related to implementation costs, managing large volumes of data, long-term robustness in harsh environments, and the gap between data and actionable decisions. The lack of universal standardization and the need for validation under real-world field conditions continue to be obstacles to mass adoption.
Future trends point toward deeper integration with digital twins for holistic lifecycle management, expansion of low-cost and crowdsourcing solutions for massive coverage, and the maturation of explainable AI (XAI) to increase confidence in automated diagnostics. The development of international standards and regulatory frameworks will be crucial to facilitate the transition of SHM systems from research into generalized operational practice.
Ultimately, SHM systems represent a fundamental tool for the sustainable and efficient management of bridge infrastructure in the 21st century, enabling the transition from reactive maintenance to predictive, condition-based strategies that optimize resources, extend service life, and improve public safety.
This project therefore aims to take all the experience generated by the eBridge research group, along with current and future trends, to propose a structural health monitoring system for bridges in Costa Rica.
The implemented systems demonstrate the technical feasibility and operational benefits of continuous SHM, ranging from fully instrumented bridges like the Dongbao River Xin'an Bridge to innovative IoT-based drive-by solutions. Sensor technologies have diversified, with MEMS accelerometers, optical fiber, and computer vision emerging as complementary pillars for multi-modal structural data capture.
Artificial intelligence, particularly deep learning, has revolutionized SHM data analysis, allowing for automated crack detection with accuracies exceeding 90% and damage evolution forecasting using LSTM models. The adoption of edge computing architectures has addressed critical challenges related to bandwidth and energy consumption, reducing the volume of data transmitted by up to 95% while maintaining real-time alerting capability.
However, significant challenges persist, related to implementation costs, managing large volumes of data, long-term robustness in harsh environments, and the gap between data and actionable decisions. The lack of universal standardization and the need for validation under real-world field conditions continue to be obstacles to mass adoption.
Future trends point toward deeper integration with digital twins for holistic lifecycle management, expansion of low-cost and crowdsourcing solutions for massive coverage, and the maturation of explainable AI (XAI) to increase confidence in automated diagnostics. The development of international standards and regulatory frameworks will be crucial to facilitate the transition of SHM systems from research into generalized operational practice.
Ultimately, SHM systems represent a fundamental tool for the sustainable and efficient management of bridge infrastructure in the 21st century, enabling the transition from reactive maintenance to predictive, condition-based strategies that optimize resources, extend service life, and improve public safety.
This project therefore aims to take all the experience generated by the eBridge research group, along with current and future trends, to propose a structural health monitoring system for bridges in Costa Rica.
General Objective
Implement a Structural Health Monitoring (SHM) system based on wireless sensors, artificial intelligence algorithms, and vibration analysis to detect, locate, and quantify damage in structures in order to optimize maintenance decisions and extend the safe lifespan of the infrastructure.
Research Lines
Road infrastructure
| Short title | eBridge 4.0 |
|---|---|
| Acronym | eBridge 4.0 |
| Status | Active |
| Effective start/end date | 1/01/26 → 31/12/28 |
Keywords
- Structural Health Monitoring
- structural behaivor
- monitoring
- IA structural
- bridges
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