Research fostering innovation
The innovation we bring to infrastructure monitoring is built on a solid foundation of academic research, which we continue to pursue with rigour and curiosity. Through scientific publications and collaborations with leading universities, we develop ever more accurate and reliable algorithms. Explore the publications and research that underpin our solutions.
Wireless Accelerometer Architecture for Bridge SHM: From Sensor Design to System Deployment
Authors:
Francesco Morgan Bono, Alessio Polinelli, Luca Radicioni, Lorenzo Benedetti, Francesco Castelli-Dezza, Simone Cinquemani, Marco Belloli
Abstract:
This paper introduces a framework to perform operational modal analysis (OMA) for structural health monitoring (SHM) by presenting the development and validation of a low-power, solar-powered wireless sensor network (WSN) tailored for bridge structures. The system integrates accelerometers and temperature sensors for dynamic structural assessment, all interconnected through the energy-efficient message queuing telemetry transport (MQTT) messaging protocol. Moreover, it delves into the details of sensor selection, calibration, and the design considerations necessary to address the unique challenges associated with bridge structures [...]
Published Papers
An automated algorithm for experimental OMA: application on a Warren truss railway bridge with a permanent monitoring system
In the attempt to move towards time-efficient and cost-effective condi-tion-based monitoring of transport infrastructures...
Improving the effectiveness of anomaly detection in bridges through a deep learning method based on coherence of signals
In recent years, real-time monitoring of health conditions for massive structures, such as bridges and buildings, has grown in interest...
A new methodology to detect corrosion on steel truss railway bridges exploiting local strain gauges: Analytical, numerical, and experimental validation
Steel truss bridges suffer from ageing deterioration phenomena, among which corrosion is the most critical...
On the performance of data‑driven dynamic models for temperature compensation on bridge monitoring data
In Structural Health Monitoring, environmental and operational variables present a persistent challenge...
A Deep Learning Approach to Detect Failures in Bridges Based on the Coherence of Signals
Structural health monitoring of civil infrastructure, such as bridges and buildings, has become a trending topic in the last few years...
Bridge structural monitoring: the Lombardia regional guidelines
In 2018 a collaborative project between Politecnico di Milano (PoliMI) and Regione Lombardia (RL) was launched to join forces...
FAQ
Displaid in details
01-What role does research play in the development of Displaid solutions?
Research is the driving force behind Displaid’s innovation. Every sensor, algorithm and platform is designed and tested starting from advanced scientific studies and rigorous engineering methodologies. This academic foundation ensures that our solutions are reliable, accurate and scalable, transforming complex data into actionable information and enabling infrastructure managers to make evidence-based decisions.
02-Does Displaid collaborate with universities and research centres?
Yes. Displaid maintains active collaborations with leading universities and research centres at both national and international level, including Politecnico di Milano and MIT. These partnerships allow us to continuously refine our algorithms, test new technologies and contribute to scientific publications, strengthening the technical credibility and reliability of our solutions.
03- How does research translate into tangible benefits for infrastructure managers?
Research does not remain theoretical: results are directly applied to real-world infrastructure monitoring. The developed algorithms enable the detection of specific degradation mechanisms, such as bearing deterioration or corrosion, and generate clear indicators to support inspection planning and maintenance actions. In practical terms, infrastructure managers gain predictive tools and decision-support systems that improve safety, reduce costs and optimise the lifecycle management of assets.
