The service life of reinforced and prestressed concrete infrastructures, such as bridges or harbours, is highly dependent on the corrosion resistance of steel. Indeed, in marine environments, carbonation and chloride penetration from the external environment are known to initiate corrosion processes of steel leading subsequently to the weakening of the infrastructure, and eventually to its collapse. A striking example is the collapse of the Morandi bridge at Genova. Traditional corrosion detection methods, such as visual inspections, are time-consuming and costly, and does not allow an early detection of corrosion since steel material is, most of the time, inaccessible either placed in concrete for reinforced concrete or in High-Density Polyethylene (HDPE) ducts for prestressed structures.

Recently, Radio Frequency Identification (RFID) technology was proposed as a new method that can be used for corrosion detection. Passive RFID corrosion sensors are particularly attractive because of their small dimensions, their cheapness and possibility to work wirelessly. Furthermore, the absence of batteries and cables facilitates their integration in concrete during construction or maintenance/repair operations and ensures a long life-service. In particular, in an European project dedicated to the cultural heritage sector, we proposed an UHF-RFID sensor based on the coupling between an RFID antenna and an additional sensitive metallic layer exposed to atmospheric corrosion [1]. The principle is simple: during corrosion, the thickness of the thin metal sheet decreases leading to an increase of its electrical resistance and hence to a variation of properties of the coupled antenna. Monitoring the variation of power received by the RFID reader, namely the RSSI factor, provides thus valuable information on the residual thickness of the sensitive metallic layer. Recently, this method was proposed to improved maintenance operations of prestressed bridges [2] and harbours [3].

At the EWSHM conference, recent developments of such technology will be presented and discussed. In particular, a focus will be made on the durability of these embedded battery-less sensors in infrastructures. This is obviously a crucial question since these sensors are expected to monitor steel corrosion over several decades.

References

1. I. El Masri, B. Lescop, P. Talbot, G. Nguyen Vien, J. Becker, D. Thierry, and S. Rioual, “Development of a RFID sensitive tag dedicated to the monitoring of the environmental corrosiveness for indoor applications,”Sensors and Actuators B: Chemical, 322, 128602, 2020.

2. L. Gaillet, C. Sarr, Y. Falaise, S. Chataigner, P. Talbot, B. Lescop, and S. Rioual, “RFID sensor development for corrosion monitoring in bridges cables,” Nondestructive Testing and Evaluation, 1–19, 2025.

3. S. Rioual, B. Lescop, P. Talbot, C. Vo and T. L. Ngoc, "Improving Harbour Infrastructure Inspections by Autonomous Corrosion Sensors Interrogated by Drones," OCEANS 2025 Brest, BREST, France, 2025.

Efficient gateway placement is a critical challenge in large-scale Structural Health Monitoring (SHM) deployments on bridges, where battery-powered wireless sensor nodes must maintain long-term operation while ensuring reliable long-range communication. Traditional placement strategies often rely on geometric considerations such as Euclidean distance; however, bridges present complex propagation environments due to steel reinforcement, thick concrete, traffic-induced vibrations, and non-line-of-sight conditions. These factors reduce the reliability of LoRa/LoRaWAN links and motivate optimization strategies that account for both node distribution and radio-channel behaviour.
This study presents a comparative investigation of two gateway-optimization strategies based on Fuzzy C-Means (FCM) clustering. For each strategy, we follow a consistent two-step workflow: first, the ideal number of gateway clusters is determined using FCM in combination with clustering-validity indices; second, with this number fixed, FCM is re-run to compute the optimal gateway positions.
The two strategies differ only in the distance metric employed: (i) a classical Euclidean metric that emphasizes geometric density, and (ii) a propagation-aware metric based on log-distance path loss models, which estimates effective communication range between nodes and gateways. The propagation-aware approach requires repeated path-loss evaluations and distance-weighted membership updates, which is expected to result in substantially higher computational cost compared to geometric FCM, particularly for dense deployments or long-span bridges.
Both optimization pipelines will be validated using NS-3, a discrete-event network simulator that enables detailed modeling of LoRaWAN protocols, radio propagation, and interference effects. NS-3 allows realistic evaluation of bridge-specific deployment scenarios, capturing the impact of multi-path, fading, and shadowing on Uplink Delivery Ratio (ULDR), coverage, and energy consumption. By using NS-3, the methodology bridges the gap between theoretical placement strategies and practical deployment constraints, providing results that reflect realistic communication conditions.
This work aims to quantify the trade-offs between computational complexity and radio-aware placement gains, offering a reproducible methodology for gateway planning in SHM networks. Beyond evaluating the two FCM-based strategies, the study lays the groundwork for future extensions including multi-hop routing and mobile sink integration. These extensions will further enhance network reliability and energy efficiency, providing practical guidance for deploying robust, long-term SHM networks across geographically distributed bridges.

Due to the planned extension of interim storage periods for high‑level radioactive waste in Germany, the structural durability of existing reinforced‑concrete storage buildings is gaining increasing importance. Reliable long‑term in‑situ data on climatic and corrosion‑related factors are essential for robust service‑life assessment. To address this need, the ZuMoBau‑ZL project developed a battery‑free NFC-based sensor system capable of recording temperature, relative humidity, and air pressure within the concrete cover.
The NFC transponders are embedded between the concrete surface and the reinforcement, enabling direct monitoring of environmental conditions inside reinforced concrete. The system is designed to support service‑life prediction concepts, as humidity is a primary indicator for corrosion risk, while temperature and air pressure influence moisture transport and pore humidity equilibria that affect chloride ingress and depassivation timing.
The sensors were installed in the full‑scale reinforced‑concrete Concerto test structure, which is used for long‑term validation of monitoring concepts under realistic environmental exposure. Eight of nine embedded transponders were successfully activated and read after installation, delivering stable and plausible measurements.
These results demonstrate the feasibility of passive NFC sensors for continuous, non‑destructive in‑situ monitoring inside concrete. By providing climatic data directly relevant to deterioration processes, the system supports adaptive service‑life prediction and can contribute to the long‑term durability assessment of safety‑critical infrastructure such as interim nuclear storage facilities.

Composite materials are increasingly used in industries such as aerospace, automotive, and civil engineering due to their exceptional strength-to-weight ratio and durability. However, continuous monitoring of these materials is crucial for detecting early signs of damage and preventing catastrophic failures. Traditional strain sensing techniques often present challenges, including bulkiness, rigidity, and difficulties in integrating with composite structures. To overcome these limitations, flexible and wireless strain sensors have emerged as promising tools for real-time structural health monitoring (SHM).

In this work, we present flexible e-tattoo sensors fabricated by direct ink writing (DIW) on commercially available tattoo paper and transferred onto composite substrates. Two applications were demonstrated. First, an e-tattoo strain gauge integrated with a wireless readout circuit was applied to a glass fiber reinforced polymer (GFRP) specimen, achieving a high gauge factor of 20.7 under tensile testing. To enhance reliability, we introduced polynomial modeling of strain responses and implemented a temperature compensation strategy. Second, a chipless LC wireless sensor was embedded on GFRP-bonded joints to track adhesive curing. Resonance frequency shifts were correlated with the degree of cure, independently verified by differential scanning calorimetry (DSC) and Fourier transform infrared spectroscopy (FTIR). These ultra-thin, lightweight, and minimally invasive sensors enable continuous, real-time monitoring without altering the mechanical performance of the host structure, offering a practical and scalable pathway for next-generation structural health monitoring (SHM) applications.

The safe and reliable operation of pipeline infrastructure is essential for modern energy systems. However, existing non-destructive testing (NDT) methods typically detect failures only after the defects have already occurred. This challenge is especially significant for non-metallic composite pipes, whose failure mechanisms and field behaviour can be difficult to assess using traditional sensing technologies. To address this challenge, a new wireless structural health monitoring (SHM) technology based on patented capacitive strain sensors designed specifically for continuous monitoring of non-metallic pipeline assets has been presented in this work.

This work presents the design, development, and validation of a cost-effective, wireless capacitive strain sensing system achieving high measurement sensitivity and wide operating range while remaining compatible with field deployment constraints. The sensing package comprises a flexible capacitive strain gauge with a sensitivity of ~ 0.5 microstrain, which outperforms commercial resistive strain gauges (~1 microstrain). Furthermore, the sensor enables a significantly broader strain measurement window (>10,000 microstrain) compared with typical commercial systems (<4,000 microstrain). The on-demand wireless communications system provides better security and energy consumption. Unlike periodic NDT inspections, this approach supports real-time assessment of pipeline health, improving operational decision-making and enabling predictive maintenance strategies. These combined advantages make the proposed system suited for next-generation wireless SHM systems, where low power requirements, robust performance, and flexible deployment are critical.

The methodology includes (1) development and fabrication of capacitive sensors, (2) integration with wireless electronics suitable for harsh environments, (3) laboratory testing to benchmark sensitivity, robustness, and measurement range relative to commercial resistive gauge systems, and (4) evaluation of system performance on representative non-metallic pipe segments under controlled loading scenarios. The results demonstrate reliable and repeatable strain measurements across the full targeted range, validating the technology’s suitability for SHM of composite infrastructure.

In conclusion, the newly developed wireless capacitive sensing system offers a promising pathway toward continuous monitoring of non-metallic pipelines. Its combination of enhanced sensitivity, wider strain range, wireless connectivity, and low system cost positions it as an effective alternative to conventional monitoring technologies. Future work will focus on field-scale pilot deployments, integration with digital twin frameworks, and multi-sensor fusion for comprehensive pipeline integrity assessment.

This study presents the design and implementation of a multi-channel ultra-low-power IoT-based strain monitoring system with edge computing and precise time synchronization. The system integrates circuit design, microcontroller technology, digital communication, and IoT frameworks to achieve efficient, real-time structural monitoring. A dedicated circuit has been developed for high-precision dynamic strain measurement, enabling ultra-low-noise excitation of resistive strain gauges and accurate acquisition of minute bridge differential signals for amplification and digitization. At the intelligent node, customized firmware with edge-computing algorithms enables real-time analysis of large-scale dynamic strain data and conversion into meaningful monitoring information. Through the integration of firmware and digital communication protocols, multi-channel synchronization within the millisecond range has been achieved, while maintaining total system power consumption at the milliwatt level. Three low-noise power modules have been developed to ensure stable and flexible power supply, mitigating the effects of poor power quality on measurement accuracy. With an ultra-low-power design, the system can operate continuously using a solar module smaller than one cubic centimeter. Furthermore, a cross-platform data exchange framework and an IoT-based strain monitoring server have been developed, allowing seamless integration with existing large-scale structural health monitoring systems. As shown in Fig. 1, on-site measurement results of the proposed eight-channel strain monitoring system installed on a bridge in Taiwan for prestressed box girder strain monitoring. Strain gauges were arranged along a selected cross-section of the girder. The system outputs, every minute, the strain values of all channels and their corresponding timestamps at the instant when Channel 1 experiences its maximum strain within a 60-second interval sampled at 100 Hz. When heavy vehicles pass, the synchronized strain responses (CH1–CH8) align with the expected strain distribution of a prestressed box girder. Based on the sensor placement, the neutral axis position can be estimated. The algorithm thus provides meaningful structural health information every minute, and long-term statistical analysis of neutral axis shifts enables detection of prestress loss or structural degradation. The field experiments conducted verified the system’s feasibility and demonstrated its capability for real-world bridge health monitoring. The proposed system provides a practical and energy-efficient solution for on-site structural health monitoring applications.