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Fatigue cracks on steel components may have strong consequences on the structure's serviceability and strength. Their detection and localization is a difficult task. Existing technologies enabling structural health monitoring have a complex link signal-to-damage or have economic barriers impeding large-scale deployment. A solution is to develop sensing methods that are inexpensive, scalable, with signals that can directly relate to damage. The authors have recently proposed a smart sensing skin for structural health monitoring applications to mesosystems. The sensor is a thin film soft elastomeric capacitor (SEC) that transduces strain into a measurable change in capacitance. Arranged in a network configuration, the SEC would have the capacity to detect and localize damage by detecting local deformation over a global surface, analogous to biological skin. In this paper, the performance of the SEC at detecting and localizing fatigue cracks in steel structures is investigated. Fatigue cracks are induced in steel specimens equipped with SECs, and data measured continuously. Test results show that the fatigue crack can be detected at an early stage. The smallest detectable crack length and width are 27.2 and 0.254 mm, respectively, and the average detectable crack length and width are 29.8 and 0.432 mm, respectively. Results also show that, when used in a network configuration, only the sensor located over the formed fatigue crack detect the damage, thus validating the capacity of the SEC at damage localization. Fatigue cracks on steel components may have strong consequences on the structure's serviceability and strength. Their detection and localization is a dicult task. Existing technologies enabling structural health monitoring have a complex link signalto-damage or have economic barriers impeding large-scale deployment. A solution is to develop sensing methods that are inexpensive, scalable, with signals that can directly relate to damage. The authors have recently proposed a smart sensing skin for structural health monitoring applications to mesosystems. The sensor is a thin lm soft elastomeric capacitor (SEC) that transduces strain into a measurable change in capacitance. Arranged in a network conguration, the SEC would have the capacity to detect and localize damage by detecting local deformation over a global surface, analogous to biological skin. In this paper, the performance of the SEC at detecting and localizing fatigue cracks in steel structures is investigated. Fatigue cracks are induced in steel specimens equipped with SECs, and data measured continuously. Test results show that the fatigue crack can be detected at an early stage. The smallest detectable crack length and width are 27.2 mm and 0.254 mm, respectively, and the average detectable crack length and width are 29.8 mm and 0.432 mm, respectively. Results also show that, when used in a network conguration, only the sensor located over the formed fatigue crack detect the damage, thus validating the capacity of the SEC at damage localizat...

Abstract. The alarmingly degrading state of transportation infrastructures combined with their key societal and economic importance calls for automatic condition assessment methods to facilitate smart management of maintenance and repairs. With the advent of ubiquitous sensing and communication capabilities, scalable data-driven approaches is of great interest, as it can utilize large volume of streaming data without requiring detailed physical models that can be inaccurate and computationally expensive to run. Properly designed, a data-driven methodology could enable fast and automatic evaluation of infrastructures, discovery of causal dependencies among various sub-system dynamic responses, and decision making with uncertainties and lack of labeled data. In this work, a spatiotemporal pattern network (STPN) strategy built on symbolic dynamic filtering (SDF) is proposed to explore spatiotemporal behaviors in a bridge network. Data from strain gauges installed on two bridges are generated using finite element simulation for three types of sensor networks from a density perspective (dense, nominal, sparse). Causal relationships among spatially distributed strain data streams are extracted and analyzed for vehicle identification and detection, and for localization of structural degradation in bridges. Multiple case studies show significant capabilities of the proposed approach in: (i) capturing spatiotemporal features to discover causality between bridges (geographically close), (ii) robustness to noise in data for feature extraction, (iii) detecting and localizing damage via comparison of bridge responses to similar vehicle loads, and (iv) implementing real-time health monitoring and decision making work flow for bridge networks. Also, the results demonstrate increased sensitivity in detecting damages and higher reliability in quantifying the damage level with increase in sensor network density.

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