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The ability to track the structural condition of existing structures is one of engineers, governments, and estate managers’ main con-cerns. In bridge maintenance programs, for example, visual in-spection predominates nowadays as the primary source of infor-mation. Nonetheless, visual inspections alone are insufficient to satisfy the current needs for structural safety assessment. The in-creasing demand for civil infrastructures, the aging of existing assets, and the strengthening of safety and liability laws have led to the inclusion of structural health monitoring (SHM) techniques into the structural management process. With the latest develop-ments in the sensors field and computational power, real-scale SHM deployment has become logistically and economically feasi-ble. However, it is still challenging to perform a quantitative evalua-tion of the structural condition based on measured data. Although the current approaches of SHM systems using traditional single-point sensors - such as electric strain sensors, accelerometers, and GPS-based sensors - have appropriate measurement precision for SHM purposes, they present challenges when deployed in real-scale applications, given the limited number of possible points to assess the structural behavior and the harsh environmental condi-tions during operation. When it comes to prestressed and rein-forced concrete structures, structural monitoring and damage identification present further challenges. They are affected by vari-ous chemical, physical and mechanical degradation processes and have a heterogeneous composition and non-linear behavior. On the other hand, fiber optic (FO) technology can provide integrated sensing in extensive measurement lengths with high sensitivity, durability, and stability, making them ideal for SHM of concrete structures. From this perspective, extensive research on structural health monitoring has been developed in the last decades. How-ever, the transfer rate from laboratory experiments to real-case applications is still unsatisfactory. This research addressed the main limitations that slow the deployment and the acceptance of real-size structural health monitoring systems in bridge mainte-nance programs. It proposed a long-term SHM concept to moni-tor prestressed concrete bridges, enabling the real-time detection of inherent damaging processes such as prestressing tendon break and crack opening and providing meaningful structural in-formation to support decision-making within bridge maintenance programs. An SHM system based on long-gauge fiber Bragg grat-ing (LGFBF) sensors was designed and deployed in a real-size prestressed concrete bridge. Autonomous and intelligent meas-urement tasks with data management and post-processing tools were implemented to operate the SHM system and delivery the expected results. A novel runtime algorithm for real-time analysis based on random variables correlation for condition monitoring was implemented to automatically detect unexpected events, such as local structural failure, within many random dynamic loads. Additionally, an integrated methodology for data interpretation and model updating built on data feature extraction using the principal component analysis (PCA), finite element (FE) modeling, and Monte Carlo simulations was proposed to identify existing damages and optimize the FE model updating process. The re-sults showed that the deployed SHM system successfully translates the massive raw data into meaningful information to access struc-tural response, predict damage formation, and calibrate a FE model of the monitored structure. Finally, the proposed real-time analysis algorithm delivers a reliable notification system that allows bridge managers to track unexpected events as a basis for deci-sion-making.

The ability to track the structural condition of existing structures is one of the main concerns of bridge owners and operators. In the context of bridge maintenance programs, visual inspection predominates nowadays as the primary source of information. Yet, visual inspections alone are insufficient to satisfy the current needs for safety assessment. From this perspective, extensive research on structural health monitoring has been developed in recent decades. However, the transfer rate from laboratory experiments to real-case applications is still unsatisfactory. This paper addresses the main limitations that slow the deployment and the acceptance of real-size structural health monitoring systems (SHM) and presents a novel real-time analysis algorithm based on random variable correlation for condition monitoring. The proposed algorithm was designed to respond automatically to detect unexpected events, such as local structural failure, within a multitude of random dynamic loads. The results are part of a project on SHM, where a high sensor-count monitoring system based on long-gauge fiber Bragg grating sensors (LGFBG) was installed on a prestressed concrete bridge in Neckarsulm, Germany. The authors also present the data management system developed to handle a large amount of data, and demonstrate the results from one of the implemented post-processing methods, the principal component analysis (PCA). The results showed that the deployed SHM system successfully translates the massive raw data into meaningful information. The proposed real-time analysis algorithm delivers a reliable notification system that allows bridge managers to track unexpected events as a basis for decision-making.

The ability to track the structural condition of existing structures is one of the main concerns of bridge owners and operators. In the context of bridge maintenance programs, visual inspection predominates nowadays as the primary source of information. Yet, visual inspections alone are insufficient to satisfy the current needs for safety assessment. From this perspective, extensive research on structural health monitoring has been developed in recent decades. However, the transfer rate from laboratory experiments to real-case applications is still unsatisfactory. This paper addresses the main limitations that slow the deployment and the acceptance of real-size structural health monitoring systems (SHM) and presents a novel real-time analysis algorithm based on random variable correlation for condition monitoring. The proposed algorithm was designed to respond automatically to detect unexpected events, such as local structural failure, within a multitude of random dynamic loads. The results are part of a project on SHM, where a high sensor-count monitoring system based on long-gauge fiber Bragg grating sensors (LGFBG) was installed on a prestressed concrete bridge in Neckarsulm, Germany. The authors also present the data management system developed to handle a large amount of data, and demonstrate the results from one of the implemented post-processing methods, the principal component analysis (PCA). The results showed that the deployed SHM system successfully translates the massive raw data into meaningful information. The proposed real-time analysis algorithm delivers a reliable notification system that allows bridge managers to track unexpected events as a basis for decision-making.