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Stable Unbonded Fibre Reinforced Elastomeric Isolators (SU-FREI) have been investigated extensively for seismic applications, with over 20 years of literature supporting their use in isolation of structures as an alternative to conventional Steel Reinforced Elastomeric Isolators (SREI). Preliminary investigations have been conducted into their potential use as bridge bearings, where they could provide an alternative to unreinforced and steel reinforced elastomeric bearings. SU-FREI offer a number of potential advantages in this application relative to SREI, including compactness due to thinner reinforcement layers, ease of installation, rotational tolerances, and ease of manufacture. Recently, SU-FREI have been installed under certain highway bridges along the 407 ETR where the previous unreinforced elastomeric bearings had experienced failure. Monitoring has been conducted by Associated Engineering for the 407 ETR Corporation. Data was collected from this monitoring program and field visits have been conducted to observe the condition of the bearings. The original design process used for the SU-FREI bearings has also been reviewed. The monitoring data was compared against design calculations, and the behavior of the SU-FREI analysed to determine whether they meet performance expectations and are suitable for further use as bridge bearings. It was found that some SU-FREI had experienced degradation, including the appearance of an unidentified liquid. The causes of deterioration were investigated and postulated to be primarily related to design limitations imposed by the geometry of the bridges, original design calculation assumptions, and installation issues. Where these factors were not present, the SU-FREI bearings were found to have experienced little to no deterioration. Furthermore, it was determined through comparative design calculations that the FREI outperformed equivalent SREI with regard to rotational capacity. Recommendations have been developed for future deployment of SU-FREI as bridge bearings. Based on the results of initial deployments, larger-scale employment of SU-FREI in this application should be considered. / Thesis / Master of Applied Science (MASc) / Concrete and steel bridges deform due to temperature changes, traffic motion, and other factors. To allow these deformations without inducing large forces or damage, bearings are employed between the bridge deck and supports. One type of bearing consists of alternating layers of rubber and steel: steel strengthens the bearing, while the rubber provides lateral and rotational flexibility. A relatively new type of bearing has been developed and tested that replaces the steel layers with carbon fibre. Recently, several of these new SU-FREI bearings have been installed on actual highway bridges. The focus of this thesis is on the performance of SU-FREI installed as bridge bearings. The goal was to observe their performance, identify any unexpected behavior, and create recommendations for future consideration whenever SU-FREI are to be used as bridge bearings. Generally, it was found that their performance is satisfactory provided they are designed and used appropriately.

A machine learning approach to damage detection is presented for a bridge structural health monitoring system, validated on the renowned Z-24 bridge benchmark dataset where a sensor instrumented, threespan bridge was realistically damaged in stages. A Bayesian autoencoder neural network is trained to reconstruct raw sensor data sequences, with uncertainty bounds in prediction. The reconstruction error is then compared with a healthy-state error distribution and the sequence determined to come from a healthy state or not. Several realistic damage stages were successfully detected, making this a viable approach in a data-based monitoring system of an operational bridge. This is a fully operational, machine learning based bridge damage detection system, that is learned directly from raw sensor data. / En maskininlärningsmetod för strukturell skadedetektering av broar presenteras. Metoden valideras på det kända referensdataset Z-24, där en sensor-instrumenterad trespannsbro stegvist skadats. Ett Bayesianskt neuralt nätverk med autoenkoders tränas till att rekonstruera råa sensordatasekvenser, med osäkerhetsgränser i förutsägningen. Rekonstrueringsavvikelsen jämförs med avvikelsesfördelningen i oskadat tillstånd och sekvensen bedöms att komma från ett skadad eller icke skadat tillstånd. Flera realistiska stegvisa skadetillstånd upptäcktes, vilket gör metoden användbar i ett databaserat skadedetektionssystem för en bro i full storlek. Detta är ett lovande steg mot ett helt operativt databaserat skadedetektionssystem.

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