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There has been a vision of creating bridge digital twins as virtual simulation models of bridge assets to facilitate remote management. Bridge model updating is one digital twin technology which can enable the continuous updating of the structural model as new monitoring data is collected. This paper examines why there is currently little industry uptake of monitoring, modelling and model updating for the operation and maintenance of bridges despite over two decades of research in these fields. The study analyses the findings from a series of semi-structured industry interviews with expert bridge professionals in the U.K. and from an extensive literature survey of bridge model updating studies to examine the disconnects between research and practice and the practical issues of implementing bridge model updating. In particular, the study found that localised damage resulting in local reduction in structural stiffness, a key assumption made in the majority of research, is subject to question by practitioners as many common types of bridge damage may not induce noticeable change in structural stiffness that existing model updating techniques would identify. Key recommendations for future research are proposed to drive adoption of bridge monitoring, modelling and model updating and thus realise their industrial value. [ABSTRACT FROM AUTHOR]

The cost and effort of modeling existing bridges from point clouds currently outweighs the perceived benefits of the resulting model. There is a pressing need to automate this process. Previous research has achieved the automatic generation of surface primitives combined with rule‐based classification to create labeled cuboids and cylinders from point clouds. Although these methods work well in synthetic data sets or idealized cases, they encounter huge challenges when dealing with real‐world bridge point clouds, which are often unevenly distributed and suffer from occlusions. In addition, real bridge geometries are complicated. In this article, we propose a novel top‐down method to tackle these challenges for detecting slab, pier, pier cap, and girder components in reinforced concrete bridges. This method uses a slicing algorithm to separate the deck assembly from pier assemblies. It then detects and segments pier caps using their surface normal, and girders using oriented bounding boxes and density histograms. Finally, our method merges oversegments into individually labeled point clusters. The results of 10 real‐world bridge point cloud experiments indicate that our method achieves very high detection performance. This is the first method of its kind to achieve robust detection performance for the four component types in reinforced concrete bridges and to directly produce labeled point clusters. Our work provides a solid foundation for future work in generating rich Industry Foundation Classes models from the labeled point clusters. [ABSTRACT FROM AUTHOR]

This study investigates integrating fibre optic sensing technology into the production process of concrete railway sleepers. Robust fibre Bragg grating strain and temperature sensor arrays were developed specifically for this application and were designed for long-term monitoring of sleeper performance. The sensors were used to monitor sleeper production and to help gain a deeper understanding of their early-age behaviour which can highly influence long-term performance. In total, 12 sleepers were instrumented and strain data were collected during the entire manufacturing process including concrete casting and curing, prestressing strand detensioning and qualification testing. Following the production process, sleepers were stored temporarily and monitored for 4 months until being placed in service. The monitoring results highlight the intrinsic variability in strain development among identical sleepers, despite high levels of production quality control. Using prestress loss as a quality control indicator, the integrated sensing system demonstrated that sleepers were performing within Eurocode-based design limits prior to being placed in service. A three-dimensional nonlinear finite element model was developed to provide additional insight into the sleepers' early-age behaviour. Based on the fibre Bragg grating-calibrated finite element model, more realistic estimates for the creep coefficient were provided and found to be 48% of the Eurocode-predicted values. [ABSTRACT FROM AUTHOR]

Deterioration due to corrosion of the main cables on long-span suspension bridges is a worldwide concern. As part of an ongoing project, wireless sensors have been installed in the north anchorage of the Humber Bridge, which is the UK's longest suspension bridge with a total length of approximately 2 km. The wireless sensor network (WSN), which consists of ten relative humidity and temperature sensors, allows the environmental conditions in the north cable anchorage to be assessed in real-time from anywhere via an internet portal. The sensors were deployed in a variety of locations including next to the main cable strands, providing an improvement over the existing wired system. The installation was not flawless, however, as difficulties with radio transmission meant that the full system was not operational until these problems were overcome. When developing a WSN, careful consideration needs to be given to the choice of software and hardware so that the deployment is efficient and robust in terms of communication and battery life. The network has demonstrated it is capable of capturing the environmental conditions both in terms of daily variations and critical events. These data can be analysed and presented in a concise and customisable fashion, allowing for future savings on energy, maintenance and inspection costs. [ABSTRACT FROM AUTHOR]