Visual inspection remains an essential tool for assessing structural damage. Damage detection is a challenging task for those specifying, designing, and deploying SHM systems. Often only traditional visual inspection processes are available to determine the type and extent of structural damage. For bridge structures in the UK, a regime of general (every two years) and principal (every six years) inspections is often followed. Such visual inspections are time-consuming and costly in terms of both labour and financial resources. Therefore, the possibility of completing more of the bridge visual inspection process offsite has many potential benefits for bridge owners and managers during the service life of the asset. Recent research conducted at the University of Bristol in collaboration with industrial partners has examined how to make the best use of metrics derived from visual inspection data when assessing bridge condition and planning maintenance activities. Recent research into which aspects of the current visual inspection regime in the UK could potentially be moved offsite has also been carried out. This paper summarises these research efforts and discusses how AI may be used as part of future enhancements to visual inspection data capture and analysis.

Hydraulic actions resulting in scour are a major cause of bridge failures worldwide. The development of scour is affected by various factors including structural geometry, river morphology and weather conditions. Detection of scour is often carried out using visual inspection-based methods. Visual inspection requires the expertise of divers which incurs high levels of risk, especially during times of high-water flow. Therefore, sensing technologies are becoming increasingly popular with the aim to supplement or replace higher risk visual inspection approaches. Specification of Structural Health Monitoring (SHM) systems is an important activity for bridge engineers and asset managers. This activity can be assisted by SHM rating frameworks, the use of which gives evidence to support specification of monitoring systems for individual bridges. In this paper, three previously published scour rating frameworks are compared using a bridge case study database.

Digitalisation and the Internet of Things (IoT) help city councils improve services, increase productivity and reduce costs. City-scale monitoring of traffic and pollution enables the development of insights into low-air quality areas and the introduction of improvements. IoT provides a platform for the intelligent interconnection of everyday objects and has become an integral part of a citizen's life. Anyone can monitor from their fitness to the air quality of their immediate environment using everyday technologies. With caveats around privacy and accuracy, such data could even complement those collected by authorities at city-scale, for validating or improving policies. The authors explore the hierarchies of urban sensing from citizen-to city-scale, how sensing at different levels may be interlinked, and the challenges of managing the urban IoT. The authors provide examples from the UK, map the data generation processes across levels of urban hierarchies and discuss the role of emerging sociotechnical urban sensing infrastructures, that is, independent, open, and transparent capabilities that facilitate stakeholder engagement and collection and curation of grassroots data. The authors discuss how such capabilities can become a conduit for the alignment of community- and city-level action via an example of tracking the use of shared electric bicycles in Bristol, UK.

The visual inspection of bridges is a major undertaking for asset owners and operators. In the UK, visual inspections require inspectors to visit bridges on site and often at night and under unfavourable weather conditions. Therefore, it would be beneficial to move some of the visual inspection process off site. This paper studies whether the defect classification aspects of the inspection process could be conducted remotely using photographs. This study examines the defect ratings assigned by ten survey participants who were tasked with examining photographs from visual inspections of ten UK bridges. The survey results were compared with the results from the general inspections previously carried out for the bridges in question. From this data set, the differences in the ratings given and the extent to which defects are missed were examined. The results show that a higher number of defects were identified for a given bridge by the remote inspectors. Statistical analysis shows that aggregated defects rated by off-site inspectors tend to be more severe and of a higher priority rating compared with those from the on-site inspectors. The results also indicate that there is closer agreement between on-site and off-site inspectors for defects of a higher severity rating.

Visual inspection remains key for assessing the condition of bridges and hence assisting with planning and maintenance activities. There have been many efforts to improve or supplement visual inspection processes using new sensing technologies and data capture methods to usher in an era of 'smart bridges' or 'smart infrastructure'. One method to improve data capture is a 'remote inspection' where inspectors use digital photographs of a bridge to identify and grade structural defects to the standard of a 'General inspection' (GI). In this paper, survey data is presented to help formulate a preliminary assessment of the potential for engineers to implement this possible evolution of the visual inspection process. A potential Schema for remote visual inspections is developed and presented as a conceptual web application. The focus on the development of the Schema includes the need for ease of use by inspectors and integration of collected digital data into bridge management systems. The suggested platform is seen as a transitional method to aid in the long-term implementation of further automation of the inspection process. The system architecture is provided along with possible technologies that may support or enhance it, as well as a discussion of the potential barriers to implementation.

Digital Twins (DTs) are forecasted to be used in two-thirds of large industrial companies in the next decade. In the Architecture, Engineering and Construction (AEC) sector, their actual application is still largely at the prototype stage. Industry and academia are currently reconciling many competing definitions and unclear processes for developing DTs. There is a compelling need to establish DTs as practice in AEC by developing common procedures and standards tailored to the sector's procedures and use cases. This paper proposes a step-by-step workflow process for developing a DT for an existing asset in the built environment, providing a proof-of-concept case study based on the Clifton Suspension Bridge in Bristol (UK). To achieve its aim, this paper (i) reviews the state-of-the-art of DTs in Civil Engineering, (ii) proposes a working DT-based workflow framework for the built environment applicable to existing assets, (iii) applies the framework and develops of the physical-virtual architecture to a case study of bridge management, and finally (iv) discusses insights from the application. The main novelty lies in the development of a versatile methodological framework that can be applied to the broad context of civil infrastructure. This paper's importance resides in the knowledge challenge, value proposition and operation dictated by developing a DT workflow for the built environment, which ultimately represents a relevant use case for the digital transformation of national infrastructure.

Society is dependent on aging infrastructure, which usually operates outside its expected life. Replacing this infrastructure is often an unviable option due to its cost and disruption. A structure's operational life might be extended if the features of its aging are better understood, enabling preventive maintenance to compensate. Digital Twins (the continuous comparison between sensor measurements and a mathematical model) are one way of enabling this sort of data-driven decision making. However, despite the possibilities for this technology, its take up amongst industry has been slow, in part because infrastructure managers are unsure of how the technology will support them. This work develops a methodological framework to enhance this uptake in the field of systems engineering and the system development life cycle, using the developed knowledge to inform how an operational Digital Twin should be created. The requirements capture is the most important part of any system design development process. We present a Digital Twin development method, grounded firmly in a thorough requirements capture, and illustrate how those requirements inform many of the later design decisions. We then present our method through a case study of the Clifton Suspension Bridge, UK. Our method provides a series of actionable steps, the completion of which will facilitate the creation of a Digital Twin able to support operational decisions. By fulfilling the requirements of infrastructure managers, we hope to encourage the uptake of the technology.