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This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure. [ABSTRACT FROM AUTHOR]

The digital twin (DT) research field is experiencing rapid expansion; yet, the research on industrial practices in this area remains poorly understood. This paper aims to address this knowledge gap by sharing feedback and future requirements from the manufacturing industry. The methodology employed in this study involves an examination of a survey that received 99 responses and interviews with 14 experts from 10 prominent UK organisations, most of which are involved in the defence industry in the UK. The survey and interviews explored topics such as DT design, return on investment, drivers, inhibitors, and future directions for DT development in manufacturing. This study's findings indicate that DTs should possess characteristics such as adaptability, scalability, interoperability, and the ability to support assets throughout their entire life cycle. On average, completed DT projects reach the breakeven point in less than two years. The primary motivators behind DT development were identified to be autonomy, customer satisfaction, safety, awareness, optimisation, and sustainability. Meanwhile, the main obstacles include a lack of expertise, funding, and interoperability. This study concludes that the federation of twins and a paradigm shift in industrial thinking are essential components for the future of DT development. [ABSTRACT FROM AUTHOR]

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]

Main road widening can reduce the clearance of the low-level underpass road, restricting the passage of vehicles and leading to collisions with structures. Therefore, checking the clearance of the underpass road effectively should be considered at the design stage. This paper describes a digital twin approach for checking the clearance of underpass roads in highway widening projects using online map data. The underpass road digital twin and BIM model of the newly widened road based on the existing main road digital twin are created to assist the clearance check and redesign. The proposed method presented a cost-effective clearance check for underpass roads in road widening design without field surveys and was successfully implemented in an underpass road in the UK. In future research, more digital twin methods for overpasses, bridges, tunnels, and traffic safety facilities should be employed comprehensively to assist more road widening applications. • A novel approach is presented to check the underpass road clearance in the road widening project. • A method for building road digital twins from online map data is proposed. • A systematic method for road widening based on the digital twin is proposed. • According to the clearance check, the underpass road can be redesigned to increase the insufficient clearance. [ABSTRACT FROM AUTHOR]

Sustainable construction can be defined as efficient resource use with ecological design. Three pillars are required to achieve sustainable construction: environmental protection, social well-being and economic prosperity. An increase in sustainable construction awareness among industry stakeholders could help improve levels of implementation. This paper aims to assess levels of awareness among UK practitioners and identify the barriers that inhibit sustainable construction. A mixed-method approach with questionnaire surveys and interviews was adopted. Questionnaire were distributed using MS Forms through social media (e.g., LinkedIn, Microsoft Teams, WhatsApp) and the researcher's contacts. The survey concluded with 127 useable responses. Interviews were also conducted over MS Teams with eight professionals with different backgrounds. Respondents for the interview were conveniently gathered through snowball sampling. Data from the questionnaire survey were analysed using SPSS. Descriptive statistics, Cronbach's Alpha and RII were calculated for the level of awareness of sustainable construction practices and barriers to adoption of sustainable construction. Transcripts from the interview were thematically analysed using NVivo. The findings showed that the industry practitioners have above-average levels of awareness. Sustainable construction practices that respondents were most aware of was reduce, reuse and recycle. Respondents were not as aware of newer sustainable practices such as digital twin. Surprisingly, the most prevalent barrier was not cost. According to the respondents, the most prevalent barrier was a tendency to maintain current practices. Many real-life experiences are documented in this research. Insights into current practice in the industry and key barriers to adopting sustainable construction are identified. Addressing these barriers could improve the adoption of sustainable construction in the UK. • investigates up-to-date techniques for attaining sustainable construction in the UK. • assess the level of awareness among industry practitioners. • identifies the hurdles faced by the industry to attain sustainable construction. • explains the current practices of construction and areas for potential improvement. [ABSTRACT FROM AUTHOR]

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. • This paper reviewed the state-of-the-art of Digital Twins (DTs) in Civil Engineering. • This paper proposes a step-by-step workflow process for DTs. • This paper applied the method for an existing bridge. • This paper presented a preliminary physical-virtual architecture of a bridge DT. • This paper contributes to the knowledge challenge, value proposition and operation dictated by developing a DT. [ABSTRACT FROM AUTHOR]

The energy consumption and its related carbon emission of non-domestic complex buildings in an urban context are complicated due to their wide variety of functions and services. A detailed assessment of the carbon emission of such buildings can contribute to decision making for in-operation building management and schematic designs of future proposals. Concurrently, advances in smart meter data analytics and sensor-enabled operational data streams offer the opportunity to investigate this problem at a finer temporal resolution. This research developed a daily carbon emission benchmarking system of a mixed-use building in a UK university. The research period was set at an annual range from 1 January 2019 to 31 December 2019 and was segmented by strategic periods in line with the operation schedule of the building. The daily benchmark revealed the fluctuation of the building's energy consumption and associated carbon emissions. Based on this, a digital twin framework was developed to identify the possible time periods when the building is less carbon efficient and potential building characters that can lead to increased carbon emission in the operational stage compared with what originally expected at the design stage. We discuss how these insights can offer actionable knowledge for user groups such as asset managers and architects. [ABSTRACT FROM AUTHOR]

By constructing digital twins (DT) of an integrated energy system (IES), one can benefit from DT's predictive capabilities to improve coordinations among various energy converters, hence enhancing energy efficiency, cost savings and carbon emission reduction. This paper is motivated by the fact that practical IESs suffer from multiple uncertainty sources, and complicated surrounding environment. To address this problem, a novel DT-based day-ahead scheduling method is proposed. The physical IES is modelled as a multi-vector energy system in its virtual space that interacts with the physical IES to manipulate its operations. A deep neural network is trained to make statistical cost-saving scheduling by learning from both historical forecasting errors and day-ahead forecasts. Case studies of IESs show that the proposed DT-based method is able to reduce the operating cost of IES by 63.5%, comparing to the existing forecast-based scheduling methods. It is also found that both electric vehicles and thermal energy storages play proactive roles in the proposed method, highlighting their importance in future energy system integration and decarbonisation. • A deep learning and digital twins (DT) based scheduling method is proposed. • Physical IES performance is addressed by its virtual replica's predictive capability. • A machine learning method is proposed to address multiple uncertainty challenges. • Historical U.K. datasets are used for performance evaluations. • Results demonstrate the Digital Twin model reduces the extra operating cost by 63.5%. [ABSTRACT FROM AUTHOR]