The discussions on digital twins (DT) have greatly increased, and this technology is believed to solve many of the engineering and construction (E&C) industry’s problems. However, there is a significant gap between this perceived potential and the maturity level of practical applications. The proliferation of context-specific frameworks for each DT project hinders the widespread adoption of DTs across the industry. This paper aims to shift the focus from individual DT frameworks to viewing DT as a tailored pack of existing technologies, chosen according to the purpose of the digitalisation initiative. A purpose-driven roadmap for DT adoption in the E&C industry is introduced to bridge the gap between perceived potential and practical applications. The methodology of this study comprises a literature review, DT investigation, and a case study analysis. The stages of the proposed roadmap stages include assessment, purpose definition, technology selection, implementation, and optimisation. The roadmap is validated, and its application is demonstrated through a case study of a reinforced concrete chimney in Sweden, where two longitudinal cracks had been previously identified. The conclusion highlights findings and future directions, emphasising the roadmap’s role in fostering a flexible and impactful adoption of DTs in the E&C industry.

The use of digital twin (DT) technology within the engineering and construction (E&C) industry is valuable for practical applications in asset management of structures. Functional DT in E&C, however, are still in initial stages of development. Efforts towards standardization of concepts and procedures are necessary to build on existing knowledge and drive progress further on functional DT. This paper proposes a DT of a snow gallery, part of the Iron Ore railway in northern Sweden. The gallery was instrumented with a structural health monitoring (SHM) system that feeds data in real time to the DT, which also includes a 3D model of the gallery. The proposed methodology can be replicated to different structures and scaled for larger amounts of data. The SHM data and the 3D digital model of the snow gallery are connected in a single, integrated platform that enables improved decision-making for maintenance of the gallery. To promote clarity and progress within the field, the proposed DT’s maturity level is classified in terms of autonomy, intelligence, learning, and fidelity. The snow galleries, the SHM system, and the proposed DT are all presented and discussed, following a brief review on DT, the importance of level classification, and predictive maintenance.

Digital Twins (DT) have become a widely discussed subject, believed to have the potential to solve various problems across different industries, including Engineering & Construction (E&C). However, there is still significant misconception concerning the definition of DT and their purpose within E&C. This study dives deep into identifying DT applications within E&C and the other prominent industries, i.e., Aerospace & Aviation, Manufacturing, Energy & Utilities, Automotive, Healthcare, Smart Cities, Oil & Gas, and Retail. The main challenges to the evolution of DT practical applications are also analyzed. A combination of literature review, multi-case study analysis, and comparative analysis compose the deployed methodology. Standardization and a maturity level classification are proposed to drive progress of the adoption of DT. The distinct aspects of the different industries and their assets are evaluated to the conclusion that DT are better employed to maintenance of structures within E&C. DT have become a well-worn topic, but the abundance of complex theoretical frameworks is met with simple or inexistent practical applications. Therefore, the novelty of this study lays in its comprehensive analysis of DT applications and real-world implementations – a departure from the often-theoretical discussions surrounding DTs.

Nowadays precast concrete multi-story dual system structures require Finite Element Method structural analysis. The stiffness of the vertical connections between wall panels influences the internal forces distribution. European standard EN 1992-1-1 recommends verification of strength and deformability of precast connections to be assisted by testing. Six different connection layouts were tested under pure shear with a special attention towards deformability. All connections showed close to/equivalent monolithic initial stiffness. Depending on the connection layout, the post-cracking stiffness is reduced. The equation for shear resistance of interfaces between concrete cast at different times according to EN 1992-1-1 can lead to significant estimation errors due to its weak physical meaning (in disagreement with experimental observed failure mechanisms). A non-linear finite element analysis solution strategy is validated by experimental results that shows how stochastic parameters can inflict large variation in terms of strength and stiffness.

The construction industry has a significant impact in terms of financialand environmental resources but is vastly behind other sectors in terms of digitalization.The potential of this industry to be improved by new technology has beenreflected in huge trends in research for terms such as “digital twins”. However, thepurpose of such technologies and how they can be applied to specific needs andassets in the construction sector is not always clear. This paper proposes an analysisof the purpose, current and future states of digital twins in the constructionindustry, based on a reviewof the evolution of research in the topic and recentmarketapplications. Even though there is a discrepancy between research and levelof development of tangible applications, it is undeniable that the digital transformationwill reach the construction industry. The efforts should then be focusedon technology that can be translated to its assets, such as smart management, andwill generate tangible results that can survive outside the theoretical realm.

Bridge inspections are relied heavily on visual inspection, and usually conducted within limited time windows, typically at night, to minimize their impact on traffic. This makes it difficult to inspect every meter of the structure, especially for large-scale bridges with hard-to-access areas, which creates a risk of missing serious defects or even safety hazards. This paper presents a new technique for the semi-automated damage detection in tunnel linings and bridges using a hybrid approach based on photogrammetry and deep learning. The first approach involves using photogrammetry to reconstruct a 3D model. It is shown that a model with sub-centimeter accuracy can be obtained after noise removal. However, noise removal also reduces the point cloud density, making the 3D point cloud unsuitable for quantification of small-scale damages such as fine cracks. Therefore, the captured images are also analyzed using deep convolutional neural network (CNN) models to enable crack detection and segmentation. For this aim, in the second approach, the 3D model is generated by the output of CNN models to enable crack localization and quantification on 3D digital model. These two approaches were evaluated in separate case studies, showing that the proposed technique could be a valuable tool to assist human inspectors in detecting, localizing, and quantifying defects on concrete structures.

The need to optimize investments in bridge maintenance has created a demand for improved bridge management systems (BMS). Outdated practices in bridge inspection and constant advances in information technology have also contributed to this demand. The use of Digital Twins (DT), although well established in other industries, is still incipient for asset management and structural analysis of bridges. There is a great deal of research on Building Information Modelling (BIM) for bridge inspection, but its post-construction potential is still under-explored. This study presents a state-of-the-art review of the literature on asset management for bridges using digital models such as BIM and digital twins. The review was conducting using a systematic approach. Despite the rapid increase in research on DT and the amount of existing research on BIM, several gaps remain to be addressed, such as the lack of consensus about the definition of digital twins, which has led to wrongful categorisation of digital models as DT. The complex data flow and software compatibility required to develop a functional DT have hindered the exploitation of their full potential so far. The integration of BIM post-construction to BMS and existing automation technologies can also significantly improve current practices of bridge management.

As reinforced concrete structures reach the end of their design lives, technology for improving accuracy and efficiency of inspections and structural health monitoring rapidly progresses. Concrete cracking and reinforcement strains are two relevant parameters in assessing damage and safety ofthese structures. The use of Digital Image Correlation (DIC) systems and distributed Fibre Optic Sensors (FOS) to evaluate these parameters are two of the technologies that have been gaining momentum due to their advantages over other approaches. This study presents an experimental investigation of crack propagation of a reinforced concrete beam specimen through FOS and DIC.The FOS were positioned inside a groove carved in the rebar and in the concrete immediately outside the bar for comparison. The results showed a significant difference between both positions, with more reliable data coming from inside the bar. The addition of the DIC crack propagation images to the FOS analysis complemented the results, and good visual correlation was identified between both methods. This study is part of a broader research program, which aims at applying DIC and FOS for structural health monitoring of a real scale bridge structure.

Bridge structures have significantly long life spans; many medieval and historic bridges remain in operation in the world. The concept of bridge management contains the activities related to managing bridge inspections and condition assessment, which can be gathered into a Bridge Management System (BMS). Deterioration and failures have increased over the years in the already aging bridges; therefore, the importance of BMS to ensure safety of bridge operation and maximize investments in bridge maintenance has also increased. Digital Twin (DT) technology can be applied in the construction industry to achieve smart management through the entire life cycle of structures. Unlike the aerospace and manufacturing industries, the maturity of development of DT models in the construction industry still lags behind. In this study, a literature review was initially performed to gather knowledge on the origins of the digital twin concept and current best practice focused on bridge structures. A systematic approach for the literature review is presented in the methodology. Lastly, a framework for facility management of bridge structures using digital twins is proposed.