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 toward standardisation 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 (DTs) 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 DTs 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 a 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 DTs. The distinct aspects of the different industries and their assets are evaluated to the conclusion that DTs are better employed for maintenance of structures within E&C. DTs 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.

Digital twins (DT) are seen as a transformative solution for the Engineering & Construction (E&C) industry’s challenges. However, there is a gap between DT research and operational applications. This paper proposes a framework for a DT, exemplified by a trough bridge case study at Luleå University of Technology (LTU). Traditional bridge management relies on manual, time-consuming methods, leading to reduced accuracy and reliability. The DT platform integrates a 3D model with real-time Structural Health Monitoring (SHM) data, providing a collaborative environment for asset management. The proposed platform can be scalable to other structures; it facilitates data interpretation and decision-making, offering a step towards enhanced asset management practices. By bridging the gap between potential and operational applications of DT technology, this study contributes to the evolution of efficient asset management practices in the E&C industry.

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.

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 construction industry has a significant impact in terms of financial and environmental resources but is vastly behind other sectors in terms of digitalization. The potential of this industry to be improved by new technology has been reflected in huge trends in research for terms such as "digital twins". However, the purpose of such technologies and how they can be applied to specific needs and assets in the construction sector is not always clear. This paper proposes an analysis of the purpose, current and future states of digital twins in the construction industry, based on a review of the evolution of research in the topic and recent market applications. Even though there is a discrepancy between research and level of development of tangible applications, it is undeniable that the digital transformation will reach the construction industry. The efforts should then be focused on technology that can be translated to its assets, such as smart management, and will generate tangible results that can survive outside the theoretical realm.

Bridges are an essential part of railways infrastructure, and it is necessary to monitor their operation during their service life.To ensure the safety of a bridge, routine inspections are required to detect likely defect areas.Railway owners manage spread rail tracks and infrastructures, which one of the most important approach is structural health monitoring (SHM).It leads engineers to implement a package of measures for maintenance services, and follow serviceability and safety issues, which usually needs putting high amount of investments into that programs.Typically, a routine inspection consists of field measurements and visual observations made by a human inspector, and the main purpose of that is to collect documents of detected defects on infrastructures.In this study, remote inspection on a model generated by photogrammetry technique is discussed.In a practical way, by generating a 3D model with photogrammetry, inspectors can carry out visual structural health monitoring on a computer remotely.Hence, the generated model is compared with that of laser scanning, in terms of resolution and accuracy, and finally, the possibility of remote inspection of railways infrastructure is discussed.

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 of these 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.

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.

Corrosion of steel reinforcement is the major cause of deterioration in reinforced concrete structures. Strengthening of concrete structures has been widely studied. However, most research was conducted on sound structures without considering the effects of corrosion. This paper presents an experimental study of the feasibility of using externally bonded FRP laminates combined with U-jackets, applied directly without repairing the deteriorated concrete cover, to strengthen beams with corroded reinforcement. Ten beams were tested in four-point bending. Two beams were not deteriorated and non strengthened; these served as references. The other eight were pre-loaded to induce flexural cracks and then exposed to accelerated corrosion. Two of the deteriorated beams were not strengthened, three were strengthened with glass-FRP (GFRP) laminates and three with carbon-FRP (CFRP) plates on the beam soffits. On the six strengthened beams, CFRP U-jackets were installed along the span. Local corrosion levels were evaluated with a 3D-scanning technique. Pitting corrosion significantly reduced the load-carrying and deformation capacity of the deteriorated beams. Despite average corrosion levels of 20%, local corrosion levels up to 57% and corrosion-induced cracks up to 1.9 mm wide, the FRPstrengthening method (applied directly to the beams without repairing the deteriorated concrete cover) was effective in upgrading the load-carrying capacity and flexural stiffness. The applied U-jackets effectively suppressed the delamination of the concrete cover and led to the rupture of GFRP laminates and a utilisation ratio of CFRP plates up to 64%. However, improvement in the deformation capacity was not noticeable; this requires further research. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

For the inspection of structures, particularly bridges, it is becoming common to replace humans with autonomous systems that use unmanned aerial vehicles (UAV). In this paper, a framework for autonomous bridge inspection using a UAV is proposed with a four-step workflow: (a) data acquisition with an efficient UAV flight path, (b) computer vision comprising training, testing and validation of convolutional neural networks (ConvNets), (c) point cloud generation using intelligent hierarchical dense structure from motion (DSfM), and (d) damage quantification. This workflow starts with planning the most efficient flight path that allows for capturing of the minimum number of images required to achieve the maximum accuracy for the desired defect size, then followed by bridge and damage recognition. Three types of autonomous detection are used: masking the background of the images, detecting areas of potential damage, and pixel-wise damage segmentation. Detection of bridge components by masking extraneous parts of the image, such as vegetation, sky, roads or rivers, can improve the 3D reconstruction in the feature detection and matching stages. In addition, detecting damaged areas involves the UAV capturing close-range images of these critical regions, and damage segmentation facilitates damage quantification using 2D images. By application of DSfM, a denser and more accurate point cloud can be generated for these detected areas, and aligned to the overall point cloud to create a digital model of the bridge. Then, this generated point cloud is evaluated in terms of outlier noise, and surface deviation. Finally, damage that has been detected is quantified and verified, based on the point cloud generated using the Terrestrial Laser Scanning (TLS) method. The results indicate this workflow for autonomous bridge inspection has potential.

The maturity of Digital Twin (DT) models has evolved in the aerospace and manufacturing industries; however, the construction industry still lags behind. DT technology can be applied to achieve smart management through the entire life cycle of structures. Particularly for bridge structures, which play an essential role in any transportation system and can have high maintenance demands throughout their long life spans. In this study, a literature review on DTs was performed, from the origins of the concept until current best practice focused on bridges. Especially concerning structural analysis and facility management, few studies that employ DT for bridges were encountered. The main challenges identified are related to treatment of the large amount of data involved in the process, mostly gathered from different platforms. Finally, a framework for smart facility management of bridges using DTs was proposed to tackle potential solutions.

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.

The enhanced carbon footprint of the construction sector has created the need for CO2 emission control and mitigation. CO2 emissions in the construction sector are influenced by a variety of factors, including raw material preparation, cement production, and, most notably, the construction process. Thus, using biobased constituents in cement could reduce CO2 emissions. However, biobased constituents can degrade and have a negative impact on cement performance. Recently, carbonised biomass known as biochar has been found to be an effective partial replacement for cement. Various studies have reported improved mechanical strength and thermal properties with the inclusion of biochar in concrete. To comprehend the properties of biochar-added cementitious materials, the properties of biochar and their effect on concrete need to be examined. This review provides a critical examination of the mechanical and thermal properties of biochar and biochar-added cementitious materials. The study also covers biochar's life cycle assessment and economic benefits. Overall, the purpose of this review article is to provide a means for researchers in the relevant field to gain a deeper understanding of the innate properties of biochar imparted into biochar-added cementitious materials for property enhancement and reduction of CO2 emissions.

This study is carried out to assess the applicability of using a digital image correlation (DIC) system in structural inspection, leading to deploy innovative instruments for strain/stress estimation along embedded rebars. A semi-empirical equation is proposed to predict the strain in embedded rebars as a function of surface strain in RC members. The proposed equation is validated by monitoring the surface strain in ten concrete tensile members, which are instrumented by strain gauges along the internal steel rebar. One advantage with this proposed model is the possibility to predict the local strain along the rebar, unlike previous models that only monitored average strain on the rebar. The results show the feasibility of strain prediction in embedded reinforcement using surface strain obtained by DIC.

Conventional bridge inspection is usually performed by experienced engineers, trying to detect and document damage patterns manually. By increased number of built Bridges, there is a growing interest in automated damage detection methods. Therefore, the field of autonomous bridge inspection with the application of machine learning techniques on UAV-taken images is gaining popularity. Due to recent technological advancement, a large number of datasets can be collected, with a high rate of productivity and accuracy, to train convolutional neural networks (CNNs) leading us to automated Structural health monitoring (SHM). In this paper, a case study is chosen to scan two times with almost one year as a time interval. In the first scanning, dataset was gathered to train four different CNNs. Then, the performance of CNNs was compared for the purpose of autonomous crack detection in the second round of scanning. Models evaluated on a number of performance metrics, namely- (i) accuracy, (ii) loss, (iii) computation time, (iv) model size, and (v) architectural depth. Finally, the performance of studied CNNs is discussed, which can lead researchers in the Transfer-Learning approach to generate a model for damage detection with a limited number of datasets prepared in the first turn of bridge inspection.

In bridge inspection, vertical displacement is a relevant parameter for both short and long-term health monitoring. Assessing change in deflections could also simplify the assessment work for inspectors. Recent developments in digital camera technology and photogrammetry software enables point cloud with colour information (RGB values) to be generated. Thus, close range photogrammetry offers the potential of monitoring big and small-scale damages by point clouds. The current paper aims to monitor geometrical deviations in Pahtajokk Bridge, Northern Sweden, using an optical data acquisition technique. The bridge in this study is scanned two times by almost one year a part. After point cloud generation the datasets were compared to detect geometrical deviations. First scanning was carried out by both close range photogrammetry (CRP) and terrestrial laser scanning (TLS), while second scanning was performed by CRP only. Analyzing the results has shown the potential of CRP in bridge inspection.

Throughout the world, many medieval and historic bridges remain in operation. Deterioration and failures have increased in the already aging bridges due to consistent growth in traffic volume and axle loads. Therefore, the importance of Bridge Management Systems (BMS) to ensure safety of operation and maximize maintenance investments has also increased. Recent improvements in technology also contribute to the demand for optimized and more resource-efficient BMS. In this study, a literature review was performed to map current bridge management practices and systems in operation in the world. The outcomes identified Bridge Information Modelling (BrIM) and Digital Twins as novel approaches that enable efficient management of the whole lifecycle of a bridge. From these outcomes, a framework of an ideal BMS is proposed to achieve automated and smart management of bridges.

Routine bridge inspections usually consist of visual observations. These inspections are time-consuming and subjective. There is a need to identify new inspection techniques for infrastructure that reduce traffic disturbance, and improve the efficiency and reliability of the acquired data. This study compared the performance of three different imaging technologies for the three-dimensional (3D) geometric modelling of existing structures: terrestrial laser scanning, close-range photogrammetry, and infrared scanning. Each technology was used to assess six existing concrete railway bridges. The technologies were compared in terms of geometric deviations, visualization capabilities, the level of the inspector’s experience, and degree of automation. The results suggest that all methods investigated can be used to create 3D models, however, with different level of completeness.