The snow galleries along the Iron Ore railway line in northern Sweden have facedproblems in recent years due to increasingly large snow loads, and several gallerieshave been damaged. These incidents motivated an evaluation of the maximumload supported by the galleries before collapse, which is presented in this study. In2021, a monitoring system was installed in one of the main frames of two snowgalleries built in the 1950s to follow up with temperature and displacements,including a trigger that sends out a warning message when a critical load isreached. A literature review on snow loads was performed, followed bycalculations on snow distribution on the galleries based on the Eurocodes andNational Swedish Standards. Finite element 2D and 3D models were created usingAxisVM to accurately assess the efforts in the structural elements. Analysis anddiscussion are complemented by observations from site visits. It was concludedthat the critical loads supported by the galleries are lower than the requirements oftoday’s standards, but since secondary construction elements were damagedbefore the main frames reached their full capacity, no major collapse has yet takenplace. The cobweb effect (load re-distribution between the neighboring elementsin a 3D structure) influenced the behavior of the galleries in the 3D analysis and thecapacity of the main frames proved to be significantly increased compared to the2D assessment.

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.

The Herøysunds bridge on the west-coast in Nordland Fylke in Norway had undergone concrete repair works with focus on reinforcement corrosion. During these actions, it was discovered that the tendon ducts had loss of injection. To ensure the safety of the bridge it was decided to map voids and defects in the ducts. Several risk areas were pointed out by the constructor and a non-destructive test (NDT) was carried out. A methodology with combin-ation of different NDT methods were used; cover meter, GPR, UPE, IE and Videoscope. The results from the NDT investigate shows that the suggested approach was successful, and it was possible with high accuracy to detect voids in the ducts. The methodology forms a basis for the procedure recommended by the Norwegian Public Roads Administration.

The Herøysunds bridge on the west-coast in Nordland Fylke in Norway had undergone concrete repair works with focus on reinforcement corrosion. During these actions, it was discovered that the tendon ducts had loss of injection.

To ensure the safety of the bridge it was decided to map voids and defects in the ducts. Several risk areas were pointed out by the contractor and a non-destructive test (NDT) was carried out. The scope of the NDT inspection was to determine voids in the cable ducts at the areas which had been pointed out by the designer. In the project we followed a strict procedure. Based on this we used a combination of different methods such as GPR (Ground Penetrating Radar), UPE (Ultra Pulse Echo) and IE (Impact Echo). It should be mentioned that considerable experience is needed when these methods are combined to investigate voids and defects in tendon ducts. In addition, it is difficult to determine the degree of grouting in the duct and the NDT methods often need to be combined with partly destructive testing, i.e. a hole needs to be drilled into the duct and closer investigation with for example endoscope might be needed. The results from the NDT investigate shows that the suggested approach was successful and it was possible with high accuracy to detect voids in the ducts. The methodology forms a basis for the procedure recommended by the Norwegian Public Roads Administration.

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.

The aim of this study was to find strains in embedded reinforcement by monitoring surface deformations. Compared with analytical methods, application of the machine learning regression technique imparts a noteworthy reduction in modeling complexity caused by the tension stiffening effect. The present research aimed to achieve a hybrid learning approach for non-contact prediction of embedded strains based on surface deformations monitored by digital image correlation (DIC). However, due to the small training dataset collected by the installed strain gauges, the input dataset was enriched by a semi-empirical equation proposed in a previous study. Therefore, the present study discussed (i) instrumentation by strain gauge and DIC as well as data acquisition and post-processing of the data, accounting for strain gradients on the concrete surface and embedded reinforcement; (ii) input dataset generation for training machine learning regression models approaching hybrid learning; (iii) data regression to predict strains in embedded reinforcement based on monitored surface deformations; and (iv) the results, validation, and post-processing responses to make the method more robust. Finally, the developed model was evaluated through numerous statistical performance measures. The results showed that the proposed method can reasonably predict strain in embedded reinforcement, providing an innovative type of sensing application with highly improved performance.

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.

Fibre reinforced polymers may be suggested as a solution to one of the major durability problems with reinforced concrete; the corrosion of the internal steel reinforcement. The durability of fibre reinforced polymers, FRP, e.g. towards chlorides, has the potential of saving extensive maintenance costs and avoiding resulting inconveniences for the public. This paper presents a pilot study with the use of glass fibre reinforced polymers, GFRP, as internal reinforcement for concrete structures. In the comparison between major international design guidelines, similar design procedures for ultimate limit state, ULS, but different design procedures for serviceability limit state, SLS, are used. Four T-beams, of which two were reinforced with GFRP rebars and the other two with steel rebars, were tested to failure in 4-point bending. Optical strain measuring equipment was used for the measurement of crack propagation. A comparison between predictions of failure loads, deflections and crack widths based on the design guidelines and the experimental results has been performed. For both GFRP reinforced beams different from predicted failure modes occurred, but at loads almost at the predicted level or higher. Extensive deflections and crack propagation gave clear warnings before failure. The behavior of the GFRP reinforced beams in SLS was reliable predicted by the design guidelines for FRP with the same accuracy as for the reinforced concrete beams. The SLS predictions of crack widths and deflections showed increasing overestimation for increasing reinforcement ratios. SLS criteria have shown to be governing in the design for the GFRP reinforced members.