Demonstrating the performance of a structural component is typically accomplished through calculations based on engineering models. However, advancements in material science have enabled the use of new materials in the construction industry. One example is fibre-reinforced concrete, which consists of concrete with short, discontinuous fibres dispersed throughout its volume to enhance performance under service limit states. The absence of conventional reinforcement can affect ductility and load capacity, making calculations alone insufficient to demonstrate the performance of these elements. Therefore, load-testing programs need to be developed. This paper reports and discusses the results of a full-scale test on a fibre-reinforced concrete multi-span slab-on-piles. The development of the test program, including the loading setup to achieve failure loads, is presented. The slab's performance is evaluated in terms of deflections and crack widths at various loading intervals corresponding to both service limit states and ultimate limit states.

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.

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.

Demonstrating the performance of a structural component can usually be done through calculations based on engineering models. However, the development in material science has made it possible to use new materials in the construction industry. One such example is the use of fibre-reinforced concrete defined as concrete containing short and discontinuous fibres dispersed throughout the concrete volume in order to improve the performance in service limit state. The lack of conventional reinforcement may affect the ductility and load capacity, and the calculations alone may not be sufficient to demonstrate the performance of these elements. Load-testing programs are therefore needed to be developed. The current paper reports and discuss the results of a full-scale test on a fibre-reinforced concrete multi-span slab-on-piles. The development of the test program including the loading setup to achieve the failure loads are presented. The performance of the slab is evaluated in terms of deflections and crack widths at certain loading intervals that corresponds with service limit state as well as ultimate limit state.

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.