Since traditional inspection methods of structures are time-consuming and prone to human errors, many researchers have investigated the possibility of using various deep learning models to automate damage detection and, in particular, crack detection. However, deep learning models require a large amount of training data to predict reasonably accurate results. Creating a dataset with segmented cracks is time-consuming, and the aim of this paper is, therefore, to present a semi-automated labelling process of cracks. This has the potential to greatly decrease the time spent creating datasets.

Shotcrete is a widely used construction material known for its rapid application and high strength. It contains high amounts of cement, a source of greenhouse gas emissions. SCM can be used to reduce the environmental impact of shotcrete by replacing amounts of cement in the mix. This can affect the strength properties of shotcrete. A pilot study is set to investigate the effect of replacing cement with slag on compressive strength for the first 24 hours. Using the stud driving method and cast cubes, the study showed reduced compressive capacity for the first few hours.

Tunnels are essential for infrastructure and require regular visual inspections by trained operators to identify defects like cracks and water ingress. This process is time-consuming, prone to human error, and requires tunnel closures. The need for efficient inspection solutions has increased as tunnel networks expand and age. Recent advancements in mobile mapping systems equipped with geomatic sensors, such as cameras and LiDAR sensors, have significantly enhanced data collection. These systems allow for rapid data acquisition, reducing tunnel downtime and enabling the generation of digital twins for remote inspections, which improves knowledge transfer. However, damage detection remains manual even if deep learning methods have been extensively investigated to automate defect detection using high-resolution images collected with mobile mapping systems. This paper is part of the TACK-II project and focuses on mobile mapping systems, providing a detailed analysis of the parameters that influence their design for effective data collection in tunnels.

This dataset [1] contains results from structural testing of cast and sprayed fibre-reinforced shotcrete with steel, synthetic and basalt fibres. This includes testing residual flexural strength on beams according to EN 14488-3 [2] and the energy absorption of round determinate panels tested according to ASTM C1550 [3]. For all tests, the compressive strength was evaluated according to EN 12390-3 [4]. Four different fibre types were tested, and for each fibre type, three dosages were tested. Beams were produced by casting, while panels were produced by casting and spraying. This resulted in a total of 36 beams and 72 panels and cubes. Structural testing was performed at an accredited laboratory by certified personnel using standard equipment found in a commercial laboratory for structural testing. The dataset contains a summary of the standard parameters evaluated for each test method but also contains the raw data from the test machine for tests on beams on panels. One unique property of this dataset is the variation in fibre types, i.e. testing includes steel, synthetic and basalt fibres. The standard test parameters summary is useful for directly comparing and evaluating the structural performance for each fibre type and dosage. This could be used to get an indication of the required dosage of each fibre type to fulfil structural requirements. The raw data from the test machine is valuable for the numerical modelling of fibre-reinforced shotcrete. This data contains the relationship between external force and vertical displacement of the specimen and can be used to calibrate a material model for finite element simulations.

In today's hard rock tunnel construction, the most common support system consists of rock bolts and shotcrete linings. The support system is effective to build, and structural safety has empirically been established. However, the utilization rate of shotcrete linings is usually unknown as no method exists today that determines the type and magnitude of loads acting on the linings. This paper investigates the implementation of distributed optical fiber sensors (DOFS) as a promising solution for monitoring of local loads in shotcrete tunnel linings. This approach enables the identification of local loads, facilitating targeted inspections in areas with deviating measurements and allowing for more informed repair and maintenance decisions. In the study, two typical local load conditions in shotcrete linings were analysed using strain measurements from DOFS installed in experimental specimens designed to replicate sections of tunnel linings. The results revealed that the examined load conditions can be distinguished based on the measured strains. While the lining thickness had a significant effect on the peak load capacity, the roughness of the substrate influenced the strain distribution in linings subjected to bending. It was also shown that DOFS outside the loaded area could detect load-induced strains for shear loaded specimens at low load levels, but not for flexurally loaded specimens.

The long-term sustainability of shotcrete reinforced with alternative fibre materials, such as basalt and synthetic fibres, is an emerging field of study with significant implications for construction durability and performance. Current research focus is on developing a suitable test method for structural capacity that can be used in a laboratory environment to evaluate these materials under accelerated conditions, simulating long-term usage in various environmental contexts. One critical aspect is to understand how fibres made from synthetic or basalt materials will behave over extended periods, as existing data primarily pertains to traditional steel fibres. To address this gap, we are devising accelerated testing protocols that mimic the thermal and chemical stressors these materials might encounter over decades.

For tunnels in hard rock, fibre-reinforced shotcrete (sprayed concrete) is an essential part of the rock support system. Due to the complexity of estimating the load on the shotcrete, design philosophies and guidelines vary between countries. This also applies to fibre type, where some countries only allow fibres of a certain material. With this background, the suitability of using steel, synthetic and basalt fibres in shotcrete linings was studied in a recently finalized project. The aim was to evaluate the structural performance through experimental testing, investigate the environmental impact of the fibres and summarize the knowledge regarding the long-term performance of the fibres. The project has shown that all three fibres are suitable to use in tunnels from a structural perspective, but some questions regarding long-term performance still exist. From an environmental perspective, the choice of fibre type has, compared to shotcrete, a negligible effect on greenhouse gas emissions.

The adoption of 3D printing (3DP) in concrete construction offers a transformative approach to structural optimization, particularly in Sweden’s prefabrication industry. This study integrates physical testing and numerical simulations to assess the mechanical performance of 3D printed concrete (3DPC). Experimental results highlight the anisotropic behavior of printed structures, emphasizing the need for tailored print paths to enhance structural stability. Numerical simulations using continuum models further validate these findings, providing insights into failure mechanisms. The outcomes contribute to the development of optimized printing strategies for real-world applications, such as staircases and stormwater systems.