In recent decades, many cities have become densely populated due to increased urbanization, and the transportation infrastructure system has been heavily used. The downtime of important parts of the infrastructure, such as tunnels and bridges, seriously affects the transportation system’s efficiency. For this reason, a safe and reliable infrastructure network is necessary for the economic growth and functionality of cities. At the same time, the infrastructure is ageing in many countries, and continuous inspection and maintenance are necessary. Nowadays, detailed inspections of large infrastructure are almost exclusively performed by inspectors on site, which is both time-consuming and subject to human errors. However, the recent technological advancements in computer vision, artificial intelligence (AI), and robotics have opened up the possibilities of automated inspections. Today, semiautomatic systems such as drones and other mobile mapping systems are available to collect data and reconstruct 3D digital models of infrastructure. This significantly decreases the downtime of the infrastructure, but both damage detection and assessments of the structural condition are still manually performed, with a high impact on the efficiency and accuracy of the procedure. Ongoing research has shown that deep-learning methods, especially convolutional neural networks (CNNs) combined with other image processing techniques, can automatically detect cracks on concrete surfaces and measure their metrics (e.g., length and width). However, these techniques are still under investigation. Additionally, to use these data for automatically assessing the structure, a clear link between the metrics of the cracks and the structural condition must be established. This paper presents a review of the damage of tunnel concrete lining that is detectable with optical instruments. Thereafter, state-of-the-art autonomous tunnel inspection methods are presented with a focus on innovative mobile mapping systems for optimizing data collection. Finally, the paper presents an in-depth review of how the risk associated with cracks is assessed today in concrete tunnel lining.

A common support measure for underground excavations in jointed rock masses to support loose blocks is to apply a thin shotcrete layer to the periphery of the excavation and systematically install rockbolts into the surrounding rock mass. In this support system, large blocks are carried by the rockbolts and small blocks are carried by the thin shotcrete layer. To verify the shotcrete layer’s load-bearing capacity and to stringently account for the large uncertainties incorporated in the variables involved in determining its capacity, analytical calculations in combination with reliability-based methods can be used. However, a lack of knowledge exists regarding the magnitude and uncertainty of shotcrete characteristics (thickness, adhesion, flexural tensile strength, residual flexural tensile strength, and compressive strength), making it difficult to apply reliability-based methods. A statistical quantification of these characteristics is therefore important to facilitate reliability-based methods in design and verification of shotcrete support. In this paper, we illustrate how shotcrete support against small loose blocks can be viewed as a correlated conditional structural system and how this system can be analyzed using reliability-based methods. In addition, we present a unique amount of data for the aforementioned variables, which are all incorporated in the design and verification of a shotcrete layer’s ability to sustain loads from small loose blocks. Based on the presented data, we statistically quantify and propose suitable probability distributions for each variable. Lastly, we illustrate how the proposed probability distributions can be used in the design process to calculate the probability of exceeding the shotcrete’s load-bearing capacity. Both the probabilistic quantification and the defined correlated conditional structural system along with the illustrative calculation example are followed by a discussion of their implications.

Fibre-reinforced shotcrete (sprayed concrete) is one of the major components in the support system for tunnels in hard rock. Several empirical design methodologies have been developed over the years due to the complexity and many uncertainties involved in rock support design. Therefore, this paper aims to highlight how the choice of design methodology and fibre type impacts the structural capacity of the lining and the emission of greenhouse gases (GHG). The paper starts with a review of different design methods. Then, an experimental campaign is presented in which the structural performance of shotcrete reinforced with various dosages of fibres made of steel, synthetic and basalt was compared. A case study is presented in which the permanent rock support is designed based on the presented design methods. Here, only the structural requirements were considered, and suitable dosages of fibres were selected based on the experimental results. The emission of GHG was calculated for all design options based on environmental product declarations for each fibre type. The result in this paper indicates that synthetic fibres have the greatest potential to lower the emissions of GHG in the design phase. Moreover, the choice of design method has a significant impact on the required dosage of fibres.

The infrastructure is in many countries aging and continuous maintenance is required to ensure the safety of the structures. For concrete structures, cracks are a part of the structure's life cycle. However, assessing the structural impact of cracks in reinforced concrete is a complex task. The purpose of this paper is to present a dataset that can be used to verify and compare the results of the measured crack propagation in concrete with the well-known Digital Image Correlation (DIC) technique and with Crack Monitoring from Motion (CMfM), a novel photogrammetric algorithm that enables high accurate measurements with a non-fixed camera. Moreover, the data can be used to investigate how existing cracks in reinforced concrete could be implemented in a numerical model.

Although the failure of a concrete dam is a complex and highly dynamic process, the current safety assessments of concrete gravity and buttress dams rely on a simplified 2D stability analysis, which neglects the load redistribution due to 3D monolith interactions and the valley shape. In addition, the estimation of breach parameters in concrete dams is based on assumptions rather than analyses, and better prediction methods are needed. Model tests have been conducted to increase the understanding of the failure behavior of concrete dams. A scale model buttress dam, with a scale of 1:15, consisting of 5 monoliths that were 1.2 m in height and 4 m in width, was constructed and loaded to failure using water pressure. The model dam had detachable abutment supports and shear keys to permit variations in the 3D behavior. The results showed that the shear transfer was large between the monoliths and that the failure of a single dam monolith is unlikely. A greater lateral restraint gives not only a higher failure load but also a better indication of impending failure. These findings suggest that the entire dam, including its boundary conditions, should be considered during a stability assessment. The results also suggest that the common assumption in dam safety codes that a single monolith fails during flooding analysis is not conservative. The dataset obtained provides a foundation for the future development of dam-monitoring alarm limits and for predictive models of dam-breaching processes.