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.

The development of technologies associated with the fourth industrial revolution is rapid. Construction 4.0 represents the architecture, engineering, construction and operations industries exploration of new technologies, equivalent to Industry 4.0 for the manufacturing industry. These concepts address multiple perspectives besides the technological, such as management and processes. The purpose of this study was to investigate to what extent research regarding construction projects addresses information and communication, automatisation or industrialisation technologies. A scoping review was the method used to perform a quantitative analysis of over two thousand journal papers published from 2015 onwards. The results show that new technologies are addressed separately, while synergy studies are uncommon. Longitudinal analyses show that there was no significant increase in journal papers concerning new technologies from 2015 to 2019. Information and communication was the search criterion with the least number of papers found. The environmental perspective of new technologies was present but the least common from 2019 to 2020. Hence, this review shows that there is an extensive research gap regarding Construction 4.0 technologies in the context of construction projects. Studies regarding synergy and environmental effects of new technologies should increase to start the progress towards a successful entry into the fourth industrial revolution.

A failure of a massive concrete dam could cause catastrophic consequences. The purpose of monitoring is to detect anomalies and damage at an early stage to prevent failure. Data-based models for anomaly detection are based on the hypothesis that the behaviour of an undamaged dam will follow an expected pattern, and deviation from this pattern is an indication of damage. In this study, simulations were used to create time series for an undamaged dam and three different damage scenarios at three different locations in the dam body. Three common data-based models were used to predict a dams crest displacements, both on the generated artificial data and the corresponding measurements from the dam. Prediction bands for future measurements were created, and the ten time-series were used to test the ability to detect damage. All models could detect instantaneous damage but struggle to detect progressive damage; the Neural network outperforms the two regression models. The choice of the mathematically optimal threshold limit leads to a large number of false alerts. Requiring three consecutive values outside the threshold before an alert is issued, increases the possibility to receive an early alert compared to the standard approach where observations are classified individually.

The piano key (PK) weir is a cost-effective structure for flood discharge. Its typical layout comprises a rectangularly cranked crest in planform with up- and downstream overhangs. With the intention to enhance its hydraulic efficiency, the conventional weir is improved. The sloping floor of each key is modified with a downward semi-circle in the cross-section; each overhanging apex is thus assigned an elliptical crest. Thus, the developed crest length of the resulting weir becomes considerably extended. Experiments are performed to compare the hydraulic behaviors of the improved weir with a reference one. The models are 3D-printed to attain high manufacture precision. For the model dimensions chosen in the study, the developed crest is ~36% longer. The study demonstrates that the improvements in geometry lead to appreciably enhanced flow discharge capacity. Within the hydraulic range examined, the augment in flow discharge varies within a range from 30% to 53%. In terms of both discharge capacity and flow patterns, the improved weir clearly outperforms the conventional one. The elliptical overhang apexes noticeably extend the developed crest length. The streamlined upstream overhang without singularity and the lowered inlet key floor reduce the entrance energy loss and improve the inflow to the inlet key and the flow over the crest. The lowered floor also gives rise to extra water volume, which administers to the flow motion towards the crest. For the outlet key, its lowered floor facilitates the outflow and alleviates the liableness of local submergence at high discharges. If the footprint for spillway construction is limited or the increase in the reservoir water stage must be controlled, the use of a more effective PK weir for flood discharge has significant engineering 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.