The appearance of ground frost is of vital importance in construction and maintenance of roads in cold climates. Frost often causes ground heave and subsequent road damage, which must be taken into account in designing the road structure. Frost depth, pavement temperature, and freezing/thawing cycles are also important for estimating the frequency of road maintenance and treatment. Various analytical, numerical, and empirical models have been developed to estimate the surface temperature of the pavement and to model the heat flow in the underlying layers. The pavement surface experiences a variety of intricate nonlinear heat transfer mechanisms during winter, making it challenging to accurately model the surface boundary. Dynamic variation of parameters such as cloud cover and traffic density during the modeling period introduces additional complexity. To address this challenge, we have established an experimental setup in Luleå, Sweden, to measure pavement profile temperatures during the winter season. Additionally, we have developed a Finite Difference Model that utilizes local weather data including dynamic cloud cover, and which also takes traffic into account. The experimental and simulation findings demonstrate how the impact of surface temperature fluctuations diminishes and, more or less, vanishes for depths more than 55 [cm] below the pavement surface. The Finite Difference Model presented in this study exhibits the ability to forecast the pavement profile temperatures, including the surface temperature based on weather conditions, with acceptable precision for at least 3 days. As a consequence, a reasonable assessment of pavement layer conditions appears feasible based on local weather conditions, and the model can serve as a useful tool for planning road maintenance and construction in cold regions.

This chapter explores how geospatial tools such as remote sensing and Geographic Information Systems (GIS) can help handle flash flood risks. Flash floods, known for their sudden and intense nature, require precise monitoring and rapid responses. Remote sensing uses satellite or drone-observed data collected using optical, Radar, and LiDAR sensors to track precipitation patterns, water levels, and dynamic changes in terrain, enabling timely flood detection and assessment. Integration of remote sensing data with GIS techniques enhances spatial analysis, allowing for the creation of flood maps, hydrological models, and real-time monitoring systems which help in emergency response coordination. Recent advancements in flood monitoring using drone mapping have emerged as a game-changing tool in flash flood management. Drones equipped with advanced sensors provide high-resolution imagery and real-time data from hard-to-reach and hazardous areas, facilitate rapid damage assessment, aid in identifying at-risk locations, and support search and rescue operations. Combining drone-derived data with GIS layers results in more accurate 3D flood models, facilitating visualization and decision-making for emergency responders and decision-makers.

The chapter delves into the significance of predictive modeling in flash flood scenarios. Remote sensing data feeds into hydrological models, enabling the simulation of flash flood events and potential outcomes. The generated information helps in preparing for flash floods by identifying vulnerable areas, predicting flood extents, and evaluating potential impacts on infrastructure and communities. The resulting data can be integrated into decision support systems, offering actionable insights for issuing timely alerts, managing evacuations, and allocating resources efficiently. Additionally, the chapter explores the socioeconomic aspects of integrating geospatial techniques in flash flood management, emphasizing the role of collaboration among disaster management agencies, meteorologists, supply chain analysts, and local communities. The power of geospatial visualizations in communicating flood risks to the public is underscored promoting awareness, preparedness, and resilience. The authorities can better reduce the effects of flash floods on vulnerable regions and communities by integrating geospatial tools such as remote sensing, GIS, and drone mapping.

Tjäldjup, tjällyftningar och tjällossningsproblem är årligen återkomman­de fenomen, som inträffar när jordmaterial fryser och tinar. I Sverige är frysningen säsongsmässig, med relativt korta vintrar i söder och upp till sex månader långa i norra Sverige. Tjällossningen sker under våren och avslutas i norra Sverige först vid midsommartid och är inte sällan förknippad med att vägars bärförmåga kan påverkas negativt. Tjäldjupet ökar successivt under vintern med mer eller mindre ojämn tjällyftning som följd. Graden av tjällyftning beror av temperatur i uteluft, jordmaterial och tillgång på vatten. Just ojämnhet i tjällyftningar för vägar och järnvägar är ett problem då det påverkar såväl komfort som säkerhet. Ojämna tjällyftningar behandlas för närvarande i tre olika doktorandprojekt vid LTU och dessa beskrivs här.

Frost heave is major problem for infrastructures build in cold regions. Frost heave occurs due to suction (negative pore water pressure) generated due to the freezing process close to the frost line, i.e., at the frozen fringe. To understand and predict these negative pore water pressures is a key factor to accurately calculate the segregation heave, i.e. heave related to the formation of ice lenses. Segregation heave is the major part of the total heave and also the most challenging to predict. Many attempts have been presented in literature where the generated suction during freezing is related to temperatures, temperature gradients, grain size of the freezing soil etc. Very few laboratory tests have been presented in which the actual suction is measured during the ice lens formation process and compared with theoretical estimations. One reason is that these measurements are challenging. This paper presents results from laboratory measurements of generated suction during freezing. Laboratory tests were conducted on a silty soil sample and suction was measured at the frozen fringe using small pore pressure transducers (PPT's). The samples were subjected to one-dimensional freezing from top to bottom in an open water system at a constant temperature gradient. Temperatures were measured at various points along the height of the soil sample while suction was measured at middle of the sample. Test results have shown that PPTs do not show pressure change in long-term static pressure test under sub-freezing temperature. For suction measurement at the frozen fringe, pore pressure readings should be measured at various points along the sample height.

Underground pipelines are an essential part of the transportation infrastructure. The structural deterioration of pipelines crossing railways and their subsequent failures are critical for society and industry resulting in direct and indirect costs for all the related stakeholders. Pipeline failures are complex processes, which are affected by many factors, both static (e.g., pipe material, size, age, and soil type) and dynamic (e.g., traffic load, pressure zone changes, and environmental impacts). These failures have serious impacts on public due to safety, disruption of traffic, inconvenience to society, environmental impacts and shortage of resources. Therefore, continuous and accurate condition assessment is critical for the effective management and maintenance of pipeline networks within transportation infrastructure. The aim of this study is to identify failure modes and consequences related to the crossing of pipelines in railway corridors. Expert opinion have been collected through two set of questionnaires which have been distributed to the 291 municipalities in the whole Sweden. The failure analysis revealed that pipe deformation has higher impact followed by pipe rupture at cross-section with railway infrastructure. For underground pipeline under railway infrastructure, aging and external load gets higher ranks among different potential failure causes to the pipeline.