The paper presents a risk assessment model, developed in the project oVERFLOw and further implemented in the project CROSScade, for determining the direct and indirect impacts of flooding hazards. As a consequence of flooding, transport infrastructure and flood protection systems can be significantly damaged and cause cascading effects on other infrastructure. To achieve flood resilient infrastructure, it is necessary to assess the vulnerability of critical assets in the affected area. The model uses novel vulnerability assessment methods for embankments and bridges exposed to different flood hazard scenarios allowing the asset owners to understand risk and performance of their infrastructure. Scarce financial resources are allocated on the critical assets allowing significant cost savings and avoiding the waste of non-renewable resources in strengthening large sections which have sufficient resilience. The consequence analysis is based on an improved quantification model for direct and indirect impacts of different flood hazard scenarios used for risk mapping of the affected area.@en

Understanding how soil varies spatially is necessary in order to accurately quantify the reliability of geotechnical infrastructure. For long linear infrastructure such as flood embankments, incorporating vertical and horizontal scales of fluctuation can have a significant impact on stability assessments. This paper presents preliminary results and discussion from a field test designed to determine the vertical and horizontal scales of fluctuation of a Croatian flood embankment. A series of 15 CPTUs were carried out over a 200m length of the embankment with a Multi-channel Analysis of Surface Waves (MASW) survey done on the same section. CPT spacing was designed specifically to determine horizontal variation with multiple CPTs carried out in close proximity to each other. There was significant variation in soil stratigraphy over the embankment section with pockets of increased strength and stiffness showing up in the MASW and CPT results. This paper discusses dealing with horizontal correlation in challenging deposits and presents initial findings from the underlying sand layer.

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This paper offers a solution to overcome time-consuming numerical analysis for the evaluation of the impact of tunnel construction in a complex karst environment by implementing Monte Carlo Simulation (MCS) using a neural network (NN) tool. The rock mass is described using three parameters: Geological Strength Index, the uniaxial compression strength of the intact rock, and the Hoek–Brown parameter for the intact rock m_i . By using their probabilistic distribution as an input, a developed neural network NetTUNN produces probabilistic distributions of tunnel crown displacement, rock bolt axial load, and shotcrete uniaxial compression stress. A full MCS is then applied on these NetTUNN outputs to determine the reliability index and probability of failure for the relevant limit states. To demonstrate the potential of NN in tunnel design, a case study of Tunnel Pecine in Croatia is used, where the NetTUNN-assisted MCS assessment served as a benchmark to evaluate approximate reliability assessment techniques. It was shown that the developed NN can be used as an accurate surrogate model for determination of probabilistic distributions of tunnel design parameters. Further, it was shown that approximate reliability assessment techniques generally overestimate the reliability index and underestimate the probability of failure when compared to the NetTUNN-assisted MCS.

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The continuous monitoring of long-term performance of tunnels constructed in soft rock masses shows that the rock mass deformations continue after construction, albeit at a rate that reduces with time. This is in contrast with NATM postulates which assume deformation stabilizes shortly after tunnel construction. This paper proposes the prediction of long-term vertical settlement performance of a tunnel in soft rock mass, through the inclusion of a Burger’s creep viscous-plastic constitutive law to model post-construction deformations. To overcome issues related to the complex characterization of this constitutive model, a neural network NetRHEO is developed and trained on a numerically obtained dataset. A particle swarm algorithm is then employed to estimate the most probable rheological parameter set, by utilizing the long-term in-situ monitoring data from several observation points on a real tunnel. The paper demonstrates the potential of the proposed methodology, using displacement measurements of two adjacent tunnels in karstic rock mass in Croatia. The complex interaction of a railway tunnel Brajdica and a road tunnel Pećine, conditioned by the character of the surrounding rock mass as well by the chronology of their construction, was evaluated to predict the future behavior of these tunnels.@en

Knowledge of the fines content is necessary for all soil classification systems and an important factor in the evaluation of soil strength in liquefaction and seismic settlement analysis. This paper presents the application of cone penetration test, CPT data for estimating the soil fines content. The correlation can be used either as a first estimate of fines content (for example in the offshore environment) or to provide statistical information on the variation of fines content within a given area of interest (e.g. for a regional liquefaction study). The paper shows how field and laboratory test data were used with a neural network to correlate the CPT results and the fines content. Data from five site investigation locations across Northern Croatia were utilised. Verification of the approach is performed using field and lab test data from the Veliki vrh landslide.

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In this paper we present issues related to the performance of immersed tube tunnels in the Netherlands. A range of issues are experienced for the ageing structures including long-term settlement at tunnel joints and consequent leakage. Because of the age of these structures some important aspects are often unknown thus creating uncertainty regarding remediation measures. Having discussed general issues the paper presents a case study of the Kil Tunnel which has experienced relatively large settlements over the past 40 years. In this case a lack of geotechnical engineering information for the soils below the tunnel was identified. A geophysical survey was undertaken and this provided ley insights into the ground conditions at the tunnel site.@en

In the current economic climate, it is crucial to optimize the use of all resources regarding railway infrastructure maintenance. In this paper, a multi-attribute decision support framework is applied to categorize railway embankments in order to prioritize maintenance activities. The paper describes a methodology to first determine the current condition of embankments using a combination of ground penetrating radar (GPR) surveys, visual inspection, and historical data about maintenance activities. These attributes are then used for the development of a multi-attribute utility theory model, which can be used as a support for decision making process for maintenance planning. The methodology is demonstrated for the categorization of 181 km of railway embankments in Croatia.

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The majority of the railway infrastructure in Croatia is over one hundred years old. In common with many other EU member states, a lack of investment in maintenance and renewal projects in over the last 30 years has resulted in generally poor track conditions. As a result traffic speeds are often restricted with some important operating with speeds limited up to only 20 km/h. This work describes a joint initiative between researchers and infrastructure managers (IM's) to revitalize risk assessment associated on ageing infrastructure, with the aim of increasing safety and reducing the costs of remediation. In order to achieve this, a new methodology for assessment of railway condition is developed. In this paper the use of phased investigation involving electromagnetic ground penetrating radar (GPR), seismic refraction, drones and in-situ geotechnical investigation to determine parameters affecting the track performance are presented. The features considered include ballast fouling, anomalies in railway embankments (including burrows), boundaries between layers, substructure condition, the water content of the soil, the slope geometry and drainage condition. The work constitutes the first step in a Decision Support Framework for IM's, being developed through the Horizon 2020 Project Destination Rail which will help to identify potential hot-spots on the rail network. By early identification of these locations low-cost remediation can be applied and thus costs can be reduced and failures avoided. In this paper the use of custom-made cart which allows acquisition of data along the three axes of railway rail cross-section and an innovative interpretation methodology is described. Based on GPR data, visual assessment and on photogrammetry images made using an unmanned aerial vehicle (UAV), a categorization of critical infrastructure data is collected to a quantitative risk assessment procedure. This provides a basis for preliminary design solutions as well as for establishment of detailed programme of investigation works and monitoring on sections where it was shown as necessary. A next step is implementation of this methodology into geographic information system (GIS) which would additionally fulfil the needs of decision-makers in railway sector.

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