To identify the unknown values of the parameters of Burger’s constitutive law, commonly used for the evaluation of the creep behavior of the soft soils, this paper demonstrates a procedure relying on the data obtained from multiple sensors, where each sensor is used to its best advantage. The geophysical, geotechnical, and unmanned aerial vehicle data are used for the development of a numerical model whose results feed into the custom-architecture neural network, which then provides information about on the complex relationships between the creep characteristics and soil displacements. By utilizing InSAR and GPS monitoring data, particle swarm algorithm identifies the most probable set of Burger’s creep parameters, eventually providing a reliable estimation of the long-term behavior of soft soils. The validation of methodology is conducted for the Oostmolendijk embankment in the Netherlands, constructed on the soft clay and peat layers. The validation results show that the application of the proposed methodology, which relies on multisensor data, can overcome the high cost and long duration issues of laboratory tests for the determination of the creep parameters and can provide reliable estimates of the long-term behavior of geotechnical structures constructed on soft soils@en

Because of the deltaic nature of the Netherlands, deep soft soil deposits are widespread. Due to the population density exploitation of underground space is vital for commercial developments and transport networks. Piles are used as primary support elements in deep excavations, cut and cover tunnels, quay walls, flood defences and to provide uplift resistance to the base of tunnels and basements. This paper examines empirical correlations linking the Cone Penetration Test (CPT) end resistance qc and the resistance of deep foundations in sand. It is found that correlations between qc and pile end resistance are independent of pile diameter. However, the impact of installation method, residual load, plugging and sand creep should be considered. In the case of shaft resistance, constant correlation factors between qc and average shaft resistance are possible for non-displacement piles. For the case of displacement piles, correlations that include the effects of friction fatigue and pile plugging during installation are recommended.@en

Tunnels are a vital link in transport networks which represent a significant investment in all life cycle phases from planning, investigation works, construction and operation. Decisions through the whole life cycle of a tunnel should be based on solid facts and reliable data especially in the context of considerable impacts on both the environment and society. The calculation of total life time costs for different design alternatives, maintenance options and societal impacts can be used to compare different technical solutions and select the optimal design and maintenance alternative. Generally problems related to tunnel degradation can be divided into those caused by external pressure and those caused by the deterioration of materials. These problems are gradually increased through all life cycle stages of a concrete structure such as a tunnel, therefore decisions about the timing and the type of maintenance should be based on degradation prediction models and monitoring of the structure performance or degradation processes. Uncertainties in the decision making process can be decreased by using information from monitoring which are used to establish triggering thresholds for the structure passing certain performance levels. In this paper the use of monitored tunnel deformations in a life cycle management model for a tunnel is presented. The monitoring data gives information about tunnel long term deformations that are used in the decision making process, in order to prevent occurrence of tunnel damage and consequently large maintenance costs.@en

The 189.50 m long Bobova tunnel constructed in 2005 is located on the D404 state highway and passes under a part of the Vežica – Sušak town area in Rijeka. The overburden above the tunnel pipe is between 2 m to 18 m thick. The rock mass along the tunnel route is made up of karst deposits (transient carbonate breccia, dolomite and limestone interlinked with rudist limestones). Extensive geotechnical instrumentation was installed in the tunnel by researchers at the faculty of Civil Engineering at the University of Zagreb. This consisted of: measurements in the tunnel area from the ground surface using inclinometers for horizontal, and sliding micrometers for vertical soil displacements, measuring displacements around underground openings using sliding micrometers supplemented with scanning of the tunnel interior using laser scanners, and measuring the stress and deformations in elements of support complex by pressure cells and measuring anchors. Data collected during construction and in the twelve years in which the three-lane highway tunnel has been operational, presented in this paper reveal that deformations and stresses in Bobova tunnel have continued to increase with time. The possible role of the shotcrete lining, rock creep and rock-bolt corrosion in the ongoing deformations are discussed. The application of continuous monitoring data from instruments as inputs for numerical model training using a machine learning model, with the objective to improve the predictions of existing probabilistic failure models is considered. The ultimate aim of this work is to develop improved predictive maintenance plans.@en

Since more than 95% of the existing European railway network is older than 100 years, the ageing has unavoidably caused its gradual degradation and there is currently a strong need for risk assessment associated with this type of infrastructure. To increase the safety aspects, as well as to reduce the cost of remediation, railway infrastructure managers need to have more advanced tools on their disposal, since they currently make safety critical investment decisions based on poor data and an over-reliance on visual assessment. As a consequence their estimates of risk are therefore highly questionable and largescale failures happen with increasing regularity. The paper presents the efforts conducted within the framework of some relevant scientific-research projects funded by the Horizon 2020 programme, whose overall goal is to implement and to further develop state-of-the-art techniques and tools in field of railway infrastructure safety.@en

The evaluation of soil parameters for design is best undertaken through comprehensive laboratory test programmes. However, due to sampling difficulty, time and cost constraints correlations between in-situ tests and physical-mechanical properties of soils are routinely applied in practice. This paper presents data collected from five sites in Northern Croatia at which Cone Penetration Tests (CPT) and comprehensive laboratory test data was available. One of the advantages of using CPT data in preference to other types of in-situ tests for establishing correlations, is the large volume of high-quality data available at each probe location allows for the application of advanced statistical approaches. In this paper, the use of neural networks in developing such correlations is demonstrated. Using a database of 216 data pairs, obtained from the five sites, a correlation between CPT qc and soil unit weight is established. A validation exercise was performed in which the correlation was tested against data from the recent Veliki vrh landslide that occurred in the same geographical region as the database sites. In addition, by using the soil behaviour type index, Ic, normalised cone tip resistance, Qtn, and normalised sleeve friction, Fr, the results can be compared to correlations developed for soils from geotechnical diverse regions to check for consistency in the derived correlations.@en

European rail infrastructure managers (IMs) are managing aging rail infrastructure with 95% of the network having been built before 1914. EU transport policy provides the challenge to IMs to increase the productivity of existing rail networks, prioritise renewal and optimise new sections to reduce bottlenecks, increase productivity and achieve a switch from freight transport by road to rail. This needs to be achieved at a time when budgets are restricted whilst improving customer satisfaction and dealing with challenges from natural hazards and extreme weather events which are affecting all of Europe. In order to deal effectively with this grand challenge, Europe will need to develop methods to manage its rail infrastructure across the single European railway area. Whilst decision support tools are widely applied across, these systems tend to concentrate on only one asset and inherently suffer from the several limitations. In this paper the European H2020 project Destination Rail that focuses on the development of decision support tool for rail infrastructure managers is presented. Within DESTination RAIL the aim is to provide solutions for a number of problems faced by EU infrastructure managers, such as assessment of existing assets, use of existing databases controlled by an information management system, risk assessment, maintenance and construction techniques for treating rail infrastructure including tracks, earthworks and structures, whole life cycle assessment and impact on the traffic flow. Each of these separate streams are incorporated into the Decision Support Tool which will be the primary exploitable deliverable from the project, and demonstrated on several railway projects across the European network.@en