Adequate Capital and Stress Testing for Operational Risks

We describe how the notion of sequential correlations naturally leads to the quantification of operational risk. Our main point is that functional dependencies between mutually supportive processes give rise to non-trivial temporal correlations, which can lead to the occurrence of collective risk events in the form of bursts and avalanches of process failures, and crashes of process networks. We show how the adequate capital for operational risk can be calculated via a stochastic dynamics defined on a topological network of interacting processes. One of the main virtues of the present model is the suitability for capital allocation and stress testing of operational risks

Testing of Concrete Abrasion Resistance in Hydraulic Structures on the Lower Sava River

The paper deals with the issues of resistance of concrete linings to long-term abrasion loading caused by waterborne particles, particularly for the proposed hydro power plants on the Sava River in Slovenia. The main purpose of the research work was to define the possibility of forecasting the process of concrete lining wear on the Sava River dam structures based on the standard procedures of abrasion resistance testing. Abrasion resistance of concrete has been researched in accordance with the standard ASTM C 1138 and Böhme (DIN 52108) methods. The research work was based on a comparison between laboratory results and measurements of abrasion resistance of concrete under natural conditions by performing test plots in the stilling basin of the Vrhovo HPP. Concrete composites with different mechanical properties have been analysed within the research programme. The analysis showed a qualitative similarity of the level of concrete abrasion between laboratory simulations and measurements in the field, as well as suitability of the ASTM C 1138 laboratory method for the assessment of\ud abrasion resistance of concretes in the spillway of the HPP chain on the Lower Sava River

Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future

Implementing 5D BIM on construction projects: Contractor perspectives from the UK construction sector

This is an accepted manuscript of an article published by Emerald in Journal of Engineering, Design and Technology on 09/05/2020: https://doi.org/10.1108/JEDT-01-2020-0007 The accepted version of the publication may differ from the final published version.Purpose The purpose of this paper is to report on primary research findings that sought to investigate and analyse salient issues on the implementation of 5D building information modelling (BIM) from the UK contractors’ perspective. Previous research and efforts have predominantly focussed on the use of technologies for cost estimation and quantity takeoff within a more traditional-led procurement, with a paucity of research focussing on how 5D BIM could facilitate costing within contractor-led procurement. This study fills this current knowledge gap and enhances the understanding of the specific costing challenges faced by contractors in contractor-led projects, leading to the development of 5D framework for use in future projects. Design/methodology/approach To develop a fully detailed understanding of the challenges and issues being faced in this regard, a phenomenological, qualitative-based study was undertaken through interviews involving 21 participants from UK-wide construction organisations. A thematic data analytical process was applied to the data to derive key issues, and this was then used to inform the development of a 5D-BIM costing framework. Findings Multi-disciplinary findings reveal a range of issues faced by contractors when implementing 5D BIM. These exist at strategic, operational and technological levels which require addressing successful implementation of 5D BIM on contractor-led projects adhering to Level 2 BIM standards. These findings cut across the range of stakeholders on contractor-led projects. Ultimately, the findings suggest strong commitment and leadership from organisational management are required to facilitate cost savings and generate accurate cost information. Practical implications This study highlights key issues for any party seeking to effectively deploy 5D BIM on a contractor-led construction project. A considerable cultural shift towards automating and digitising cost functions virtually, stronger collaborative working relationship relative to costing in design development, construction practice, maintenance and operation is required. Originality/value By analysing findings from primary research data, the work concludes with the development of a 5D BIM costing framework to support contractor-led projects which can be implemented to ensure that 5D BIM is successfully implemented

The UK Benchmark Network - Designation, Evolution and Application

The UK has one of the densest gauging station networks in the world – a necessary response to its diversity in terms of climate, geology, land use and patterns of water utilisation. This diversity and, particularly, the compelling impact of artificial influences on natural flow regimes across most of the country, implies a considerable challenge in identifying, interpreting and indexing changes in river flow regimes. Quantifying and interpreting trends in river flows – in particular separating climate-driven changes from those resulting from other driving mechanisms – is a necessary pre-requisite to the development of improved river and water management strategies. It is also a primary strategic objective of many national and international river flow monitoring programmes. This paper charts the development of the UK Benchmark Network through its initial promotion phase – involving key institutional partners in both the hydrometric data acquisition and user communities – through to its exploitation across a wide a range of policy, scientific and engineering design applications. Particular consideration is given to the criteria used to appraise and select candidate catchments and gauging stations. Spatial characterisations (particularly physiographic, geological and land use) are used to determine the representativeness of individual candidate catchments and hydrometric performance (in the extreme flow ranges especially), together with record length, is of primary importance in relation to gauging station selection. Indexing the degree to which artificial influences disturb the natural flow regime is also a necessary pre-requisite for selection across much of the UK. Descriptions are given of a number of network and data review mechanisms developed to maximize the utility of the Benchmark Network and the burgeoning range of applications which have capitalized on it – embracing both national and international monitoring programmes. The review finishes with an overview of the strategic benefits deriving from the operation of the Benchmark Network and examines some of the enduring issues which require further work – including the continuing focus on operationally driven gauging activities; meeting the more stringent data demands of the Benchmark Network, and the need for further integration of catchment monitoring activities – embracing a wider range of hydrometeorogical variables

Virtualisation of the test environment for signalling

ERTMS is a well-known, well-performing technology applied all over the world but it still lacks flexibility when it comes to authorisation and certification procedures. The key of its success in the future lies as much in cost reduction as in simplification of placing in service procedures. This holds true for the implementation of a new subsystem and even more so for new software releases related to subsystems already in service. Currently the placing in service process of ETCS components and subsystems requires a large amount of tests due to the complexity of the signalling systems and the different engineering rules applied. The S2R Multi-Annual Action Plan states that the effort and time consumption of these onsite tests are at least 30% for any particular project. VITE research project (VIrtualisation of the Test Environment) aims at reducing these onsite tests to a minimum while ensuring that laboratory tests can serve as evidence for valid system behaviour and are accepted by all stakeholders involved in the placing in service process. This paper presents the first VITE results

Marine baseline and monitoring strategies for Carbon Dioxide Capture and Storage (CCS)

The QICS controlled release experiment demonstrates that leaks of carbon dioxide (CO2) gas can be detected by monitoring acoustic, geochemical and biological parameters within a given marine system. However the natural complexity and variability of marine system responses to (artificial) leakage strongly suggests that there are no absolute indicators of leakage or impact that can unequivocally and universally be used for all potential future storage sites. We suggest a multivariate, hierarchical approach to monitoring, escalating from anomaly detection to attribution, quantification and then impact assessment, as required. Given the spatial heterogeneity of many marine ecosystems it is essential that environmental monitoring programmes are supported by a temporally (tidal, seasonal and annual) and spatially resolved baseline of data from which changes can be accurately identified. In this paper we outline and discuss the options for monitoring methodologies and identify the components of an appropriate baseline survey

Defining and characterising structural uncertainty in decision analytic models

An inappropriate structure for a decision analytic model can potentially invalidate estimates of cost-effectiveness and estimates of the value of further research. However, there are often a number of alternative and credible structural assumptions which can be made. Although it is common practice to acknowledge potential limitations in model structure, there is a lack of clarity about methods to characterize the uncertainty surrounding alternative structural assumptions and their contribution to decision uncertainty. A review of decision models commissioned by the NHS Health Technology Programme was undertaken to identify the types of model uncertainties described in the literature. A second review was undertaken to identify approaches to characterise these uncertainties. The assessment of structural uncertainty has received little attention in the health economics literature. A common method to characterise structural uncertainty is to compute results for each alternative model specification, and to present alternative results as scenario analyses. It is then left to decision maker to assess the credibility of the alternative structures in interpreting the range of results. The review of methods to explicitly characterise structural uncertainty identified two methods: 1) model averaging, where alternative models, with different specifications, are built, and their results averaged, using explicit prior distributions often based on expert opinion and 2) Model selection on the basis of prediction performance or goodness of fit. For a number of reasons these methods are neither appropriate nor desirable methods to characterize structural uncertainty in decision analytic models. When faced with a choice between multiple models, another method can be employed which allows structural uncertainty to be explicitly considered and does not ignore potentially relevant model structures. Uncertainty can be directly characterised (or parameterised) in the model itself. This method is analogous to model averaging on individual or sets of model inputs, but also allows the value of information associated with structural uncertainties to be resolved.