Modeling Extreme Traffic Loading On Bridges Using Kernel Density Estimators

Kernel density estimators are a non-parametric method of estimating the probability density function of sample data. In this paper, the method is applied to find characteristic maximum daily truck weights on highway bridges. The results are then compared with the conventional approac

Portable Bridge WIM Data Collection Strategy for Secondary Roads

A common method of collecting traffic loading data across a large road network is to use a network of permanent pavement-based WIM systems. An alternative is to use one or more portable Bridge Weigh-In-Motion systems which are moved periodically between bridges on the network. To make optimum use of such a system, a suitable data collection strategy is needed to choose locations for the system. This paper describes a number of possible strategies which the authors have investigated for the National Roads Authority in Ireland. The different strategies are examined and their advantages and disadvantages compared. Their effectiveness at detecting a heavy loading event is also investigated and the preferred approach is identified

Estimating Characteristic Bridge Loads On A Non-Primary Road Network

When collecting truck loading data on a primary road network a common approach is to install a large network of permanent pavement-based Weigh-In-Motion systems. An alternative to this approach would be to use one or more portable Bridge Weigh-In-Motion systems which could be moved between bridges at regular intervals to determine the traffic loading throughout the network. A data collection strategy is needed to put such a system to best use. This paper details the data collection strategies which were examined for the National Roads Authority in Ireland. The use of urban economic concepts including Central Place Theory are discussed as methods for analysing which roads are expected to experience the greatest truck loading

Clinical spectrum and features of activated phosphoinositide 3-kinase δ syndrome: A large patient cohort study.

BACKGROUND: Activated phosphoinositide 3-kinase δ syndrome (APDS) is a recently described combined immunodeficiency resulting from gain-of-function mutations in PIK3CD, the gene encoding the catalytic subunit of phosphoinositide 3-kinase δ (PI3Kδ). OBJECTIVE: We sought to review the clinical, immunologic, histopathologic, and radiologic features of APDS in a large genetically defined international cohort. METHODS: We applied a clinical questionnaire and performed review of medical notes, radiology, histopathology, and laboratory investigations of 53 patients with APDS. RESULTS: Recurrent sinopulmonary infections (98%) and nonneoplastic lymphoproliferation (75%) were common, often from childhood. Other significant complications included herpesvirus infections (49%), autoinflammatory disease (34%), and lymphoma (13%). Unexpectedly, neurodevelopmental delay occurred in 19% of the cohort, suggesting a role for PI3Kδ in the central nervous system; consistent with this, PI3Kδ is broadly expressed in the developing murine central nervous system. Thoracic imaging revealed high rates of mosaic attenuation (90%) and bronchiectasis (60%). Increased IgM levels (78%), IgG deficiency (43%), and CD4 lymphopenia (84%) were significant immunologic features. No immunologic marker reliably predicted clinical severity, which ranged from asymptomatic to death in early childhood. The majority of patients received immunoglobulin replacement and antibiotic prophylaxis, and 5 patients underwent hematopoietic stem cell transplantation. Five patients died from complications of APDS. CONCLUSION: APDS is a combined immunodeficiency with multiple clinical manifestations, many with incomplete penetrance and others with variable expressivity. The severity of complications in some patients supports consideration of hematopoietic stem cell transplantation for severe childhood disease. Clinical trials of selective PI3Kδ inhibitors offer new prospects for APDS treatment.T.C. is supported by National Children’s Research Centre, Our Lady’s Children’s Hospital Crumlin, Dublin, Ireland. A.C. has a Wellcome Trust Postdoctoral Training Fellowship for Clinicians (103413/Z/13/Z). K.O. is supported by funding from BBSRC, MRC, Wellcome Trust and GSK. R.D. and D.S.K are funded by National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre, Cambridge, UK. C.S. and S.E. are supported by the German Federal Ministry of Education and Research (BMBF 01 EO 0803 grant to the Center of Chronic immunodeficiency and BMBF 01GM1111B grant to the PID-NET initiative). S.N.F is supported in part by the Southampton UK National Institute for Health Research (NIHR) Wellcome Trust Clinical Research Facility and NIHR Respiratory Biomedical Research Unit. M.A.A.I. is funded by NHS Innovation London and King’s College Hospital Charitable Trust. A.F., S.L., A.D., F.R-L and S.K. are supported by the European Union’s 7th RTD Framework Programme (ERC advanced grant PID-IMMUNE contract 249816) and a government grant managed by the French Agence Nationale de la Recherche as part of the "Investments for the Future" program (ANR-10-IAHU-01). S.L. is supported by the Agence Nationale de la Recherche (ANR) (ANR-14-CE14-0028-01), the Foundation ARC pour la Recherche sur le Cancer (France), the Rare Diseases Foundation (France) and François Aupetit Association (France). S.L. is a senior scientist and S.K is a researcher at the Centre National de la Recherche Scientifique-CNRS (France). A.D. and S.K. are supported by the “Institut National de la Santé et de la Recherche Médicale". S.K. also supported by the Fondation pour la Recherche Médicale (grant number: ING20130526624), la Ligue Contre le Cancer (Comité de Paris) and the Centre de Référence Déficits Immunitaires Héréditaires (CEREDIH). S.O.B is supported by the Higher Education Funding Council for England. B.V. is supported by the UK Biotechnology and Biological Sciences Research Council [BB/I007806/1], Cancer Research UK [C23338/A15965) and the National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre. B.V. is consultant to Karus Therapeutics (Oxford, UK). S.N. is a Wellcome Trust Senior Research Fellow in Basic Biomedical Science (095198/Z/10/Z). S.N. is also supported by the European Research Council Starting grant 260477, the EU FP7 collaborative grant 261441 (PEVNET project) and the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre, UK. A.M.C. is funded by the Medical Research Council, British Lung Foundation, University of Sheffield and Cambridge NIHR-BRC. Research in A.M.C. laboratory has received non-commercial grant support from GSK, Novartis, and MedImmune.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.jaci.2016.06.02

Modelling Extreme Traffic Loading on Bridges Using Kernal Density Estimators

Innovations on Bridges and Soil-Bridge Interaction (IBSBI 2011), Athens, Greece, October 13-15, 2011Kernel density estimators are a non-parametric method of estimating the probability density function of sample data. In this paper, the method is applied to find characteristic maximum daily truck weights on highway bridges. The results are then compared with the conventional approach.Deposited by bulk impor

Traffic Load Effect Forecasting for Bridges

IABSE Conference on Structural Engineering: Providing Solutions to Global Challenges, Geneva, Switzerland, 23-25 September 2015Traffic flows, as well as truck weights, increase with time. This must be taken into account in order to accurately assess traffic loading on bridges. The Eurocode Load Model 1 is used for the design of new bridges but a scaled down version of the model can be used for the assessment of existing bridges. This scaling is usually done by applying α–factors to the load model. The effect of traffic growth on these α–factors is assessed in this paper. Weigh-in-motion data from the Netherlands is used as the basis for traffic models which simulate year-on-year growth of both traffic flow and truck weights. A time-varying generalised extreme value distribution is then used to calculate the characteristic load effects and determine the α–factors. The effect of different traffic growth rates on these α–factors is then examined. It is found that an increase in truck weights has the most influence on the α–factors but that increased flow also has a significant effect.National road administrations of Denmark, Germany, Ireland, Netherlands, UK and Sloveni