04279nam 2200613 450 991046286710332120200520144314.0(CKB)2670000000281002(EBL)4388511(SSID)ssj0001054384(PQKBManifestationID)11588473(PQKBTitleCode)TC0001054384(PQKBWorkID)11126340(PQKB)11038898(MiAaPQ)EBC4388511(Au-PeEL)EBL4388511(CaPaEBR)ebr10621766(OCoLC)847727849(EXLCZ)99267000000028100220160220d2007 uy| 0engur|n|---|||||txtccrPrediction of accidental actions likely to occur on building structures an approach based on stochastic simulation /Egidijus R. Vaidogas ; Vilnius Gediminas Technical UniversityVilnius :VGTU leidykla TECHNIKA,2007.1 online resource (249 p.)Description based upon print version of record.9955-28-140-5 Includes bibliographical references and index.Contents; Preface; Part I. The problem of accidental actions; 1. Current practice of description and prediction; 1.1 Industrial accidents & accidental actions; 1.2 Accidental actions: definition and classification; 1.3 Current practice of deterministic modelling accidental actions; 1.4 Knowledge available for selecting action models; 1.5 Principal probabilistic model of accidental action; 1.6 Classical statistical approach to modelling accidental actions; 1.7 Conclusion: the need of risk analysis for predicting accidental actions2. A brief overview of the situation of data related to accidental actions2.1 The need for diverse information; 2.2 Accident data; 2.3 Data on human reliability; 2.4 Concluding remarks; Part II. Prediction by means of stochastic accident simulation; 3. Classical bayesien approach to predicting accidental actions; 3.1 Introduction; 3.2 Form of action model; 3.3 Selection of action model; 3.4 Case study; 3.5 Expert judgment in Bayesian predicting accidental actions; 3.6 How to apply classical Bayesian action models to damage assessment?3.7 Conclusion: pros and cons of the classical Bayesian approach4. Predictive, epistemic approach to forecasting accidental actions; 4.1 Introduction; 4.2 Principles of application to accidental actions; 4.3 Form of action model; 4.4 Specifying the action model by a stochastic accident simulation; 4.5 Case study; 4.6 Quantifying epistemic uncertainties related to problem input; 4.7 Application to damage assessment; 4.8 Conclusion: pros and cons of the predictive epistemic approach; Part III. Utilising direct data on accidental actions; 5. Resampling direct data within frequentist's approach5.1 Introduction5.2 Risk of damage due to accidental action; 5.3 Damage assessment: frequentist's approach or Bayesian updating?; 5.4 Use of bootstrap resampling to estimating damage probabilities; 5.5 Case study; 5.6 Concluding remarks; 6. Bayesian resampling of direct data on an accidental action; 6.1 Introduction; 6.2 Basic ideas; 6.3 Knowledge available for estimating damage probability; 6.4 Application of Bayesian bootstrap; 6.5 Case study; 6.6 Concluding remarks; Postscript; Appendix A. Abbreviations; Appendix B. Novation; Appendix C. Compiuter programsAppendix D. Selected bibliographyReferences; IndexStructural engineeringComputer simulationStructural engineeringData processingStochastic analysisSimulation methodsElectronic books.Structural engineeringComputer simulation.Structural engineeringData processing.Stochastic analysis.Simulation methods.Vaidogas Egidijus Rytas937818Vilniaus Gedimino technikos universitetas,MiAaPQMiAaPQMiAaPQBOOK9910462867103321Prediction of accidental actions likely to occur on building structures2112513UNINA