LEADER 04246nam 2200601 450 001 9910786313703321 005 20230721044401.0 035 $a(CKB)2670000000281002 035 $a(EBL)4388511 035 $a(SSID)ssj0001054384 035 $a(PQKBManifestationID)11588473 035 $a(PQKBTitleCode)TC0001054384 035 $a(PQKBWorkID)11126340 035 $a(PQKB)11038898 035 $a(MiAaPQ)EBC4388511 035 $a(Au-PeEL)EBL4388511 035 $a(CaPaEBR)ebr10621766 035 $a(OCoLC)847727849 035 $a(EXLCZ)992670000000281002 100 $a20160220d2007 uy| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aPrediction of accidental actions likely to occur on building structures $ean approach based on stochastic simulation /$fEgidijus R. Vaidogas ; Vilnius Gediminas Technical University 210 1$aVilnius :$cVGTU leidykla TECHNIKA,$d2007. 215 $a1 online resource (249 p.) 300 $aDescription based upon print version of record. 311 $a9955-28-140-5 320 $aIncludes bibliographical references and index. 327 $aContents; 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 actions 327 $a2. 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? 327 $a3.7 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 approach 327 $a5.1 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 programs 327 $aAppendix D. Selected bibliographyReferences; Index 606 $aStructural engineering$xComputer simulation 606 $aStructural engineering$xData processing 606 $aStochastic analysis 606 $aSimulation methods 615 0$aStructural engineering$xComputer simulation. 615 0$aStructural engineering$xData processing. 615 0$aStochastic analysis. 615 0$aSimulation methods. 700 $aVaidogas$b Egidijus Rytas$01478202 712 02$aVilniaus Gedimino technikos universitetas, 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910786313703321 996 $aPrediction of accidental actions likely to occur on building structures$93693847 997 $aUNINA