A brief look at data on the reliability of sprinklers used in conventional buildings / Trumpa duomenų apie įprastiniuose pastatuose įrengtų sprinklerių patikimumą apžvalga

Failures of sprinklers to extinguish fires generate a basic need for the assessment and increase of reliability of these crucial safety systems. This in turn creates a demand for input data used for reliability assessment. Broadly speaking, data on sprinkler failures are available in large amounts and some countries have well-established systems of data collection and reporting. Data are accumulated in the sprinklered environments of conventional buildings and some industrial facilities. The compilation of data sets necessary for reliability assessment may face several problems: differences in definition and naming failure modes; differences in the failure of data reporting; the prevalence of a human factor among the causes of sprinkler failures in a conventional building; the influence of ageing, modifications and repairs on sprinkler reliability. The size of data sets can be limited by such factors as limited relevance of data collected in different sprinklered environments, differences in operation conditions and components, ageing of data collected in the past, the concealment of data and/or a high cost of data, poor documentation and explanation of data in available databases. Data on sprinkler component failure rates necessary for fault tree models can be extracted from generic databases. However, databases containing information on the failure rates of sprinkler-specific components do not seem to exist in literature or on the Internet. Scarce data on sprinkler failures can be utilised within the Bayesian format. The potentially critical issue of reliability dependence on sprinkler aging and other changes in time remains unsolved from the standpoint of both theoretical modelling and data collection. Santrauka Nereti sprinklerių atsakai, gesinant gaisrus, vercia vertinti sių kritinių saugos sistemų patikimumą. Dėl to reikia kaupti ir apdoroti duomenis, kurių reikia vertinant patikimumą. Duomenų apie sprinklerių atsakus yra daug. Kai kurios salys turi gerai sudarytas sprinklerių patikimumo duomenų rinkimo ir skelbimo sistemas. Duomenys renkami apie sprinklerius, įrengtus tiek įprastiniuose pastatuose, tiek pramoniniuose objektuose. Taciau duomenų patikimumui vertinti rinkimas susiduria ir su kai kuriais sunkumais. Nėra sutartinės sprinklerių atsakų apibrėžimo ir įvardijimo praktikos, ataskaitos apie atsakus dažnai labai skiriasi, patikimumo vertinimą sunkina ir tai, kad vyraujanti įprastinių pastatų sprinklerių atsakų priežastis yra žmonių klaidos. Patikimumo vertinimą apsunkina ir sprinklerių senėjimo reiskinys, sistemų modifikavimai ir remontai. Patikimumo duomenų kiekį riboja ir tai, kad duomenys, gauti skirtingose eksploatavimo aplinkose, tinka tik toms aplinkoms. Sprinklerių eksploatacija ir aplinkos sąlygos gali būti skirtingos. Duomenų kiekį riboja ir jų kaina, senėjimas bei slėpimas. Duomenys, kaupiami kai kuriose bazėse, būna nepakankamai paaiskinti ir netinkamai dokumentuoti. Kai sprinklerių sistemos patikimumas vertinamas taikant atsakų medžio analize, įvesties duomenys gali būti gauti ir is bendrųjų patikimumo duomenų bazių. Taciau literatūroje ir internete negalima rasti duomenų bazės, kurioje būtų sukaupti duomenys būtent apie sprinklerių sistemų komponentų patikimumą. Kai patikimumo duomenų trūksta, jį galima vertinti taikant Bajeso metodus. Tiek teorinis modeliavimas, tiek duomenų rinkimas siandien dar neleidžia atsižvelgti į fizinį sprinklerių senėjimą bei modifikacijas, kurios gali labai paveikti sių sistemų patikimumą. Reiksminiai žodžiai: sprinkleriai, gaisras, duomenų saltinis, duomenų bazė, patikimumas, atsako dažnis, senėjimas, žmogaus klaida

[1]  M. Law,et al.  Evaluation of Fire Safety: Rasbash/Evaluation of Fire Safety , 2006 .

[2]  Nathan Siu,et al.  Bayesian parameter estimation in probabilistic risk assessment , 1998 .

[3]  M. Röwekamp,et al.  Reliability data collection for fire protection features , 2000 .

[4]  Egidijus Rytas Vaidogas,et al.  Sustainable development and major industrial accidents: The beneficial role of risk‐oriented structural engineering , 2008 .

[5]  Egidijus Rytas Vaidogas,et al.  Assessing external threats to structures using limited statistical data: An approach based on data resampling , 2007 .

[6]  Daniel Malm,et al.  Reliability of automatic sprinkler systems - An analysis of available statistics , 2008 .

[7]  LeslieE. Lahti,et al.  Guidelines for process equipment reliability data , 1991 .

[8]  Kaisa Simola,et al.  Bayesian models and ageing indicators for analysing random changes in failure occurrence , 2000, Reliab. Eng. Syst. Saf..

[9]  Richard W. Bukowski,et al.  Estimates of the Operational Reliability of Fire Protection Systems , 2002 .

[10]  Egidijus Rytas Vaidogas,et al.  Reliability of a timber structure exposed to fire: estimation using fragility function , 2008 .

[11]  B. Hoła.  Development tendency for accident situation in building industry , 2006 .

[12]  B. Hoła.  Dynamics of the development of accident situation in construction industry with reference to building production value , 2009 .

[13]  Wan Ki Chow,et al.  Fire safety aspects of refuge floors in supertall buildings with Computational Fluid Dynamics , 2009 .

[14]  Dennis P. Nolan.  Handbook of Fire and Explosion Protection Engineering Principles: for Oil, Gas, Chemical and Related Facilities , 1997 .

[15]  Edward K. Budnick.  AUTOMATIC SPRINKLER SYSTEM RELIABILITY , 2001 .

[16]  Bożena Hoła.  Methodology of hazards identification in construction work course , 2010 .

[17]  Robert E. Melchers,et al.  Structural Reliability: Analysis and Prediction , 1987 .

[18]  S. J. Melinek.  Potential value of sprinklers in reducing fire casualties , 1993 .

[19]  Hung-Chi Su,et al.  Influence of fire source locations on the actuation of wet-type sprinklers in an office fire , 2010 .

[20]  Ulrich Hauptmanns,et al.  Availability analysis for a fixed wet sprinkler system , 2008 .

[21]  George E. Apostolakis,et al.  A Methodology for Analyzing the Detection and Suppression of Fires in Nuclear Power Plants , 1986 .

[22]  S. J. Melinek.  Effectiveness of sprinklers in reducing fire severity , 1993 .

[23]  Krzysztof Schabowicz,et al.  Estimation of earthworks execution time cost by means of artificial neural networks , 2010 .

[24]  Edmundas K. Zavadskas,et al.  Multiattribute Selection from Alternative Designs of Infrastructure Components for Accidental Situations , 2009, Comput. Aided Civ. Infrastructure Eng..

[25]  Jerzy Gałaj.  A general concept of fire hybrid modelling in compartments , 2009 .

[26]  G. Apostolakis,et al.  Uncertain data and expert opinions in the assessment of the unavailability of suppression systems , 1988 .

[27]  Krzysztof Schabowicz,et al.  MATHEMATICAL-NEURAL MODEL FOR ASSESSING PRODUCTIVITY OF EARTHMOVING MACHINERY , 2007 .

[28]  Sergey Kozlov,et al.  Choosing the risk curve type , 2009 .

[29]  Egidijus Rytas Vaidogas,et al.  Protecting built property against fire disasters: Multi ‐attribute decision making with respect to fire risk , 2010 .

[30]  Egidijus Rytas Vaidogas,et al.  ON APPLYING SPARSE AND UNCERTAIN INFORMATION TO ESTIMATING THE PROBABILITY OF FAILURE DUE TO RARE ABNORMAL SITUATIONS , 2009 .

[31]  D. Rasbash.  Evaluation of Fire Safety , 2004 .

[32]  Bożena Hoła.  General model of accident rate growth in the construction industry , 2007 .

[33]  Egidijus Rytas Vaidogas,et al.  Assessment of structures subjected to accidental actions using crisp and uncertain fragility functions , 2009 .

[34]  Curtis Smith,et al.  Bayesian inference in probabilistic risk assessment - The current state of the art , 2009, Reliab. Eng. Syst. Saf..

[35]  Chu Guanquan,et al.  Quantitative assessment of building fire risk to life safety. , 2008, Risk analysis : an official publication of the Society for Risk Analysis.

[36]  Marek Konecki,et al.  Simple fire model for comparative studies of critical conditions during combustion of chosen polymer materials , 2009 .

[37]  E. Vaidogas,et al.  Effect of explosive loading on mechanical properties of concrete and reinforcing steel: towards developing a predictive model , 2010 .

[38]  Jayant V. Deshpande,et al.  Life-Time Data - Statistical Models and Methods , 2006, Series on Quality, Reliability and Engineering Statistics.

[39]  Olavi Keski-Rahkonen,et al.  Reliability of sprinkler systems: Exploration and analysis of data from nuclear and non-nuclear installations , 2004 .

[40]  S. A. Eide,et al.  Component failure rate data sources for probabilistic safety and reliability , 2010 .

[41]  Olavi Keski-Rahkonen,et al.  On the reliability of fire detection and alarm systems Exploration and analysis of data from nuclear and non-nuclear installations , 2005 .

[42]  David T. Butry,et al.  Benefit-Cost Analysis of Residential Fire Sprinkler Systems | NIST , 2007 .

[43]  Egidijus Rytas Vaidogas,et al.  Prediction of accidental actions likely to occur on building structures. An approach based on stochastic simulation , 2007 .

[44]  L. C. Cadwallader,et al.  Failure Rate Data Analysis for High Technology Components , 2007 .

[45]  Bożena Hoła.  Methodology of estimation of accident situation in building industry , 2009 .

[46]  Egidijus Rytas Vaidogas.  First step towards preventing losses due to mechanical damage from abnormal actions : Knowledge-based forecasting the actions , 2006 .