TY - BOOK ID - 133564132 TI - Probabilistic and Fuzzy Approaches for Estimating the Life Cycle Costs of Buildings PY - 2021 PB - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - Technology: general issues KW - dynamic analysis KW - steel frames KW - Tuned Mass Damper KW - optimization KW - drift ratio KW - sustainable construction industry KW - lifecycles KW - European Union Member States KW - complex evaluation KW - multiple criteria analysis KW - COPRAS and INVAR methods KW - success and image of a country KW - marketing KW - residential buildings KW - defects KW - intensity KW - reliability KW - technical wear KW - railway infrastructure KW - occurrences KW - socioeconomic impact KW - economic evaluation KW - CBA KW - life cycle KW - investment project KW - probability distribution KW - sensitivity analyses KW - risk assessment KW - tenement houses KW - damage KW - maintenance KW - fuzzy sets KW - Bayes conditional probability KW - substitution KW - operation and maintenance phase KW - cause–effect relationships KW - historical buildings KW - implementation factors KW - information and communication technology KW - life cycle costs KW - buildings KW - bidding decision KW - LCC criterion KW - price criterion KW - construction KW - statistical method KW - classification KW - probability of winning KW - risk identification KW - MCDM KW - critical risk factors KW - commercial and recreational complex building projects UR - https://www.unicat.be/uniCat?func=search&query=sysid:133564132 AB - The Life cycle cost (LCC) method makes it possible for the whole life performance of buildings and other structures to be optimized. The introduction of the idea of thinking in terms of a building life cycle resulted in the need to use appropriate tools and techniques for assessing and analyzing costs throughout the life cycle of the building. Traditionally, estimates of LCC have been calculated based on historical analysis of data and have used deterministic models. The concepts of probability theory can also be applied to life cycle costing, treating the costs and timings as a stochastic process. If any subjectivity is introduced into the estimates, then the uncertainty cannot be handled using the probability theory alone. The theory of fuzzy sets is a valuable tool for handling such uncertainties. In this Special Issue, a collection of 11 contributions provide an updated overview of the approaches for estimating the life cycle cost of buildings. ER -