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Problems in Service Life Prediction of Building and Construction Materials

by L. W. Masters
Save 35% Save 35%
Current price ₹14,534.00
Original price ₹22,359.00
Original price ₹22,359.00
Original price ₹22,359.00
(-35%)
₹14,534.00
Current price ₹14,534.00

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Book cover type: Paperback
  • ISBN13: 9789401087667
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Springer
  • Publisher Imprint: Springer
  • Publication Date:
  • Pages: 300
  • Original Price: EUR 199.99
  • Language: English
  • Edition: Softcover Repri
  • Item Weight: 467 grams
  • BISAC Subject(s): Construction / General, Civil / General, and Materials Science / General

Degradation, the chemical/physical response of building and con- struction materials exposed to in-service environments, must be predicted prior to their installation in structures if materials are to be effectively selected, used and maintained. These assessments of materials degradation require that methods be available to aid prediction of service life. The objectives of building materials science are a) to charac- terize and categorize materials, b) to predict, preferably in a mathematical sense, material or component response including expected service life, and c) to make improvements in material response through improvements in design, formulation, processing or specification. For building and construction materials, continued progress has been made towards objective (a), but little progress has been made towards objectives (b) and (c). Of these, the mathematical prediction of service life appears to be of greater importance, because, if general approaches or models having application to a wide range of building and construction materials can be identi- fied, then the categorization, selection, use and improvement of materials can proceed in a systematic manner. Researchers in advanced technologies, such as aerospace, nuclear, electronics and medicine, have apparently been more successful than researchers in building and construction technology in responding to the need for reliable predictions of service life.

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