In today’s market, it is imperative to be knowledgeable and have an edge over the competition. ACI members have it…they are engaged, informed, and stay up to date by taking advantage of benefits that ACI membership provides them.
Read more about membership
Become an ACI Member
Founded in 1904 and headquartered in Farmington Hills, Michigan, USA, the American Concrete Institute is a leading authority and resource worldwide for the development, dissemination, and adoption of its consensus-based standards, technical resources, educational programs, and proven expertise for individuals and organizations involved in concrete design, construction, and materials, who share a commitment to pursuing the best use of concrete.
American Concrete Institute
38800 Country Club Dr.
Farmington Hills, MI
Feedback via Email
Home > Publications > International Concrete Abstracts Portal
The International Concrete Abstracts Portal is an ACI led collaboration with leading technical organizations from within the international concrete industry and offers the most comprehensive collection of published concrete abstracts.
Title: Artificial Neural Networks in Prediction of Concrete Strength Reduction Due to High Temperature
Author(s): Chih-Hung Chiang and Chung-Chia Yang
Publication: Materials Journal
Appears on pages(s): 93-102
Keywords: exposure; pulse velocity; strength
Abstract:The effect of temperature on the compressive strength of concrete has been thoroughly explored. The effect of exposure time, however, still needs systematic exploration. This paper demonstrates that artificial neural networks can be used to predict the residual strength of heated concrete effectively. Experimental investigation reveals that loss of strength becomes significant as exposure times prolonged at exposure temperatures of 400 °C and higher. Regression analysis of residual pulse velocity and residual strength shows that a certain linear relationship exists for exposure time is between 30 and 120 min. The dependence of residual strength on exposure time and temperature is highly nonlinear. Due to the absence of a theoretical relationship, neural network analysis is applied to identify a possible general relationship between residual strength and variables including exposure temperature, exposure time, water-cement ratio (w/c), and residual pulse velocity. Three neural networks are designed and trained by data from the experiments and data collected from the literature. Good linear relationships are found when comparing the residual strength predicted by networks with correspondent target values. Such results are promising and can be extended to be part of assessment for fire-damaged concrete prior to repair of structures.
Click here to become an online Journal subscriber