Adapting ANFIS to Improve Field Rebound Hammer Test for Concrete Compressive Strength Estimation

Abstract:

Rebound hammer tests are one of the most popular non-destructive testing methods to examine the concrete compressive strength in the field. Rebound hammer tests are relatively easy to conduct and low cost. More importantly, it will not cause damage to the existing structure and can obtain the results in a short time. However, concrete compressive strength estimations provided by rebound hammer tests have an average of around 20% mean absolute percentage error (MAPE) when comparing to the results from destructive tests. This research proposes an alternative approach to estimate the concrete compressive strengths using the rebound hammer test data. The alternative approach is to adopt the Artificial Neural Fuzzy Inference Systems, ANFIS, to develop an AI-based prediction model for the rebound hammer tests. A total of 100 rebound hammer tests are conducted in a 24-story residential building. Core samples are carefully taken to obtain the actual compressive tests. The data collected are used to train and validate the ANFIS prediction model. The results show that the proposed ANFIS model has successfully reduced the MAPE to 10.01%.

• Periodical: Materials Science Forum (Volume 975)
• Pages: 191-196
• DOI: https://doi.org/10.4028/www.scientific.net/MSF.975.191
• Online since: January 2020
• Authors: Yu Ren Wang*, Dai Lun Chiang, Yi Jao Chen
• Keywords: Adaptive Neural Fuzzy Inference Systems, ANFIS, Artificial Intelligence, Concrete Compressive Strength, Non-Destructive Test, Rebound Hammer Test

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