Compressive strength is probably one the most crucial properties of concrete material. For existing structures, core samples are drilled and tested to obtain the concrete compressive strength. Many times, taking core samples is not feasible, and as a result, nondestructive methods to examine the concrete are required. The rebound hammer test is one of the most popular methods to estimate concrete compressive strength without causing damage to the existing structure. The test is inexpensive and can be easily conducted compared to other nondestructive testing methods. Also, concrete compressive strength estimations can be obtained almost instantly. However, previous results have shown that concrete compressive strength estimations obtained from rebound hammer tests are not very accurate. As a result, this research attempts to apply artificial intelligence prediction models to estimate concrete compressive strength using data from in situ rebound hammer tests. The results show that artificial intelligence methods can effectively improve in situ concrete compressive strength estimations in rebound hammer tests.

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%.