Adapting Artificial Intelligence to Improve In Situ Concrete Compressive Strength Estimations in Rebound Hammer Tests - Wang, Yu Ren; Lu, Yen Ling; Chiang, Dai Lun (Frontiers in Materials, 2020)
The compressive strength is probably one the most crucial properties of the concrete material. For existing structures, core samples are drilled and tested to obtain the concrete compressive strengths. In many occasions, taking core samples are not feasible and as a result, it requires non-destructive methods to examine the concrete. Rebound hammer test is one of the most popular methods to estimate the concrete compressive strength without causing damage to the existing structure. The test is inexpensive and can be easily conducted comparing to other non-destructive testing methods. Also, the 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 based 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 the in-situ concrete compressive strength estimations in rebound hammer tests.