Design and development of a material impact tester using neural network for concrete ratio classification

Concrete can be molded to any shape and size, and once hardened it can withstand tremendous amount of compressive loads. This ability of concrete makes it the most widely used material in construction and thus, a need for identification and prediction of its compressive strength. Nondestructive tests have been solely preferred for this purpose and a drop-impact test machine prototype named; Material Strength Impact Tester requires a validation of its test results. The study implements an Artificial Neural Network in evaluating the test results from three concrete class ratios. The resulting test and validation of the sample specimens from the developed prototype showed a strong correlation to its corresponding concrete class ratio. The study strengthens the claim of the previous literature as to the prototype's functionality as a drop-impact testing machine.