Prediction of Energy Absorption Capacity of High-Performance Fiber-Reinforced Cementitious Composite

High-performance fiber-reinforced cementitious composite (HPFRCC), a new class of concrete technology, exhibits outstanding mechanical resistance, especially in terms of superior post cracking strength, strain capacity, and energy absorption capacity. Among mechanical properties, energy absorption capacity of HPFRCCs has become one of the most popular properties that received much attention from researchers to discover and model. However, a more accurate model for prediction of energy absorption capacity is still discouraged to develop since current empirical regression models based on limited data have shown their limitations. In this research, the energy absorption capacity of HPFRCCs is predicted through a proposed machine learning-based model using 103 tensile test results. The input variables include matrix strength, fiber type, fiber length, fiber diameter, and fiber volume content, while the output variable consists of energy absorption capacity. From the prediction results, the energy absorption capacity could be predicted well using machine learning based models. From the results of sensitivity analysis, the contribution of each input variable to the energy absorption capacity of HPFRCCs was figured out.