Assessment of fracture energy of strain‐hardening fiber‐reinforced cementitious composite using experiment and machine learning technique

This study investigates effects of both matrix strength and fiber type on fracture energy of strain‐hardening fiber‐reinforced concrete cementitious composite (SHFRCC) under direct tensile test. Three steel fiber types (twisted, hooked, and smooth fibers) were reinforced to three matrices with different compressive strengths, ranging from 28 to 180 MPa. In addition, a considerable number of direct tensile test results were collected to develop a machine learning‐based model for estimating fracture energy of SHFRCCs. The test results indicated that the fracture energy of SHFRCCs exhibited significant improvements with increasing matrix strength. Moreover, smooth fibers generated the highest values of fracture energy in the matrix with the highest compressive strength of 180 MPa whereas twisted did in other matrices. From the prediction results, the possibility of using a machine learning‐based model to predict the fracture energy of SHFRCCs was demonstrated.