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Producing high-ductility cementitious composites (HDCC) increased in parallel with concrete demand in China recently. However, the high cost of manufacturing cementitious composites (HDCC) persists. To reduce the cost of HDCC, steel fibers, polyvinyl alcohol (PVA), and river sand were used to produce HDCC concrete in the present study. A total fiber content of 2% was formed with five different proportions of PVA fiber and steel fiber. Within the scope of the experimental studies, mechanical (workability, compressive strength, tensile, and bending properties), and microstructural (scanning electron microscopy) tests were carried out to investigate the properties of the hybrid fiber-reinforced composites. The results showed that the fluidity of HDCC increased with increasing steel fiber substitution. The compressive strength of the mixture containing 0.5% steel fiber and 1.5% PVA fiber exhibited a better compressive strength of 31.3 MPa. The tensile performance of the mixture was improved due to the incorporation of steel fiber. The initial cracking strength was about 2.32 MPa, 25.4% higher than that of the reference group, and the ultimate tensile strength was 3.36–3.56 MPa. However, reducing the content of PVA fiber impacts the flexural rigidity of the matrix.

Artificial intelligence technology has super high-dimensional nonlinear computing capabilities, intelligent comprehensive analysis and judgment functions, and self-learning knowledge reserve expression functions. It can unlock the potential of high-dimensional nonlinearity relation between tangible components and performance indicators when compared to the empirical formula generated from classic statistical approaches. This article summarizes the types of artificial intelligence algorithms used to predict concrete performance, comprehensively sorts out the research progress of artificial intelligence technology in predicting the mechanical properties, work performance, and durability of concrete, and compares and analyzes the effects of algorithm selection, sample data, and model construction on the concrete compressive prediction system. The analysis shows that artificial intelligence technology has obvious advantages in measurement accuracy in predicting concrete performance compared to conventional statistical methods. Multiple algorithms should be used to cross-validate the model prediction findings. For tiny data sets, support vector machines are utilized. Decision tree evolution techniques should be used in algorithm models that require feature optimization or dispersed index prediction. Artificial neural networks can be used to solve different challenges. To improve the prediction model and boost its prediction accuracy, measures such as optimized features, integrated algorithms, hyperparameter optimization, enlarged sample data set, richer data sources, and data pretreatment are proposed.