Seawater sea sand-engineered cementitious composites (SS-ECCs) provide a potential solution to the shortage of freshwater and sand resources for coastal and offshore construction. However, systematic studies on the combined effects of fiber parameters in SS-ECC systems remain limited. This study examines the effects of polyethylene (PE) fiber content (0%, 1%, 1.5%, and 2%) and length (12 mm, 18 mm, and 24 mm) on the mechanical properties of SS-ECC via compressive, tensile, and bending tests. The results indicate that increasing the volume fraction of PE fibers effectively enhances the tensile strength, flexural strength, and flexural toughness of SS-ECC. SS-ECC attained its highest tensile strength with a 24 mm PE fiber length, showing increases of 41.1% and 44.2% over specimens with 12 mm and 18 mm fibers, respectively. Furthermore, based on 28-day curing, the utilization of seawater and sea sand led to increases in tensile and flexural strengths by 12.3% and 17.2%, respectively, relative to ECC prepared with freshwater and river sand, though it resulted in a reduction in toughness. A predictive model for tensile strength is established considering the characteristic value of PE fiber with an R² of 0.8461, indicating reasonable correlation within the tested range. Results from this paper can help to develop a favorable PE fiber-reinforced SS-ECC for ocean engineering.

The degradation of concrete structures is significantly influenced by water penetration since water serves as the primary vehicle for the movement of harmful compounds. The process of capillary water absorption is widely recognized as a crucial indicator of durability for unsaturated concrete, as it allows dangerous substances to enter the composite material. The water absorption capacity of concrete is intricately linked to its pore structure, as concrete is inherently porous. The main goal of this work is to create an innovative predictive tool that assesses the porosity of concrete by analyzing its components using a machine-learning (ML) framework. Seven distinct batch design variables were included in the generated database: fly ash, superplasticizer, water-to-binder ratio, curing time, ground granulated blast furnace slag, binder, and coarse-to-fine aggregate ratio. Four distant ML algorithms, including AdaBoost, linear regression (LR), decision tree (DT), and support vector machine (SVM), are utilized to infer the generalization capabilities of ML algorithms to estimate concrete porosity accurately. The RReliefF algorithm was implemented to calculate the significant features influencing porosity. This study concludes that in comparison to the alternative techniques, the AdaBoost method demonstrated superior performance with an R ² score of 0.914, followed by SVM (0.870), DT (0.838), and LR (0.763). The results of the evaluation of RReliefF indicated that the binder possesses a remarkable influence on the porosity of concrete.