Engineered Cementitious Composites (2024-05)¶

Engineered Cementitious Composites (ECC) represent a new category of cementitious materials that exhibit exceptional ductility and strain-hardening properties. It possesses several attractive characteristics. The most distinctive characteristic of this material is its remarkable tensile ductility, which surpasses that of concrete by several hundred times while simultaneously retaining compressive strengths comparable to those of concrete or high-strength concrete. This study explores the diverse domain of ECCs, particularly emphasizing their configuration, structural applications, and implementation within the context of three-dimensional (3D) printing. The study commences by conducting a comprehensive analysis of the fundamental principles that regulate ECC's design. The ECC design methodology employs a micromechanical framework, which entails the optimization of material characteristics and mixture ratios to attain superior tensile strain-hardening performance. The material selection process involves multiple elements, and it is crucial to cautiously establish the appropriate mix proportions to guarantee the intended performance attributes of ECC. The utilization of ECC in construction materials is considered a significant advancement due to its exceptional mechanical properties and superior durability. ECC's fiber content, limited to 2% or less by volume, renders it highly versatile for construction project implementation on-site or in producing structural elements at precast plants. ECC provides numerous benefits when utilized in structural contexts. The material's ductility, toughness, strain-hardening behavior and self-repair capabilities render it valuable for repairing and retrofitting pre-existing structures, constructing novel structures, and advancing sustainable construction practices. The self-reinforcing characteristic of ECC eliminates the need for steel reinforcement, rendering ECC a desirable material for 3D printing applications. This study also investigates the potential for machine learning (ML) to advance the area of ECC. ML has been used to forecast the characteristics and self-healing capacity of ECC. Furthermore, the study concludes with reflections on ECC's prospects, challenges, and future directions.