Concrete and cement-based materials inherently possess an autogenous self-healing capacity. Despite the huge amount of literature on the topic, self-healing concepts still fail to consistently enter design strategies able to effectively quantify their benefits on structural performance. This study aims to develop quantitative relationships through statistical models and artificial neural network (ANN) by establishing a correlation between the mix proportions, exposure type and time, and width of the initial crack against suitably defined self-healing indices (SHI), quantifying the recovery of material performance. Furthermore, it is intended to pave the way towards consistent incorporation of self-healing concepts into durability-based design approaches for reinforced concrete structures, aimed at quantifying, with reliable confidence, the benefits in terms of slower degradation of the structural performance and extension of the service lifespan. It has been observed that the exposure type, crack width and presence of healing stimulators such as crystalline admixtures has the most significant effect on enhancing SHI and hence self-healing efficiency. However, other parameters, such as the amount of fibers and Supplementary Cementitious Materials have less impact on the autogenous self-healing. The study proposes, through suitably built design charts and ANN analysis, a straightforward input–output model to quickly predict and evaluate, and hence “design”, the self-healing efficiency of cement-based materials.

Geotextiles are widely used for reinforcing soil, improving drainage, controlling soil erosion and embankment construction. Existing research recommends the improvement of soil in an economic and eco-friendly manner using jute geotextiles. However, jute fibres have the tendency to degrade in the acidic and alkaline environment of the soil. Jute geotextile treated with antimicrobial chemicals are used as a substitute for manmade geosynthetics, as it improves the life expectancy of jute. But these chemicals are expensive and are a potential source of leaching. The present study aims to develop a fly ash-based treatment procedure that is economic, ecologically safe as well as significantly improves the engineering and strength properties of jute as a geotextile. There is an increasing interest in the application of alkali-activated binders (AAB) in engineering practices. It is produced by the reaction between an aluminosilicate precursor (primarily Class F fly ash and/or slag) and alkali activator solution. AAB-treated jute geotextile shows that the load-bearing capacity increases by 27% approximately. The vertical permeability of sand does not vary significantly with the inclusion of AAB-treated jute. The tensile strength of jute also exhibits a marked improvement following the treatment. Thus, the technique proposed in this study has the potential for implementation in practical applications.

Cortical bone is a tough biological material composed of tube‐like osteons embedded in the organic matrix surrounded by weak interfaces known as cement lines. The cement lines provide a microstructurally preferable crack path, hence triggering in‐plane crack deflection around osteons due to cement line‐crack interaction. Inspired by this toughening mechanism and facilitated by a hybrid (3D‐printing/casting) process, the study engineers architected tubular cement‐based materials with the stepwise cracking toughening mechanism, that enables a non‐brittle fracture. Using experimental and theoretical approaches, the study demonstrates the competition between tube size and shape on stress intensity factor from which engineering stepwise cracking can emerge. Two competing mechanisms, both positively and negatively affected by the growing tube size, arise to significantly enhance the overall fracture toughness by up to 5.6‐fold compared to the monolithic brittle counterpart without sacrificing the specific strength. This is enabled by crack‐tube interaction and engineering the tube size, shape, and orientation, which promotes rising resistance‐curves (R‐curve). “Disorder” curves and statistical mechanics parameters are proposed for the first time to quantitatively characterize the degree of disorder for describing the representation of the architected arrangement of materials in lieu of otherwise inadequate “periodicity” classification and misperceived disorder parameters (perturbation and Voronoi tessellation methods).