ABSTRACT
Self-healing cementitious materials containing microcapsules filled with healing agents can autonomously seal cracks and restore structural integrity. However, optimising the microcapsule mechanical properties to survive concrete mixing whilst still rupturing at the cracked interface to release the healing agent remains challenging. This study develops an integrated numerical modelling and machine learning approach for tailoring acrylate-based microcapsules for triggering within cementitious matrices. Microfluidics is first utilised to produce microcapsules with systematically varied shell thickness, strength, and cement compatibility. The capsules are characterised and simulated using a continuum damage mechanics model that is able to simulate cracking. A parametric study investigates the key microcapsule and interfacial properties governing shell rupture versus matrix failure. The simulation results are used to train an artificial neural network to rapidly predict the triggering behaviour based on capsule properties. The machine learning model produces design curves relating the microcapsule strength, toughness, and interfacial bond to its propensity for fracture. By combining advanced simulations and data science, the framework connects tailored microcapsule properties to their intended performance in complex cementitious environments for more robust self-healing concrete systems.
ABSTRACT
Intelligent cementitious materials integrated with carbon nanofibers (CNFs) have the potential to be used as sensors in structural health monitoring (SHM). The difficulty in dispersing CNFs in cement-based matrices, however, limits the sensitivity to deformation (gauge factor) and strength. Here, we synthesise CNF by chemical vapour deposition on the surface of calcium oxide (CaO) and, for the first time, investigate this amphiphilic carbon nanomaterial for self-sensing in mortar. SEM, TEM, TGA, Raman and VSM were used to characterise the produced CNF@CaO. In addition, the electrical resistivity of the mortar, containing different concentrations of CNF with and without CaO, was measured using the four-point probe method. Furthermore, the piezoresistive response of the composite was quantified by means of compressive loading. The synthesised CNF was 5-10 µm long with an average diameter of ~160 nm, containing magnetic nanoparticles inside. Thermal decomposition of the CNF@CaO compound indicated that 26% of the material was composed of CNF; after CaO removal, 84% of the material was composed of CNF. The electrical resistivity of the material drops sharply at concentrations of 2% by weight of CNF and this drop is even more pronounced for samples with 1.2% by weight of washed CaO. This indicates a better dispersion of the material when the CaO is removed. The sensitivity to deformation of the sample with 1.2% by weight of CNF@CaO was quantified as a gauge factor (GF) of 1552, while all other samples showed a GF below 100. Its FCR amplitude can vary inversely up to 8% by means of cyclic compressive loading. The method proposed in this study provides versatility for the fabrication of carbon nanofibers on a tailored substrate to promote self-sensing in cementitious materials.
ABSTRACT
Soil mix cut-off walls have been increasingly used for containment of organic contaminants in polluted land. However, the mixed soil is susceptible to deterioration due to aggressive environmental and mechanical stresses, leading to crack-originated damage and requiring costly maintenance. This paper proposed a novel approach to achieve self-healing properties of soil mix cut-off wall materials triggered by the ingress of organic contaminants. Oil sorbent polymers with high absorption and swelling capacities were incorporated in a cementitious grout and mixed with soil using a laboratory-scale auger setup. The self-healing performance results showed that 500 µm-wide cracks could be bridged and blocked by the swollen oil sorbents, and that the permeability was reduced by almost an order of magnitude after the permeation of liquid paraffin. It was shown by micro-CT scan tests that the network formed by the swollen oil sorbents acted as attachments and binder, preventing the cracked mixed soil sample from crumbling, and that the oil sorbents swelled three times in volume and therefore occupied the air space and blocked the cracks in the matrix. These promising results exhibit the potential for the oil sorbents to provide soil mix cut-off walls in organically-contaminated land with self-healing properties and enhanced durability.