Autogenous Healing of Early-Age Cementitious Materials Incorporating Superabsorbent Polymers Exposed to Wet/Dry Cycles

Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R²) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R²_GSA-GBR = 0.958) and stronger robustness (RMSE_GSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

Autogenous Self-Healing Capacity of Early-Age Ultra-High-Performance Fiber-Reinforced Concrete

This paper analyzes the autogenous self-healing capacity of early-age Ultra-High-Performance Fiber-Reinforced concretes (UHPFRCs) by measuring the crack closure and the possible mechanical recovery on healed specimens. The main parameters considered in this research were the healing exposure conditions (humidity chamber, immersion in tap water, immersion in seawater and heat curing) and the precracking levels (microcracks and macrocracks). For the microcrack level, four-point bending tests were performed on prismatic specimens (100 × 100 × 500 mm3) obtaining a multiple cracking pattern characterized by crack widths ranged from 10 to 20 µm. Whereas for the macrocrack level (behavior after crack localization), splitting tests were carried out on notched cubic specimens (100 × 100 × 100 mm3) obtaining crack widths of up to 0.4 mm. For both precracking levels, specimens were precracked at two days and were cured for one month in the mentioned exposure conditions. Healing products were analyzed on the specimen surface and also inside the cracks; to this purpose, their microstructure was analyzed by means of SEM and EDS analyses. The results have shown that the highest crack closure values were obtained for the heat-cured specimens and for the specimens immersed in water (tap water and seawater) whereas the less efficient condition was the humidity chamber.

Autogenous Healing of Early-Age Cracks in Cementitious Materials by Superabsorbent Polymers

The effect of superabsorbent polymers (SAPs) on autogenous crack healing in cementitious materials with early-age cracking was investigated. SAP-containing samples exposed to wet/dry cycles showed better autogenous healing than those only exposed to wet conditions, as determined by water flow and compressive strength recovery tests. The water flow rates through cracks (380 ± 40 µm) in cement paste and cement mortar containing 1.0% SAP decreased by around 97.1-100% and 79.7-90.7%, respectively, after 14 cycles of healing compared to 1 cycle. Although the initial compressive strength decreased with SAP addition, it recovered somewhat after a 28-d healing period. Microscopy and spectroscopy results identified CaCO3 and/or calcium silicate hydrate (CSH) as the main healing products.

Influence of Effective Water-to-Cement Ratios on Internal Damage and Salt Scaling of Concrete with Superabsorbent Polymer

Superabsorbent polymer (SAP) is attracting attention as a water-entraining admixture that reduces shrinkage or heals cracks in concrete. Cross-linked sodium polyacrylate SAPs, which are the most widely produced SAPs in the global market, are applicable as concrete admixtures. However, there have been contradictory results on the freeze–thaw resistance of concrete with SAPs. This study aims to clarify these results considering the water absorption behavior of SAPs in hardened concrete when effective water-to-cement ratios are different. Firstly, the absorbencies of one kind of cross-linked sodium polyacrylate SAP (SAP_SP) in pore solution and fresh mortar were measured by a tea bag test and flow test, respectively. Pore size distribution, capillary water absorption, and deformation during freeze–thaw cycles were analyzed for mortar samples with varying SAP_SP dosages. In the main tests, concrete samples with three different SAP_SPs/cement ratios (0.1%, 0.2%, and 0.3%) and a reference sample were prepared, and internal damage and salt scaling were measured under freeze–thaw cycles. Because SAP_SP absorbs water in fresh mixtures, additional water was added to the mixture considering the water absorbency of the SAP_SP. It was found that the used SAP_SPs prematurely release their stored water so the effective water-to-cement ratio was increased when a larger amount of SAP_SP was used. The higher effective water-to-cement ratio caused more internal damage and salt scaling due to the weaker cementitious matrix. In addition, mortar samples with a high SAP_SP content show a larger absorption of capillary water than the reference sample. The result can be interpreted by an observation that SAP_SP in air voids absorbs water and expands to relatively large capillary pores or neighbor air voids during the capillary water absorption process.