Effect evaluation of microbial mineralization for repairing load-induced crack in concrete with a cyclic injection-immersion process
Source abstract
Abstract
This study presents the experimental investigations on repairing the load-induced cracks in concrete with microbial mineralization technology. Two groups of load-induced cracked concrete specimens, splitting specimens and bending specimens, with crack widths in range of 0.05–0.10 mm and 0.10–0.30 mm were prepared, respectively. Bacillus pasteurii (DSM 33) was selected to repair these cracks using microbially induced calcium carbonate precipitation (MICP) method. The repair effectiveness on crack healing was evaluated by means of visual observations, ultrasonic measurement tests, rapid chloride migration (RCM) tests and splitting tensile strength tests. The results indicated that after MICP treatment the resistance of repaired cracked specimens to chloride penetration could be effectively improved. Comparison of repair effectiveness between splitting specimens and bending specimens showed that the former had slightly better crack recovery than the latter. For both cracked specimens, the improvement coefficient which can reflect the repair effectiveness decreased from nearly 90–70% with increasing crack width from 0.05 mm to 0.23 mm. Scanning electron microscope (SEM) and energy dispersive spectrum (EDS) tests revealed that the formation of microbial induced CaCO3 precipitation would accumulate and fill the concrete crack, which is believed to be the reason of improving resistance to chloride ingression in the repaired cracked specimens. Besides, for repaired splitting cracked cylinders, their mean splitting tensile strengths could recover around 65.8% and 52.6% for crack width intervals of 0.05–0.10 mm and 0.10–0.30 mm, respectively.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Microbial Applications in Construction Materials · Concrete and Cement Materials Research · Corrosion Behavior and Inhibition
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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