Enhancing self-healing efficiency of concrete using multifunctional granules and PVA fibers

Zhang, Chunyu; Wang, Meng; Liu, Rentai; Li, Xiuhao; Yan, Jia; Du, Hongjian

doi:10.1016/j.jobe.2023.107314

Cited by 4 publications

(2 citation statements)

References 49 publications

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“…Concrete is widely used in dams, bridges, and other large-scale engineering structures [1]. For these structures, maintenance, monitoring, and life assessments are very important tasks after the post-construction period [2][3][4]. Among the various disasters that can occur during the maintenance period of bridge engineering, cracks often appear first [5].…”

Section: Introductionmentioning

confidence: 99%

A Rapid Bridge Crack Detection Method Based on Deep Learning

Liu,

Gao,

Zhao

et al. 2023

Applied Sciences

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The aim of this study is to enhance the efficiency and lower the expense of detecting cracks in large-scale concrete structures. A rapid crack detection method based on deep learning is proposed. A large number of artificial samples from existing concrete crack images were generated by a deep convolutional generative adversarial network (DCGAN), and the artificial samples were balanced and feature-rich. Then, the dataset was established by mixing the artificial samples with the original samples. You Only Look Once v5 (YOLOv5) was trained on this dataset to implement rapid detection of concrete bridge cracks, and the detection accuracy was compared with the results using only the original samples. The experiments show that DCGAN can mine the potential distribution of image data and extract crack features through the deep transposed convolution layer and down sampling operation. Moreover, the light-weight YOLOv5 increases channel capacity and reduces the dimensions of the input image without losing pixel information. This method maintains the generalization performance of the neural network and provides an alternative solution with a low cost of data acquisition while accomplishing the rapid detection of bridge cracks with high precision.

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Section: Introductionmentioning

confidence: 99%

A Rapid Bridge Crack Detection Method Based on Deep Learning

Liu,

Gao,

Zhao

et al. 2023

Applied Sciences

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“…On the other hand, repairing cracks through the precipitation of CaCO 3 is not beneficial for larger cracks, and this is why a few scholars have explored other chemicals to seal large cracks [30]. For example, Zhang et al [31] and Sun et al [32] suggested the use of polyvinyl alcohol (PVA) fibers and aluminum oxide to repair cracks 0.5 to 2.0 mm in size through microbially induced carbonate precipitation. Existing studies on crack repair did not consider the crack roughness.…”

Section: Introductionmentioning

confidence: 99%

A Novel Mathematical Model for Repairing Rough Cracks Using the Microbially Induced Carbonate Precipitation (MICP)

Zhang,

Wang,

Ahmed

et al. 2023

Sustainability

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Concrete cracks have an adverse effect on the strength properties and durability of concrete structures. Therefore, repairing concrete cracks to recover the concrete’s strength parameters is an important task in the civil engineering field. For repairing concrete cracks, the MICP technique has been widely analyzed in recent times; however, no research has been conducted to deeply investigate the repair effects of MICP on concrete cracks with a rough surface using a theoretical model. In the current research, MICP with a novel mathematical model was conducted considering the precipitation of calcium carbonate (CaCO3), ureolysis, suspended biomass, geochemistry, transport of solutes, and biofilm growth. Furthermore, crack repair experiments were performed to assess the performance of the new mathematical model. The results revealed that the calculated concentrations of suspended biomass in cracks gradually decreased during the test. The comparison between the experimental results and calculated results verified the precision of the migration behavior of the suspended biomass. At the inlet, the solute concentrations and volume fractions of biofilm were higher, causing an increase in the productive rates of calcium carbonate. The consumed concentrations of solutes were higher for cracks with a smoother surface, eventually leading to smaller values of sonic time; the upper parts of the cracks also had smaller values of sonic time, showing good repair effects. The proposed mathematical model provides a better solution to control the repair time and microbial metabolism process, allowing for adjustive bioremediation and biomineralization of concrete, which could provide a firm basis for the remediation of materials in the civil engineering field.

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A Novel Mathematical Model for Rough Cracks Repairing by Using the Method of Microbially Induced Carbonate Precipitation (MICP)

Zhang,

Wang,

Ahmed

et al. 2023

Preprint

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Concrete cracks have a detrimental effect on the strength properties and durability of the structures. So, repairing of concrete cracks to recover their strength parameters is more important task in civil engineering. To repair concrete cracks, MICP has been widely analysed in recent times; but, zero research is conducted to deeply investigate the repair effects of MICP on concrete cracks with rough surface by using the theoretic model. In current research, MICP with a novel mathematical model was attained by taking the precipitation of calcium carbonate (CaCO3), ureolysis, suspended biomass, geochemistry, transport of solute and biofilm growth. Furthermore, crack repair experiments were performed to check the workability of the new mathematical method. The outcomes revealed that the designed suspended biomass concentrations in cracks diminished step by step. The comparison in between the experimental results and calculated results verified the precision of migration behaviour of the suspended biomass. At the inlet, the solute concentrations and biofilm volume fractions was higher, causing in rise of yield amounts for calcium carbonate. The consumed solutes concentrations were higher for cracks of less rough surface, ultimately causing in smaller sonic time; and the values of sonic time for upper portions of cracks was smaller, showing good repair impacts. The recommended mathematical model provides a better tool that controls repair time and microbial metabolism process to report a new adjustive, bioremediation and biomineralization to concrete, which could provide a firm base for remediation of material in civil engineering field.

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Enhancing self-healing efficiency of concrete using multifunctional granules and PVA fibers

Cited by 4 publications

References 49 publications

A Rapid Bridge Crack Detection Method Based on Deep Learning

A Rapid Bridge Crack Detection Method Based on Deep Learning

A Novel Mathematical Model for Repairing Rough Cracks Using the Microbially Induced Carbonate Precipitation (MICP)

A Novel Mathematical Model for Rough Cracks Repairing by Using the Method of Microbially Induced Carbonate Precipitation (MICP)

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