Performance Evaluation of Concrete Structures Using Crack Repair Methods

Kim, Tae-Kyun; Park, Jong Sup

doi:10.3390/su13063217

Cited by 16 publications

(4 citation statements)

References 19 publications

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“…So far, there are two main types of materials used for crack repair. One is polymer-based repair agents [1][2][3], such as epoxy, polyurethane, polyacrylate, etc. The components of the polymer are transferred into cracks by means of injection or grouting, and in-situ polymerization then occurs inside the crack and the formed polymer filled/repaired the crack.…”

Section: Introductionmentioning

confidence: 99%

Repair of concrete existing micro-cracks by use of microbially induced carbonate precipitation

Wang

Gao

2023

MATEC Web Conf.

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Micro-crack is the start of concrete deterioration and failure, though it does not decrease the bearing capacity in the beginning. In order to prolong the service life of existing concrete structures, it is important to repair the existing micro-cracks before they develop into large cracks, which are much more harmful. Bio-genic CaCO3 from microbially induced carbonate precipitation (MICP) process is proved to be a promising crack repair material due to its environmental friendliness and super compatibility with cementitious matrix. In this study, MICP based repair technology was applied to repair micro-cracks. The influence of crack environment and repair technique on the precipitation of bio-CaCO3 was investigated. Results shown that moderate alkali pH had limited effect on the properties of bio-CaCO3. Bacteria could still induce the CaCO3 precipitation in crack if it was saturated with Ca2+ containing deposition medium. Crack of a width 0.5mm can be fully sealed within 3 days by use of a controlled injection technique.

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Section: Introductionmentioning

confidence: 99%

Repair of concrete existing micro-cracks by use of microbially induced carbonate precipitation

Wang

Gao

2023

MATEC Web Conf.

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“…Concrete is one of the most utilized materials in the construction industry. However, when subjected to tensile stresses [1], temperature fluctuations, multiple loading cycles [2], and chemical and environmental factors [3], concrete crack generation is inevitable. These cracks increase water permeability promoting structural reinforcement corrosion and decreasing structural integrity and concrete strength [4].…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Durian (Durio Zibethinus) Rinds Fiber-Silica Composite as Concrete Additive

Gallawan

Lawagon

2023

Construction Technologies and Architecture

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Natural fiber as a fiber reinforcement enhances the high-performance cement composites' strength, ductility, and durability requirements for a concrete application. This study aims to utilize an indigenous natural fiber-silica composite as an additive to cement. Pre-treated durian fibers extracted from durian rinds (100 mesh) were mixed with sodium metasilicate (Na2SiO3), and the synthesized durian rind fiber-silica composite (DRFC) was utilized as a cement mass replacement (5% w/w) on concrete to test its effect to mechanical properties. SEM-EDX micrographs show that silica has a rough sheet-like morphology similar to DRFC. However, DRFC also contains a rough fibrous structure indicating the uniformly distributed durian rinds fiber (DRF) present in the composite matrix. Additionally, the presence of silica significantly improves the thermal stability of DRF. Results demonstrated that both concrete with DRF and DRFC additives have superior mechanical properties, surpassing the controlled specimens. Hence, the potential application of DRF to concrete demonstrates a viable upcycling route for durian rinds waste.

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“…In this regard, crack is a commonly used indicator to evaluate deterioration significance of structures. [1][2][3] As such, crack detection and classification is of great importance to support prediction of residual lifetime and avoid structural failure. Traditional manual inspections that currently used in practice have the drawbacks of time consuming and labour intensive, and is heavily dependent on the experience of operators.…”

Section: Introductionmentioning

confidence: 99%

Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm

Rashidi

Samali

et al. 2022

Structural Health Monitoring

107

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With the rapid increase of ageing infrastructures worldwide, effective and robust inspection techniques are highly demanding to evaluate structural conditions and residual lifetime. The damages on structural surfaces, for example, spalling, crack, rebar buckling and exposure, are important indicators to assess the structural condition. In fact, several state-of-the-art automated inspection techniques using these indicators have been developed to reduce human-conducted onsite inspection activities. However, the efficiency of these techniques is still required to be improved in terms of accuracy and computational cost. In this study, a vision-based crack diagnosis method is developed using deep convolutional neural network (DCNN) and enhanced chicken swarm algorithm (ECSA). A DCNN model is designed with a deep architecture, consisting of six convolutional layers, two pooling layers and three fully connected layers. To enhance the generalisation capacity of trained model, ECSA is introduced to optimize meta-parameters of the DCNN model. The model is trained and tested using image patches cropped from raw images obtained from damaged concrete samples. Finally, a comparative study on different crack detection techniques is conducted to evaluate performance of the proposed method via a group of statistical evaluation indicators.

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Performance Evaluation of Concrete Structures Using Crack Repair Methods

Cited by 16 publications

References 19 publications

Repair of concrete existing micro-cracks by use of microbially induced carbonate precipitation

Repair of concrete existing micro-cracks by use of microbially induced carbonate precipitation

Synthesis of Durian (Durio Zibethinus) Rinds Fiber-Silica Composite as Concrete Additive

Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm

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