Self-healing of Concrete Cracks by Immobilizing Microorganisms in Recycled Aggregate

Liu, Chao; Xu, Xiaoyu; Lv, Zijian; Xing, Lu

doi:10.3151/jact.18.168

Cited by 48 publications

(17 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A total of 797 data sets employed for predicting the HP of BSHC were collected from 14 articles published between 2000 and 2021 [18,[23][24][25][26][27][28][29][30][31][32][33][34][35]. As mentioned in Section 1.3, 22 variables influencing the HP of BSHC are employed in this paper to train ML models with the five algorithms.…”

Section: Data Preparationmentioning

confidence: 99%

Self-Healing Performance Assessment of Bacterial-Based Concrete Using Machine Learning Approaches

et al. 2022

View full text Add to dashboard Cite

Bacterial-based self-healing concrete (BSHC) is a well-known healing technology which has been investigated for a few decades for its excellent crack healing capacity. Nevertheless, considered as costly and time-consuming, the healing performance (HP) of concrete with various types of bacteria can be designed and evaluated only in laboratory environments. Employing machine learning (ML) models for predicting the HP of BSHC is inspired by practical applications using concrete mechanical properties. The HP of BSHC can be predicted to save the time and cost of laboratory tests, bacteria selection and healing mechanisms adoption. In this paper, three types of BSHC, including ureolytic bacterial healing concrete (UBHC), aerobic bacterial healing concrete (ABHC) and nitrifying bacterial healing concrete (NBHC), and ML models with five kinds of algorithms consisting of the support vector regression (SVR), decision tree regression (DTR), deep neural network (DNN), gradient boosting regression (GBR) and random forest (RF) are established. Most importantly, 22 influencing factors are first employed as variables in the ML models to predict the HP of BSHC. A total of 797 sets of BSHC tests available in the open literature between 2000 and 2021 are collected to verify the ML models. The grid search algorithm (GSA) is also utilised for tuning parameters of the algorithms. Moreover, the coefficient of determination (R2) and root mean square error (RMSE) are applied to evaluate the prediction ability, including the prediction performance and accuracy of the ML models. The results exhibit that the GBR model has better prediction ability (R2GBR = 0.956, RMSEGBR = 6.756%) than other ML models. Finally, the influence of the variables on the HP is investigated by employing the sensitivity analysis in the GBR model.

show abstract

Section: Data Preparationmentioning

confidence: 99%

Self-Healing Performance Assessment of Bacterial-Based Concrete Using Machine Learning Approaches

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The purpose of this paper is to monitor various problems such as concrete cracks, and to study the concrete management and monitoring system based on particle swarm algorithm. By measuring the concrete crack width, the machine vision measurement value of the concrete crack width under different shooting distance conditions is obtained, and the actual value of the concrete crack width is compared with the machine vision measurement value, and the absolute and relative errors of the measurement results are obtained, which verifies this article Establish the accuracy and practicality of the system [5][6].…”

Section: Introductionmentioning

confidence: 53%

“…According to W1 and W2, calculate the absolute error Eabs of the crack width. The absolute error is calculated according to formula (6). The calculation results are shown in Table 2.…”

Section: Absolute Error Analysis Of Cracksmentioning

confidence: 99%

Concrete Management Monitoring System Based on Particle Swarm Algorithm

Pan

2021

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

With the rapid development of the national construction infrastructure industry, concrete structures often have various problems such as cracks in the early and later operations. The further development of these problems may endanger the structural safety of the entire building. The particle swarm algorithm is simple to operate, less parameter settings, easy to implement, etc., which can solve the problem of concrete monitoring. Based on this, the purpose of this article is to study the concrete management monitoring system based on particle swarm optimization. This article first summarizes the basic theory of particle swarm algorithm, and then extends its core algorithm technology. Analyze the existing problems and deficiencies in the current concrete structures in our country. And use particle swarm algorithm to research and analyze the concrete management monitoring system. This paper systematically describes the outline design and detailed design of the concrete management monitoring system based on particle swarm optimization. And by comparative method, field survey method and other research forms to carry out experimental research on the main body of this article. Research shows that the maximum absolute error of the crack width measured by the particle swarm optimization-based concrete management monitoring system W2 and the actual crack width W1 measured by the crack width comparison ruler is 0.232mm, which is less than 0.3mm, and the maximum relative error is 4.59%, to meet the system design requirements, which proves the rationality and effectiveness of the system.

show abstract

“…There are two ways to induce calcium carbonate precipitation by microorganisms: the first is the self-healing condition formed by aerobic microorganism respiration; the second is enzymatic reaction by anaerobic microorganism [4]. And this paper mainly studies the second way, through the microbial microcapsules into concrete, microorganisms in the absence of cracks, has been in a dormant state.…”

Section: Introductionmentioning

confidence: 99%

Investigation on mineralization performance and spore germination conditions of calcium carbonate mineralizing bacteria

Hu¹,

Liu²,

Zhang³

et al. 2022

Mater. Res. Express

View full text Add to dashboard Cite

Microbial induced calcium carbonate precipitation (MICP) provides a theoretical basis for repairing underground rocks. The optimal germination conditions and mineralization activities of Bacillus pasteurii with crack repair function were investigated. The growth and mineralization properties of microorganisms in different environments were explored by measuring the urease activity and calcium carbonate production of bacteria, and the influencing factors of bacterial biomass and urease activity were analyzed. The microscopic morphology of bacterial mineralization products was analyzed by SEM, XRD and TG-DSC test. The influencing factors of spore germination rate were studied by measuring the OD value of spore germination. The results showed that under the conditions of pH 7.3, urea concentration 0.25–0.5 mol l−1, and calcium ion concentration 0.4–0.6 mol l−1, the mineralization activity of B. pasteurii was the strongest, and the generated mineralization product was a stable calcite crystal. The optimum concentration of germination agent and inorganic salt were 10 mmol l−1 and 200 mmol l−1, respectively.

show abstract

Self-healing of Concrete Cracks by Immobilizing Microorganisms in Recycled Aggregate

Cited by 48 publications

References 40 publications

Self-Healing Performance Assessment of Bacterial-Based Concrete Using Machine Learning Approaches

Self-Healing Performance Assessment of Bacterial-Based Concrete Using Machine Learning Approaches

Concrete Management Monitoring System Based on Particle Swarm Algorithm

Investigation on mineralization performance and spore germination conditions of calcium carbonate mineralizing bacteria

Contact Info

Product

Resources

About