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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제구조공학회 Smart Structures and Systems, An International Journal Smart Structures and Systems, An International Journal Vol.28 No.4
- 발행연도
- 2021.10
- 수록면
- 535 - 551 (17page)
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초록· 키워드
오류제보하기
In recent years, bacteria-based self-healing concrete has been widely exploited to improve the compressive strength of concrete using different bacterial species. However, both the identification of the optimal involved reaction parameters and theoretical framework information are still limited. In the present study, both experimentally and numerical modelling using machine learning (ANN and ANFIS) and response surface methodology (RSM) were implemented to evaluate and optimse the evolution of bacterial concrete strength. Therefore, a total of 58 compressive strength tests of the concrete incorporating new bacterial species were designed using different concentrations of urea, cells concentration, calcium, nutrient and time. Based on the results, the compressive strength of the bacterial concrete improved by 16% due to the decrement of the pore percentage in the concrete skin; specifically, 5 mm from the concrete surface, compared to that of the control concrete. In the same context, both machine the learning and RSM models indicated that the optimal range of urea, calcium, nutrient and bacterial cells were (18-23 g/L), (150-350 mM), (1-3 g/L) and 2×107 cells/mL, respectively. Based on the statistical analysis, RMSE, <i>R</i><sup>2</sup>, MPE, RAE and RRSE were (0.793, 0.785), (0.985, 0.986), (1.508, 1.1), (0.11, 0.09) and (0.121, 0.12) from both the ANN and ANFIS models, respectively, while; the following values (0.839, 0.972, 1.678, 0.131 and 0.165) was obtained from RSM model, respectively. As such, it can be concluded that a high correlation and minimum error were obtained, however, machine learning models provided more accurate results compared to that of the RSM model.
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참고문헌 (71)
참고문헌 신청
Abdulhameed U. Abubakar / 2018 / P-value significance level test for high-performance steel fiber concrete (HPSFC) / Computers and Concrete, An International Journal 21(5):485~493
Tawfiq Al-Mughanam / 2020 / Modeling of Compressive Strength of Sustainable Self-Compacting Concrete Incorporating Treated Palm Oil Fuel Ash Using Artificial Neural Network / Sustainability 12(22):9322
Hisham Alabduljabbar / 2020 / Engineering Properties of Waste Sawdust-Based Lightweight Alkali-Activated Concrete: Experimental Assessment and Numerical Prediction / Materials 13(23):5490
Hassan Amer Algaifi / 2018 / Numerical modeling for crack self-healing concrete by microbial calcium carbonate / Construction and Building Materials 189:816~824
Hassan Amer Algaifi / 2020 / Insight into the role of microbial calcium carbonate and the factors involved in self-healing concrete / Construction and Building Materials 254:119258
Tawfiq Al-Mughanam / 2020 / Modeling of Compressive Strength of Sustainable Self-Compacting Concrete Incorporating Treated Palm Oil Fuel Ash Using Artificial Neural Network / Sustainability 12(22):9322
Hisham Alabduljabbar / 2020 / Engineering Properties of Waste Sawdust-Based Lightweight Alkali-Activated Concrete: Experimental Assessment and Numerical Prediction / Materials 13(23):5490
Hassan Amer Algaifi / 2018 / Numerical modeling for crack self-healing concrete by microbial calcium carbonate / Construction and Building Materials 189:816~824
Hassan Amer Algaifi / 2020 / Insight into the role of microbial calcium carbonate and the factors involved in self-healing concrete / Construction and Building Materials 254:119258
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