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커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.21 No.2
- 발행연도
- 2021.6
- 수록면
- 176 - 188 (13page)
- DOI
- 10.5391/IJFIS.2021.21.2.176
이용수
초록· 키워드
오류제보하기
The objectives of this study are to develop a model for predicting the compressive strength of concrete using an adaptive neuro-fuzzy inference system (ANFIS) and validate the mix proportion using artificial neural networks (ANNs) and by experimentation. A model was developed, and the compressive strength was predicted using the ANFIS (with the subtractive clustering method of the fuzzy inference system) by MATLAB programming. In the present study, two ANFIS models were considered: ANFIS models-1 and -2. ANFIS model-1 was developed to predict the 3-day compressive strength, whereas ANFIS model-2 predicts the 28-day compressive strength by considering the 3-day compressive strength data obtained using ANFIS model-1. It was observed that the errors in the 3- and 28-day compressive strengths were 6.33%, and 17.07%, respectively. Furthermore, experiments were performed for selective mixes-M40, M50, and M60-to verify the compressive strength obtained using the ANFIS model. The model results were verified against the experimental ones based on the mixes selected from the model, and the results were found to agree with the predicted ones, with a maximum deviation of 18%. Furthermore, an ANN model was developed to predict the compressive strength to verify the accuracy of the ANFIS model. The results predicted by the ANFIS and the ANN were compared with the original results available in the literature. A significant deviation was found between the ANN model results and the original results, however, the ANN model results presented the same trend as the original results. It was concluded that the ANFIS model results were highly consistent with the original results.
목차
Abstract
1. Introduction
2. Prediction of Compressive Strength using ANFIS Model
3. Prediction of Compressive Strength using ANN Model
4. Comparison of ANFIS Results and Experiment
5. Comparison of Original, ANN, and ANFIS Results
6. Conclusion
References
참고문헌 (25)
참고문헌 신청
A. Kabir / 2012 / Predicting 28 days compressive strength of concrete from 7 days test result / Proceedings of the International Conference on Advances in Design and Construction of Structures :18~22
J. D. Supe / 2014 / Predictive model of compressive strength for concrete in-situ / International Journal of Structural and Civil Engineering Research 3(1):90~102
M. Viviani / 2008 / Equivalency points: predicting concrete compressive strength evolution in three days / Cement and Concrete Research 38(8-9):1070~1078
M. Akram / 2014 / A review on expert system and its applications in civil engineering / International Journal of Civil Engineering and Built Environment 1(1):24~29
A. Abraham / 2005 / Handbook of Measuring System Design / John Wiley &Sons
J. D. Supe / 2014 / Predictive model of compressive strength for concrete in-situ / International Journal of Structural and Civil Engineering Research 3(1):90~102
M. Viviani / 2008 / Equivalency points: predicting concrete compressive strength evolution in three days / Cement and Concrete Research 38(8-9):1070~1078
M. Akram / 2014 / A review on expert system and its applications in civil engineering / International Journal of Civil Engineering and Built Environment 1(1):24~29
A. Abraham / 2005 / Handbook of Measuring System Design / John Wiley &Sons
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