A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Compressive strength Prediction recycled aggregate incorporated concrete using Adaptive Neuro-Fuzzy System and Multiple Linear Regression
Compressive strength of concrete, renowned as one of the most substantial mechanical properties of concrete and key factors for the quality assurance of concrete. In the present study, two different data-driven models, i.e., Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR) were used to predict the 28 days compressive strength of recycled aggregate concrete (RAC). 16 different input parameters, including both dimensional and non-dimensional parameters, were used for predicting the 28 days compressive strength of concrete. The present study established that estimation of 28 days compressive strength of recycled aggregate concrete was performed better by ANFIS in comparison to MLR. Besides, the performance of data-driven models with and without the non-dimensional parameters is explored. It was observed that the data-driven models show better accuracy when the non-dimensional parameters were used as additional input parameters. Furthermore, the effect of each non-dimensional parameter on the performance of each data-driven model is investigated and 28 days compressive strength of concrete is examined.
Compressive strength Prediction recycled aggregate incorporated concrete using Adaptive Neuro-Fuzzy System and Multiple Linear Regression
Compressive strength of concrete, renowned as one of the most substantial mechanical properties of concrete and key factors for the quality assurance of concrete. In the present study, two different data-driven models, i.e., Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR) were used to predict the 28 days compressive strength of recycled aggregate concrete (RAC). 16 different input parameters, including both dimensional and non-dimensional parameters, were used for predicting the 28 days compressive strength of concrete. The present study established that estimation of 28 days compressive strength of recycled aggregate concrete was performed better by ANFIS in comparison to MLR. Besides, the performance of data-driven models with and without the non-dimensional parameters is explored. It was observed that the data-driven models show better accuracy when the non-dimensional parameters were used as additional input parameters. Furthermore, the effect of each non-dimensional parameter on the performance of each data-driven model is investigated and 28 days compressive strength of concrete is examined.
Compressive strength Prediction recycled aggregate incorporated concrete using Adaptive Neuro-Fuzzy System and Multiple Linear Regression
Falade, Funso (author) / Iqbal, Taim (author)
2019-05-30
doi:10.34256/ijceae1913
International Journal of Civil, Environmental and Agricultural Engineering; Volume 1, Issue 1, Year 2019; 19-24 ; 2582-2721 ; 10.34256/ijceae191
Article (Journal)
Electronic Resource
English
DDC:
690
| British Library Online Contents | 2017
Predicting Compressive Strength of Recycled Aggregate Concrete by Multiple Regression Analysis
| British Library Conference Proceedings | 2013
Improving compressive strength for recycled aggregate concrete
| British Library Conference Proceedings | 2004
| British Library Online Contents | 2015