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Kumar, Pramod, Sharma, Sanjay, PRATAP, BHEEM. (1403). Prediction of Compressive Strength of Geopolymer Fiber Reinforced Concrete Using Machine Learning. , (), -. doi: 10.22059/ceij.2024.364871.1956
Pramod Kumar; Sanjay Sharma; BHEEM PRATAP. "Prediction of Compressive Strength of Geopolymer Fiber Reinforced Concrete Using Machine Learning". , , , 1403, -. doi: 10.22059/ceij.2024.364871.1956
Kumar, Pramod, Sharma, Sanjay, PRATAP, BHEEM. (1403). 'Prediction of Compressive Strength of Geopolymer Fiber Reinforced Concrete Using Machine Learning', , (), pp. -. doi: 10.22059/ceij.2024.364871.1956
Kumar, Pramod, Sharma, Sanjay, PRATAP, BHEEM. Prediction of Compressive Strength of Geopolymer Fiber Reinforced Concrete Using Machine Learning. , 1403; (): -. doi: 10.22059/ceij.2024.364871.1956

Prediction of Compressive Strength of Geopolymer Fiber Reinforced Concrete Using Machine Learning

Civil Engineering Infrastructures Journal
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 04 اردیبهشت 1403    اصل مقاله (690.73 K)
شناسه دیجیتال (DOI): 10.22059/ceij.2024.364871.1956
نویسندگان
Pramod Kumar1؛ Sanjay Sharma2؛ BHEEM PRATAP* 3
1Department of Civil Engineering, Mohan Babu University (SVEC), Tirupati 517102, Andhra Pradesh, India
2Department of Civil Engineering, National Institute of Technology Jamshedpur, Jharkhand, India-831014
3Department of Civil Engineering, Graphic Era (Deemed to be University), Dehradun, Uttarakhand-248002, India.
چکیده
Geopolymers represent a cutting-edge class of inorganic materials that provide a sustainable substitute for conventional cement and concrete. Through meticulous combinations and ratios of elements like fly ash (FA), silica fume, ground granulated blast slag (GGBS), alkaline solutions, aggregates, superplasticizers, and fibers, geopolymer concrete mixes are generated as part of the experimental program. The investigation concentrates on the prediction of the 28-day compressive strength, a pivotal parameter in assessing concrete performance. In total, the dataset employed comprises 96 data points, two advanced techniques, namely Support Vector Regression (SVR) and Artificial Neural Networks (ANN), are harnessed for this research. The ANN demonstrates an R2 value of 0.992 on the training dataset, indicating its capacity to elucidate around 99.2% of the variability. On the other hand, SVR boasts an R2 value of 0.995, signifying an ability to account for about 99.5% of the variance. When applied to the testing data, the ANN achieves an R2 of 0.96, while SVR attains an R2 of 0.99. This study suggests that SVR exhibits slightly superior performance in terms of elucidating variance within the testing dataset.
کلیدواژه‌ها
ANN؛ Fly ash؛ GGBS؛ Soft computing
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