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Computation of Compressive Strength of GGBS Mixed Concrete using Machine Learning
Concrete is a composite material formed by cement, water, and aggregate. Concrete is an important material for any Civil Engineering project. Several concretes are produced as per the functional requirements using waste materials or byproducts. Many researchers reported that these waste materials or by-products enhance the concrete properties, but the laboratory procedures for determining the concrete properties are time-consuming. Therefore, numerous researchers used statistical and artificial intelligence methods for predicting concrete properties. In the present research work, the compressive strength of GGBS mixed concrete is computed using AI technologies, namely Regression Analysis (RA), Support Vector Machine (SVM), Decision Tree (DT), and Artificial Neural Networks (ANNs). The cement content (CC), C/F ratio, w/c ratio, GGBS (in Kg & %), admixture, and age (days) are selected as input parameters to construct the RA, SVM, DT, ANNs models for computing the compressive strength of GGBS mixed concrete. The CS_MLR, Link_CS_SVM, 20LF_CS_DT, and GDM_CS_ANN models are identified as the best architectural AI models based on the performance of AI models. The performance of the best architectural AI models is compared to determine the optimum performance model. The correlation coefficient is computed for input and output variables. The compressive strength of GGBS mixed concrete is highly influenced by age (curing days). Comparing the performance of optimum performance AI models and models available in the literature study shows that the optimum performance AI model outperformed the published models.
Computation of Compressive Strength of GGBS Mixed Concrete using Machine Learning
Concrete is a composite material formed by cement, water, and aggregate. Concrete is an important material for any Civil Engineering project. Several concretes are produced as per the functional requirements using waste materials or byproducts. Many researchers reported that these waste materials or by-products enhance the concrete properties, but the laboratory procedures for determining the concrete properties are time-consuming. Therefore, numerous researchers used statistical and artificial intelligence methods for predicting concrete properties. In the present research work, the compressive strength of GGBS mixed concrete is computed using AI technologies, namely Regression Analysis (RA), Support Vector Machine (SVM), Decision Tree (DT), and Artificial Neural Networks (ANNs). The cement content (CC), C/F ratio, w/c ratio, GGBS (in Kg & %), admixture, and age (days) are selected as input parameters to construct the RA, SVM, DT, ANNs models for computing the compressive strength of GGBS mixed concrete. The CS_MLR, Link_CS_SVM, 20LF_CS_DT, and GDM_CS_ANN models are identified as the best architectural AI models based on the performance of AI models. The performance of the best architectural AI models is compared to determine the optimum performance model. The correlation coefficient is computed for input and output variables. The compressive strength of GGBS mixed concrete is highly influenced by age (curing days). Comparing the performance of optimum performance AI models and models available in the literature study shows that the optimum performance AI model outperformed the published models.
Computation of Compressive Strength of GGBS Mixed Concrete using Machine Learning
Swati, (author) / Jitendra Khatti (author) / Kamaldeep Singh Grover (author) / Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
2021-11-30
oai:zenodo.org:5731368
International Journal of Recent Technology and Engineering (IJRTE) 10(4) 241-250
Article (Journal)
Electronic Resource
English
DDC:
690
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