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Silica fume as a supplementary cementitious material in pervious concrete: prediction of compressive strength through a machine learning approach
https://orcid.org/http://orcid.org/0000-0001-8570-0580
https://orcid.org/http://orcid.org/0000-0003-0476-3964
https://orcid.org/http://orcid.org/0000-0002-1023-1617
Utilizing silica fume as a substitute for cement in pervious concrete offers a viable approach to achieve sustainability within the realm of construction industry. The mechanical characteristics of pervious concrete are influenced by several factors, such as quantity of silica fume utilized as a replacement for cement, the cement content, coarse aggregate, sand, admixture and water used in the mix, aggregate size and the curing period. The present study introduces a predictive model that utilizes machine learning approaches to estimate the compressive strength of pervious concrete blended with silica fume. The models underwent training and testing procedures using 222 datasets from various literature sources. In this study, seven machine learning algorithms were used as statistical evaluation methods to identify the most suitable and reliable model for predicting compressive strength of pervious concrete. Among several models under consideration, the eXtreme Gradient Boosting model showed superior performance in forecasting compressive strength of pervious concrete. The coefficient of determination value obtained for training data is almost one, which suggests a robust correlation between the anticipated and actual values. The root mean squared error of training data is 0.28 MPa, which indicates the mean variation between the predicted and observed values. The coefficient of determination value for the test datasets is 0.97, along with a root mean squared error of 2.21 MPa. The outcomes of the sensitivity analysis conducted on the eXtreme Gradient Boosting model indicate that the parameter with the most significant impact on predicting the compressive strength of pervious concrete is the admixture content, followed by the curing period. This work provides a comprehensive evaluation of the compressive strength of pervious concrete, thereby enhancing the existing knowledge and facilitating its practical application in this domain.
Silica fume as a supplementary cementitious material in pervious concrete: prediction of compressive strength through a machine learning approach
https://orcid.org/http://orcid.org/0000-0001-8570-0580
https://orcid.org/http://orcid.org/0000-0003-0476-3964
https://orcid.org/http://orcid.org/0000-0002-1023-1617
Utilizing silica fume as a substitute for cement in pervious concrete offers a viable approach to achieve sustainability within the realm of construction industry. The mechanical characteristics of pervious concrete are influenced by several factors, such as quantity of silica fume utilized as a replacement for cement, the cement content, coarse aggregate, sand, admixture and water used in the mix, aggregate size and the curing period. The present study introduces a predictive model that utilizes machine learning approaches to estimate the compressive strength of pervious concrete blended with silica fume. The models underwent training and testing procedures using 222 datasets from various literature sources. In this study, seven machine learning algorithms were used as statistical evaluation methods to identify the most suitable and reliable model for predicting compressive strength of pervious concrete. Among several models under consideration, the eXtreme Gradient Boosting model showed superior performance in forecasting compressive strength of pervious concrete. The coefficient of determination value obtained for training data is almost one, which suggests a robust correlation between the anticipated and actual values. The root mean squared error of training data is 0.28 MPa, which indicates the mean variation between the predicted and observed values. The coefficient of determination value for the test datasets is 0.97, along with a root mean squared error of 2.21 MPa. The outcomes of the sensitivity analysis conducted on the eXtreme Gradient Boosting model indicate that the parameter with the most significant impact on predicting the compressive strength of pervious concrete is the admixture content, followed by the curing period. This work provides a comprehensive evaluation of the compressive strength of pervious concrete, thereby enhancing the existing knowledge and facilitating its practical application in this domain.
Silica fume as a supplementary cementitious material in pervious concrete: prediction of compressive strength through a machine learning approach
https://orcid.org/http://orcid.org/0000-0001-8570-0580
https://orcid.org/http://orcid.org/0000-0003-0476-3964
https://orcid.org/http://orcid.org/0000-0002-1023-1617
Utilizing silica fume as a substitute for cement in pervious concrete offers a viable approach to achieve sustainability within the realm of construction industry. The mechanical characteristics of pervious concrete are influenced by several factors, such as quantity of silica fume utilized as a replacement for cement, the cement content, coarse aggregate, sand, admixture and water used in the mix, aggregate size and the curing period. The present study introduces a predictive model that utilizes machine learning approaches to estimate the compressive strength of pervious concrete blended with silica fume. The models underwent training and testing procedures using 222 datasets from various literature sources. In this study, seven machine learning algorithms were used as statistical evaluation methods to identify the most suitable and reliable model for predicting compressive strength of pervious concrete. Among several models under consideration, the eXtreme Gradient Boosting model showed superior performance in forecasting compressive strength of pervious concrete. The coefficient of determination value obtained for training data is almost one, which suggests a robust correlation between the anticipated and actual values. The root mean squared error of training data is 0.28 MPa, which indicates the mean variation between the predicted and observed values. The coefficient of determination value for the test datasets is 0.97, along with a root mean squared error of 2.21 MPa. The outcomes of the sensitivity analysis conducted on the eXtreme Gradient Boosting model indicate that the parameter with the most significant impact on predicting the compressive strength of pervious concrete is the admixture content, followed by the curing period. This work provides a comprehensive evaluation of the compressive strength of pervious concrete, thereby enhancing the existing knowledge and facilitating its practical application in this domain.
Silica fume as a supplementary cementitious material in pervious concrete: prediction of compressive strength through a machine learning approach
Asian J Civ Eng
Sathiparan, Navaratnarajah (author) / Jeyananthan, Pratheeba (author) / Subramaniam, Daniel Niruban (author)
Asian Journal of Civil Engineering ; 25 ; 2963-2977
2024-04-01
15 pages
Article (Journal)
Electronic Resource
English
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