Prediction of cement-based mortars compressive strength using machine learning techniques

Asteris, Panagiotis G. and Koopialipoor, Mohammadreza and Armaghani, Danial J. and Kotsonis, Evgenios A. and Lourenco, Paulo B. (2021) Prediction of cement-based mortars compressive strength using machine learning techniques. Neural Computing and Applications, 33 (19). pp. 13089-13121. ISSN 0941-0643, DOI

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The application of artificial neural networks in mapping the mechanical characteristics of the cement-based materials is underlined in previous investigations. However, this machine learning technique includes several major deficiencies highlighted in the literature, such as the overfitting problem and the inability to explain the decisions. Hence, the present study investigates the applicability of other common machine learning techniques, i.e., support vector machine, random forest (RF), decision tree, AdaBoost and k-nearest neighbors in mapping the behavior of the compressive strength (CS) of cement-based mortars. To this end, a big experimental database has been compiled based on experimental data available in the literature considering, namely the cement grade, which is an important parameter for the modeling of mortar's CS. Other important parameters are namely the age, the water-to-binder ratio, the particle size distribution of the sand and the amount of plasticizer. Many models based on the influential factors affecting machine learning techniques have been developed, and their prediction capacities have been assessed using performance indexes. The present research highlights the potential of AdaBoost and RF models as useful tools which can assist in mortar design and/or optimization. In addition, mapping the development of mortar characteristics can assist in revealing the influence of the different mortar mix parameters on the compressive strength.

Item Type: Article
Uncontrolled Keywords: Predictive models; Cement; Compressive strength; Machine learning; Metakaolin; Mortar
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering > Department of Civil Engineering
Depositing User: Ms Zaharah Ramly
Date Deposited: 18 Jul 2022 07:51
Last Modified: 18 Jul 2022 07:51

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