![]() This study assessed the strength and durability parameters of brick aggregate concrete (BAC) incorporating rice husk ash (RHA) as a partial replacement of cement. This research advances the capabilities of understanding the underlying reactions for complex concrete mixtures and designing low-carbon cost-effective concrete. The proposed approach has been implemented to predict the compressive strength of complex concrete mixtures, assess the importance of variables, and discover chemical reactions, showing high accuracy and high generalizability. Specifically, this research creates an artificial language to represent concrete mixtures and the physicochemical information of their ingredients, develops a feature extraction method based on character-level N-grams, and proposes a method to configure deep learning models automatically. ![]() This paper presents an approach to discover the intrinsic relationships between the physicochemical properties of the ingredients and mechanical properties of concrete. The fillers were limestone powder (Yu et al., 2015 Huang et al., 2017 Kang et al., 2019 Wang et al., 2022), red mud (Li and Zhang, 2022), and quartz powder (Liu and Wei, 2021 Kang et al., 2019 Van et al., 2014).Įxisting machine learning-based approaches to investigate and design concrete mainly use the mixture design variables to predict concrete properties and do not consider the physicochemical properties of ingredients such as the particle size distribution and chemical composition of various binders and aggregates. The included SCMs are different fly ash (Yu et al., 2015 Jaturapitakkul et al., 2004 Shi et al., 2021 Li and Zhang, 2022 Hasnat and Ghafoori, 2021), silica fume (Liu and Wei, 2021 Pezeshkian et al., 2021 Huang et al., 2017 Kang et al., 2019 Jaturapitakkul et al., 2004 Shi et al., 2021 Hasnat and Ghafoori, 2021 Wang et al., 2022 Van et al., 2014), nano silica (Yu et al., 2015 Shi et al., 2021), natural zeolite (Pezeshkian et al., 2021), natural pozzolan (Hasnat and Ghafoori, 2021), slag (Yu et al., 2015 Shi et al., 2021 Zhan et al., 2021 Li and Zhang, 2022 Hasnat and Ghafoori, 2021 Van et al., 2014), metakaolin (Zhan et al., 2021), glass powder (Zhan et al., 2021), rice husk ash (Van et al., 2014 Jamil et al., 2016), and recycled concrete powder (Horsakulthai, 2021). ![]() This dataset includes 15 types of cement, 37 types of SCMs, 8 types of fillers, and 14 types of aggregates, which had different particle sizes and chemical compositions. ![]()
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