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A platform for research: civil engineering, architecture and urbanism
Novel Optical-Inspired Rain Forest for the Explainable Prediction of Geopolymer Concrete Compressive Strength
https://orcid.org/0000-0003-1312-4822
Geopolymer concrete (GPC) is an extraordinary material for promoting sustainable development in the construction industry and reducing environmental risk. However, material properties, such as compressive strength, are commonly determined using laboratory experiments, which are costly and time-consuming to run. Therefore, optical-inspired rain forest (ORF), a sophisticated predictive model, was developed to offer an alternative mathematical solution. The developed model uses a novel mechanism that grows an operation tree into multiple operation forests and employs an optical microscope algorithm to optimize the weight and forest topology. The experimental results indicate that the proposed model outperformed several other popular artificial intelligence approaches, achieving the highest evaluation criteria of and , respectively, for training and testing data sets. Hence, ORF is recommended as a viable tool to assist material engineers to significantly increase the utilization of GPC in construction projects.
Novel Optical-Inspired Rain Forest for the Explainable Prediction of Geopolymer Concrete Compressive Strength
https://orcid.org/0000-0003-1312-4822
Geopolymer concrete (GPC) is an extraordinary material for promoting sustainable development in the construction industry and reducing environmental risk. However, material properties, such as compressive strength, are commonly determined using laboratory experiments, which are costly and time-consuming to run. Therefore, optical-inspired rain forest (ORF), a sophisticated predictive model, was developed to offer an alternative mathematical solution. The developed model uses a novel mechanism that grows an operation tree into multiple operation forests and employs an optical microscope algorithm to optimize the weight and forest topology. The experimental results indicate that the proposed model outperformed several other popular artificial intelligence approaches, achieving the highest evaluation criteria of and , respectively, for training and testing data sets. Hence, ORF is recommended as a viable tool to assist material engineers to significantly increase the utilization of GPC in construction projects.
Novel Optical-Inspired Rain Forest for the Explainable Prediction of Geopolymer Concrete Compressive Strength
https://orcid.org/0000-0003-1312-4822
Geopolymer concrete (GPC) is an extraordinary material for promoting sustainable development in the construction industry and reducing environmental risk. However, material properties, such as compressive strength, are commonly determined using laboratory experiments, which are costly and time-consuming to run. Therefore, optical-inspired rain forest (ORF), a sophisticated predictive model, was developed to offer an alternative mathematical solution. The developed model uses a novel mechanism that grows an operation tree into multiple operation forests and employs an optical microscope algorithm to optimize the weight and forest topology. The experimental results indicate that the proposed model outperformed several other popular artificial intelligence approaches, achieving the highest evaluation criteria of and , respectively, for training and testing data sets. Hence, ORF is recommended as a viable tool to assist material engineers to significantly increase the utilization of GPC in construction projects.
Novel Optical-Inspired Rain Forest for the Explainable Prediction of Geopolymer Concrete Compressive Strength
J. Comput. Civ. Eng.
Cheng, Min-Yuan (author) / Khitam, Akhmad F. K. (author)
2024-11-01
Article (Journal)
Electronic Resource
English
Prediction of Compressive Strength of Alccofine-Based Geopolymer Concrete
| Springer Verlag | 2024
Prediction of Compressive Strength of Alccofine-Based Geopolymer Concrete
| Springer Verlag | 2024