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Open AccessArticle

Feature Importance of Stabilised Rammed Earth Components Affecting the Compressive Strength Calculated with Explainable Artificial Intelligence Tools

1
Faculty of Civil Engineering, Warsaw University of Technology, Al. Armii Ludowej 16, 00-637 Warsaw, Poland
2
Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland
*
Author to whom correspondence should be addressed.
Materials 2020, 13(10), 2317; https://doi.org/10.3390/ma13102317
Received: 5 April 2020 / Revised: 11 May 2020 / Accepted: 12 May 2020 / Published: 18 May 2020
(This article belongs to the Special Issue Modeling of Cementitious Materials and Structures)
Cement-stabilized rammed earth (CSRE) is a sustainable construction material. The use of it allows for economizing on the cost of a structure. These two properties of CSRE are based on the fact that the soil used for the rammed mixture is usually dug close to the construction site, so it has random characteristics. That is the reason for the lack of widely accepted prescriptions for CSRE mixture, which could ascertain high enough compressive strength. Therefore, assessing which components of CSRE have the highest impact on its compressive strength becomes an important issue. There are three machine learning regression tools, i.e., artificial neural networks, decision tree, and random forest, used for predicting the compressive strength based on the relative content of CSRE composites (clay, silt, sand, gravel, cement, and water content). The database consisted of 434 samples of CSRE, which were prepared and crushed for testing purposes. Relatively low prediction errors of aforementioned models allowed for the use of explainable artificial intelligence tools (drop-out loss, mean squared error reduction, accumulated local effect) to rank the influence of the ingredients on the dependent variable—the compressive strength. Consistent results from all above-mentioned methods are discussed and compared to some statistical analysis of selected features. This innovative approach, helpful in designing the construction material is a solid base for reliable conclusions. View Full-Text
Keywords: rammed earth; cement stabilized rammed earth; multivariate regression; random forest; artificial inteligence; features importance ranking rammed earth; cement stabilized rammed earth; multivariate regression; random forest; artificial inteligence; features importance ranking
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Anysz, H.; Brzozowski, Ł.; Kretowicz, W.; Narloch, P. Feature Importance of Stabilised Rammed Earth Components Affecting the Compressive Strength Calculated with Explainable Artificial Intelligence Tools. Materials 2020, 13, 2317.

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