Display options
Cite
Anysz H, Brzozowski Ł, Kretowicz W, et al. Feature Importance of Stabilised Rammed Earth Components Affecting the Compressive Strength Calculated with Explainable Artificial Intelligence Tools. Materials (Basel). 2020;13(10)doi: 10.3390/ma13102317.
Anysz, H., Brzozowski, �. �., Kretowicz, W., & Narloch, P. (2020). Feature Importance of Stabilised Rammed Earth Components Affecting the Compressive Strength Calculated with Explainable Artificial Intelligence Tools. Materials (Basel, Switzerland), 13(10), . https://doi.org/10.3390/ma13102317
Anysz, Hubert, et al. "Feature Importance of Stabilised Rammed Earth Components Affecting the Compressive Strength Calculated with Explainable Artificial Intelligence Tools." Materials (Basel, Switzerland) vol. 13,10 (2020). doi: https://doi.org/10.3390/ma13102317
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 (Basel). 2020 May 18;13(10). doi: 10.3390/ma13102317. PMID: 32443513; PMCID: PMC7288123.
Copy Download .nbib Format: NLM
Share it on

Materials (Basel). 2020 May 18;13(10). doi: 10.3390/ma13102317.

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

Materials (Basel, Switzerland)

Hubert Anysz, Łukasz Brzozowski, Wojciech Kretowicz, Piotr Narloch

Affiliations

  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.

PMID: 32443513 PMCID: PMC7288123 DOI: 10.3390/ma13102317

Abstract

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.

Keywords: artificial inteligence; cement stabilized rammed earth; features importance ranking; multivariate regression; rammed earth; random forest

References

  1. BMC Bioinformatics. 2007 Jan 25;8:25 - PubMed
  2. Materials (Basel). 2014 Dec 23;8(1):1-15 - PubMed
  3. Materials (Basel). 2019 Apr 29;12(9): - PubMed
  4. Materials (Basel). 2020 Jan 10;13(2): - PubMed

Publication Types