Utilizing contemporary machine learning techniques for determining soilcrete properties
Shranjeno v:
| izdano v: | Earth Science Informatics vol. 18, no. 1 (Jan 2025), p. 176 |
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| Izdano: |
Springer Nature B.V.
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| Teme: | |
| Online dostop: | Citation/Abstract Full Text - PDF |
| Oznake: |
Brez oznak, prvi označite!
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| 001 | 3155469962 | ||
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| 022 | |a 1865-0473 | ||
| 022 | |a 1865-0481 | ||
| 024 | 7 | |a 10.1007/s12145-024-01520-2 |2 doi | |
| 035 | |a 3155469962 | ||
| 045 | 2 | |b d20250101 |b d20250131 | |
| 084 | |a 109043 |2 nlm | ||
| 245 | 1 | |a Utilizing contemporary machine learning techniques for determining soilcrete properties | |
| 260 | |b Springer Nature B.V. |c Jan 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Soilcrete is an innovative construction material made by combining naturally occurring earth materials with cement. It can be effectively used in areas where other construction materials are not readily available due to financial or environmental reasons since soilcrete is made from readily available natural clay. It can also help to cut down the greenhouse gas emissions from the construction industry by encouraging the use of resources that are locally available. Thus, it is imperative to reliably predict different properties of soilcrete since the accurate determination of these properties is crucial for the widespread use of soilcrete materials. However, the laboratory determination of these properties is subjected to significant time and resource constraints. As a result, this research was undertaken to provide empirical prediction models for the density, shrinkage, and strain of soilcrete mixes using two machine learning algorithms: Gene Expression Programming (GEP) and Extreme Gradient Boosting (XGB). The analysis revealed that XGB-based predictions correlated more with real-life values than GEP having training R2=0.999<inline-graphic specific-use="web" mime-subtype="GIF" xlink:href="12145_2024_1520_Article_IEq1.gif" /> for both density and shrinkage prediction and R2=0.944<inline-graphic specific-use="web" mime-subtype="GIF" xlink:href="12145_2024_1520_Article_IEq2.gif" /> for strain prediction. Moreover, several explanatory analyses including individual conditional expectation (ICE) analysis and shapely analysis were done on the XGB model which showed that water-to-binder ratio, metakaolin content, and modulus of elasticity are some of the most important variables for forecasting soilcrete materials properties. Furthermore, an interactive graphical user interface (GUI) has been developed for effective utilization in civil engineering industry to forecast these properties of soilcrete materials. | |
| 653 | |a Mechanical properties | ||
| 653 | |a Material properties | ||
| 653 | |a Construction materials | ||
| 653 | |a Emissions | ||
| 653 | |a Strain analysis | ||
| 653 | |a Civil engineering | ||
| 653 | |a Aggregates | ||
| 653 | |a Graphical user interface | ||
| 653 | |a Greenhouse gases | ||
| 653 | |a Machine learning | ||
| 653 | |a Density | ||
| 653 | |a User interface | ||
| 653 | |a Concrete mixing | ||
| 653 | |a Construction industry | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Modulus of elasticity | ||
| 653 | |a Gene expression | ||
| 653 | |a Prediction models | ||
| 653 | |a Permeability | ||
| 653 | |a Carbon dioxide | ||
| 653 | |a Construction costs | ||
| 653 | |a Algorithms | ||
| 653 | |a Earth science | ||
| 653 | |a Industrial development | ||
| 653 | |a Soil properties | ||
| 653 | |a Cement | ||
| 653 | |a Informatics | ||
| 653 | |a Metakaolin | ||
| 653 | |a Economic | ||
| 653 | |a Environmental | ||
| 773 | 0 | |t Earth Science Informatics |g vol. 18, no. 1 (Jan 2025), p. 176 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3155469962/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3155469962/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |