IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i3p2374-d1049347.html
   My bibliography  Save this article

Use of Machine Learning Techniques in Soil Classification

Author

Listed:
  • Yaren Aydın

    (Department of Civil Engineering, Istanbul University-Cerrahpaşa, 34320 Istanbul, Turkey)

  • Ümit Işıkdağ

    (Department of Informatics, Mimar Sinan Fine Arts University, 34427 Istanbul, Turkey)

  • Gebrail Bekdaş

    (Department of Civil Engineering, Istanbul University-Cerrahpaşa, 34320 Istanbul, Turkey)

  • Sinan Melih Nigdeli

    (Department of Civil Engineering, Istanbul University-Cerrahpaşa, 34320 Istanbul, Turkey)

  • Zong Woo Geem

    (Department of Smart City & Energy, Gachon University, Seongnam 13120, Republic of Korea)

Abstract

In the design of reliable structures, the soil classification process is the first step, which involves costly and time-consuming work including laboratory tests. Machine learning (ML), which has wide use in many scientific fields, can be utilized for facilitating soil classification. This study aims to provide a concrete example of the use of ML for soil classification. The dataset of the study comprises 805 soil samples based on the soil drillings of the new Gayrettepe–Istanbul Airport metro line construction. The dataset has both missing data and class imbalance. In the data preprocessing stage, first, data imputation techniques were applied to deal with the missing data. Two different imputation techniques were tested, and finally, the data were imputed with the KNN imputer. Later, a balance was achieved with the synthetic minority oversampling technique (SMOTE). After the preprocessing, a series of ML algorithms were tested with 10-fold cross-validation. Unlike the studies conducted in previous research, new gradient-boosting methods such as XGBoost, LightGBM, and CatBoost were tested, high classification accuracy rates of up to +90% were observed, and a significant improvement in the accuracy of prediction (when compared with previous research) was achieved.

Suggested Citation

  • Yaren Aydın & Ümit Işıkdağ & Gebrail Bekdaş & Sinan Melih Nigdeli & Zong Woo Geem, 2023. "Use of Machine Learning Techniques in Soil Classification," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2374-:d:1049347
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/3/2374/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/3/2374/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Quang Hung Nguyen & Hai-Bang Ly & Lanh Si Ho & Nadhir Al-Ansari & Hiep Van Le & Van Quan Tran & Indra Prakash & Binh Thai Pham, 2021. "Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-15, February.
    2. Celal Cakiroglu & Gebrail Bekdaş & Sanghun Kim & Zong Woo Geem, 2022. "Explainable Ensemble Learning Models for the Rheological Properties of Self-Compacting Concrete," Sustainability, MDPI, vol. 14(21), pages 1-24, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kim, Jongkyum & Lim, Jee-Hae & Yoon, Kyunghee, 2022. "How do the content, format, and tone of Twitter-based corporate disclosure vary depending on earnings performance?," International Journal of Accounting Information Systems, Elsevier, vol. 47(C).
    2. Zhang, Jianhong & van Witteloostuijn, Arjen & Zhou, Chaohong & Zhou, Shengyang, 2024. "Cross-border acquisition completion by emerging market MNEs revisited: Inductive evidence from a machine learning analysis," Journal of World Business, Elsevier, vol. 59(2).
    3. Matthew Oyeleye & Tianhua Chen & Sofya Titarenko & Grigoris Antoniou, 2022. "A Predictive Analysis of Heart Rates Using Machine Learning Techniques," IJERPH, MDPI, vol. 19(4), pages 1-14, February.
    4. Madhusmita Das & Rasmita Dash & Sambit Kumar Mishra, 2023. "Automatic Detection of Oral Squamous Cell Carcinoma from Histopathological Images of Oral Mucosa Using Deep Convolutional Neural Network," IJERPH, MDPI, vol. 20(3), pages 1-21, January.
    5. Celal Cakiroglu & Gebrail Bekdaş, 2023. "Predictive Modeling of Recycled Aggregate Concrete Beam Shear Strength Using Explainable Ensemble Learning Methods," Sustainability, MDPI, vol. 15(6), pages 1-21, March.
    6. Xinguo Miao & Lei Liu & Zhiyong Wang & Xiaoming Chen, 2024. "Improved Error-Based Ensemble Learning Model for Compressor Performance Parameter Prediction," Energies, MDPI, vol. 17(9), pages 1-12, April.
    7. Mohamed Zul Fadhli Khairuddin & Puat Lu Hui & Khairunnisa Hasikin & Nasrul Anuar Abd Razak & Khin Wee Lai & Ahmad Shakir Mohd Saudi & Siti Salwa Ibrahim, 2022. "Occupational Injury Risk Mitigation: Machine Learning Approach and Feature Optimization for Smart Workplace Surveillance," IJERPH, MDPI, vol. 19(21), pages 1-19, October.
    8. Celal Cakiroglu & Gebrail Bekdaş & Sanghun Kim & Zong Woo Geem, 2022. "Explainable Ensemble Learning Models for the Rheological Properties of Self-Compacting Concrete," Sustainability, MDPI, vol. 14(21), pages 1-24, November.
    9. Van Quan Tran, 2023. "Using Artificial Intelligence Approach for Investigating and Predicting Yield Stress of Cemented Paste Backfill," Sustainability, MDPI, vol. 15(4), pages 1-22, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2374-:d:1049347. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.