IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/4832864.html
   My bibliography  Save this article

Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil

Author

Listed:
  • Quang Hung Nguyen
  • Hai-Bang Ly
  • Lanh Si Ho
  • Nadhir Al-Ansari
  • Hiep Van Le
  • Van Quan Tran
  • Indra Prakash
  • Binh Thai Pham

Abstract

The main objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Boosting Trees (Boosted) algorithms, considering the influence of various training to testing ratios in predicting the soil shear strength, one of the most critical geotechnical engineering properties in civil engineering design and construction. For this aim, a database of 538 soil samples collected from the Long Phu 1 power plant project, Vietnam, was utilized to generate the datasets for the modeling process. Different ratios (i.e., 10/90, 20/80, 30/70, 40/60, 50/50, 60/40, 70/30, 80/20, and 90/10) were used to divide the datasets into the training and testing datasets for the performance assessment of models. Popular statistical indicators, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient ( R ), were employed to evaluate the predictive capability of the models under different training and testing ratios. Besides, Monte Carlo simulation was simultaneously carried out to evaluate the performance of the proposed models, taking into account the random sampling effect. The results showed that although all three ML models performed well, the ANN was the most accurate and statistically stable model after 1000 Monte Carlo simulations (Mean R = 0.9348) compared with other models such as Boosted (Mean R = 0.9192) and ELM (Mean R = 0.8703). Investigation on the performance of the models showed that the predictive capability of the ML models was greatly affected by the training/testing ratios, where the 70/30 one presented the best performance of the models. Concisely, the results presented herein showed an effective manner in selecting the appropriate ratios of datasets and the best ML model to predict the soil shear strength accurately, which would be helpful in the design and engineering phases of construction projects.

Suggested Citation

  • 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.
  • Handle: RePEc:hin:jnlmpe:4832864
    DOI: 10.1155/2021/4832864
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2021/4832864.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/MPE/2021/4832864.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2021/4832864?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. 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.

    More about this item

    Statistics

    Access and download statistics

    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:hin:jnlmpe:4832864. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.