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Slope Stability Classification Model Based on Single-Valued Neutrosophic Matrix Energy and Its Application Under a Single-Valued Neutrosophic Matrix Scenario

Author

Listed:
  • Jun Ye

    (Ningbo University)

  • Kaiqian Du

    (Shaoxing University)

  • Shigui Du

    (Ningbo University
    Shaoxing University)

  • Rui Yong

    (Ningbo University)

Abstract

Since matrix energy (ME) implies the expressive merit of collective information, a classification method based on ME has not been investigated in the existing literature, which reflects its research gap in a matrix scenario. Therefore, the purpose of this paper is to propose a slope stability classification model based on the single-valued neutrosophic matrix (SVNM) energy to solve the current research gap in slope stability classification analysis with uncertain and inconsistent information. In this study, we first present SVNM and define the SVNM energy based on true, uncertain, and false MEs. Then, using a neutrosophication technique based on true, false, and uncertain Gaussian membership functions, the multiple sampling data of the stability affecting factors for each slope are transformed into SVNM. Next, a slope stability classification model based on the SVNM energy and score function is developed to solve the slope stability classification analysis under the full SVNM scenario of both the affecting factor weights and the affecting factors of slope stability. Finally, the developed classification model is applied to the classification analysis of 50 slope samples collected from different areas of Zhejiang province in China as a case study to verify its rationality and accuracy under the SVNM scenario. The accuracy of the classification results for the 50 slope samples is 100%.

Suggested Citation

  • Jun Ye & Kaiqian Du & Shigui Du & Rui Yong, 2025. "Slope Stability Classification Model Based on Single-Valued Neutrosophic Matrix Energy and Its Application Under a Single-Valued Neutrosophic Matrix Scenario," Journal of Classification, Springer;The Classification Society, vol. 42(1), pages 163-180, March.
    • Handle: RePEc:spr:jclass:v:42:y:2025:i:1:d:10.1007_s00357-024-09487-x
    DOI: 10.1007/s00357-024-09487-x
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