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A novel multi-dimensional cloud model coupled with connection numbers theory for evaluation of slope stability

机译：结合连接数理论的新型多维云模型用于边坡稳定性评估

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摘要

Stability evaluation of a slope involves various fuzzy and correlation indicators randomly distributed in finite intervals. A novel multi-dimensional connection cloud model was presented here to address multiple uncertainties and distribution characteristics of indicators, and to depict the randomness and fuzziness of the measured index value belonging to the classification standard in the slope stability analysis. In the model, when simulating fuzzy and random characteristics of evaluation indicators in finite intervals, the numerical characteristics of connection cloud model were assigned on the basis of the analysis of identical-discrepancy-contrary (IDC) relationships between measured indicators and the classification standard to overcome the subjectivity. Considering the effect of indicator correlation in a unified way, the integrated connection degree of a grade was further specified for the evaluation sample. Moreover, case studies and comparisons of the proposed model with one-dimensional normal cloud model, extension model, and support vector machine (SVM) were performed to confirm the validity and reliability. The results indicate that this model employed to evaluate slope stability can clearly depict the random and fuzzy distribution features of measured data in finite intervals, and its calculation process is quicker and simpler than that of one-dimensional normal cloud model. (C) 2019 Elsevier Inc. All rights reserved.
机译：边坡的稳定性评估涉及各种模糊和相关指标，这些指标以有限的间隔随机分布。提出了一种新颖的多维连接云模型，以解决指标的多重不确定性和分布特征，并描述了边坡稳定性分析中属于分类标准的实测指标值的随机性和模糊性。在模型中，在有限区间中模拟评估指标的模糊和随机特征时，在分析测量指标与分类标准之间的等差-反比（IDC）关系的基础上，分配连接云模型的数值特征。克服主观性。综合考虑指标相关性的影响，进一步为评估样本指定了等级的综合关联度。此外，通过案例研究和拟议的模型与一维正态云模型，扩展模型和支持向量机（SVM）的比较，以确认有效性和可靠性。结果表明，用于评价边坡稳定性的模型可以清晰地描述有限间隔内测得数据的随机和模糊分布特征，其计算过程比一维正态云模型更快，更简单。 （C）2019 Elsevier Inc.保留所有权利。

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