Slope stability prediction and application based on MISSA-SVM model

doi:10.16265/j.cnki.issn1003-3033.2024.04.1275

Abstract

Abstract:

In order to further improve the prediction accuracy of slope stability, a slope stability prediction model based on MISSA optimized SVM was proposed. Six representative indexes, including bulk density (γ), cohesion (c), internal friction angle (Ф), slope angle (φ_f), slope height (H) and pore pressure ratio (r_u) were selected as the prediction indexes of the model. In response to the problems of slow convergence speed, low accuracy, and susceptibility to local optima in the sparrow optimization algorithm (SSA), strategies such as one-dimensional composite chaotic mapping, SCA, Levy flight mechanism, and dynamic adjustment of step size factor are introduced for optimization and improvement. A slope stability prediction model based on MISSA-SVM was constructed. The MISSA-SVM model was applied to 9 groups of slope engineering examples, such as the Daxi landslide, for verification. The results show that the accuracy, precision, recall, F₁ score, mean square error (MSE) and area under the curve (AUC) of the MISSA-SVM model reach 96.29%, 92.3%, 100%, 0.96, 0.016 and 0.967, respectively, which are better than the SSA-optimized SVM model and BP model, and the prediction results are completely consistent with the actual slope conditions, indicating that the MISSA-SVM model has strong generalization ability.

Key words: multi-strategy improvements sparrow search algorithm (MISSA), support vector machine (SVM), slope stability, sine cosine algorithm (SCA), predictive indicators

CLC Number:

X948

WANG Tuanhui, WANG Chao, WU Shunchuan, WANG Qiwei, XU Jianhui. Slope stability prediction and application based on MISSA-SVM model[J]. China Safety Science Journal, 2024, 34(4): 135-144.

Figures/Tables 11

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样本编号	工程名称	预测指标						边坡类型	边坡实际状态
样本编号	工程名称	γ/(kN·m^-3)	c/kPa	Ф/(°)	φ_f/ (°)	H /m	r_u /kPa	边坡类型	边坡实际状态
1	桂柳高速公路	27.1	22	18.6	25.6	100	0.19	土质	失稳
2	小浪底水库滑坡	21.2	0	35	23.75	150	0.25	土质	失稳
3	三峡水电项目滑坡	26.57	300	38.7	45.3	80	0.15	岩质	失稳
︙	︙	︙	︙	︙	︙	︙	︙	︙	︙
89	三穗-凯里高速公路	26.5	36.1	31	35	39	0	岩质	稳定
90	三穗-凯里高速公路	26	42.4	37	38	55	0	岩质	稳定

工程名称	预测指标						边坡实际状态	预测结果
工程名称	γ/ (kN·m^-3)	c/kPa	Ф/(°)	φ_f /(°)	H/m	r_u/kPa	边坡实际状态	MISSA- SVM	SSA- SVM	SSA- BP
三峡库区链子崖滑坡	18.5	25	0	30	6.003	0.29	失稳	失稳	失稳	失稳
三穗-凯里高速公路	20	8	20	10	10	0	失稳	失稳	稳定	失稳
三穗-凯里高速公路	21.4	28.8	20	50	52	0	失稳	稳定	失稳	失稳
︙	︙	︙	︙	︙	︙	︙	︙	︙	︙	︙
三板溪水电站 3号边坡	27.3	36	1	50	92	0.29	稳定	稳定	稳定	失稳
印度露天铁矿	28.44	29.42	35	35	100	0	稳定	稳定	失稳	失稳
印度露天铁矿	28.44	39.23	38	35	100	0	稳定	稳定	稳定	失稳
霸王山边坡	22.1	45.8	49.5	45.8	49.5	0.21	稳定	稳定	稳定	失稳

预测模型	准确率/%	精确率/%	召回率/%	F₁ 分数	MSE
MISSA-SVM	96.29	92.3	100	0.96	0.016
SSA-SVM	92.59	91.67	91.67	0.92	0.074
SSA-BP	85.18	100	66.67	0.8	0.148