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基于优化WOA-BP策略的土体冻胀率因素敏感性定量分析

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土体冻胀是寒区工程建设面临的重要挑战之一,对其进行准确预测和敏感性分析,对于保障工程安全与稳定、预防结构变形与破坏至关重要.针对东北某矿区三种土样,在不同冷端温度、含水率和干密度条件下,实施了单向冻结且不补水的冻胀实验.基于实验数据,分析了影响冻胀率的关键因素,构建了以干密度、含水率、冷端温度、比重及结冰温度为输入变量的WOA-BP预测模型,引入Chebyshev混沌映射与自适应权重调整策略,优化得到Chebyshev混沌映射自适应权重的WOA-BP神经网络.经验证,该模型预测误差小,可以较好地预测土体的冻胀率.结合Garson算法、扰动法及蒙特卡洛模拟等三种方法,对土体冻胀率的影响因素进行了敏感性分析,所得结果一致.该矿区土样的冻胀率对干密度、比重、含水率、冷端温度、结冰温度变化的敏感程度依次降低.
Quantitative analysis of factor sensitivity of soil frost heave rate based on optimized WOA-BP strategy
Soil frost heave is one of the important challenges faced by engineering construction in cold regions.Accurate prediction and sensitivity analysis of soil frost heave are very important to ensure engineering safety and stability and prevent structural deformation and damage.Aiming at three kinds of soil samples in a mining area in Northeast China,the frost heave experiment of one-way freezing without water replenishment was carried out under different conditions of cold end temperature,moisture content and dry density.Based on the experi-mental data,the key factors affecting frost heave rate are deeply analyzed,and a WOA-BP prediction model with dry density,moisture content,cold end temperature,specific gravity and icing temperature as input varia-bles is constructed.On this basis,Chebyshev chaotic mapping and adaptive weight adjustment strategy are in-troduced,and the WOA-BP neural network with adaptive weight of Chebyshev chaotic mapping is optimized.It is verified that the model has small prediction error and can better predict the frost heave rate of soil.Combined with Garson algorithm,perturbation method and Monte Carlo simulation,the sensitivity analysis of the influen-cing factors of soil frost heave rate is carried out.The results show that the results obtained by all methods are consistent,namely the sensitivity of frost heave rate of soil samples in this mining area to the changes of dry density,specific gravity,moisture content,cold end temperature and freezing temperature decreases in turn.

soil frost heave ratefactor sensitivityWOA-BP neural networkChebyshev chaotic mappingGar-son's algorithmMonte Carlo simulation

姚兆明、孔宏水、王洵、齐健

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安徽理工大学 土木建筑学院,安徽 淮南 232001

矿山地下工程教育部工程研究中心,安徽 淮南 232001

土体冻胀率 因素敏感性 WOA-BP神经网络 Chebyshev混沌映射 Garson算法 蒙特卡洛模拟

矿山地下工程教育部工程研究中心开放研究项目

JYBGCZX2021104

2024

河南城建学院学报
河南城建学院

河南城建学院学报

影响因子:0.457
ISSN:1674-7046
年,卷(期):2024.33(4)
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