CAJ | 학술논문

边坡稳定性分析与评价是边坡工程的核心内容，具有高度非线性和不确定性特征。首先，选取了多个边坡工程实例构成学习样本集，以土体重度、内摩擦角、粘聚力、坡角、坡高、孔隙比六个主要影响因素作为土坡稳定性的评价判别指标；然后，采用改进的粒子群算法优化BP神经网络模型，将网络权值和阈值粒子化，通过引入粒子群进化度和粒子群聚合度实现惯性权重的动态变化，利用粒子群算法的全局搜索性实现网络权值和阈值的更新，从而增强算法对非线性问题的处理能力，加快了收敛速度；最后，通过与其它边坡稳定性评价算法进行比较分析，表明了本文研究算法的可行性与合理性。
변파은정성분석여평개시변파공정적핵심내용，구유고도비선성화불학정성특정。수선，선취료다개변파공정실례구성학습양본집，이토체중도、내마찰각、점취력、파각、파고、공극비륙개주요영향인소작위토파은정성적평개판별지표；연후，채용개진적입자군산법우화BP신경망락모형，장망락권치화역치입자화，통과인입입자군진화도화입자군취합도실현관성권중적동태변화，이용입자군산법적전국수색성실현망락권치화역치적경신，종이증강산법대비선성문제적처리능력，가쾌료수렴속도；최후，통과여기타변파은정성평개산법진행비교분석，표명료본문연구산법적가행성여합이성。
Slope stability analysis and evaluation is the core content of slope engineering, it has the characteristics of highly non-linearity and uncertainty. Firstly, the multiple slope engineering instances was selected to constitute a learning sample set, the soil severe, internal friction angle, cohesive force, slope angle, slope height, void ratio, six main influencing factors were slope stability evaluation index. Then, the improved particle swarm algorithm was used to optimize the BP neural network model, the network weights and threshold values were particles, the inertia weight was implemented to dynamic change with the introduction of the particle swarm evolution degree and cluster degree. The network weights and threshold values were updated by use of particle swarm algorithm global searching capability, thus to enhance the algorithm processing capacity for nonlinear problem, and speed up the convergence rate. Finally, by comparing with other slope stability evaluation algorithm analysis, the feasibility and rationality of the research approach in the paper is presented.