摘要
随着施工安全问题日益复杂,为进一步减少施工安全事故的发生,针对传统安全评价方法无法有效挖掘各安全指标之间的内在联系,并且现有聚类方法存在紧凑性不足、结果解释性差的问题,提出一种采用因子分析与变分贝叶斯高斯混合聚类的安全评价方法。该方法利用因子分析将复杂的施工安全评价指标转换为有内在联系的因子变量,作为变分贝叶斯高斯混合方法的输入,并使用T分布随机相邻嵌入法(t-distributed stochastic neighbor embedding, T-SNE)对多维聚类结果进行可视化,充分挖掘各施工安全指标之间的内在关联性并对施工安全做出评价。案例分析表明,与层次聚类分析、K-means以及高斯混合模型(gaussian mixture model, GMM)方法相比,所提方法具有更好的聚类效果和全局寻优性能,不仅验证了所提方法的可行性和有效性,还通过可视化的方法增强了多维聚类问题的可解释性。
With the continuous increase of construction needs,the problem of construction safety is becoming more and more complex.In order to further decrease the occurrence of construction safety accidents,the traditional safety evaluation methods cannot effectively excavate the internal connection between the safety indicators.Besides,the existing clustering methods have the problems of insufficient compactness and poor interpretation.Therefore,this paper presents a safety evaluation method using factor analysis and variational Bayesian Gaussians mixture model,which adopts factor analysis to convert complex construction safety evaluation indicators into internally related factor variables as the inputs of variational Bayesian Gaussians mixture method.The multidimensional clustering results are visualized using the T-distributed stochastic neighbor embedding(T-SNE)method,so as to fully explore the intrinsic correlation between various construction safety indicators and evaluate construction safety.Case analysis shows that compared with hierarchical clustering analysis,K-means and Gaussian mixture model(GMM)method,the proposed method has better clustering effect and global optimization performance.It not only verifies the feasibility and effectiveness of the proposed method,but also improves the interpretability of multidimensional clustering problems by visualization method.
作者
於三大
朱浪
苏立
廖勇
YU Sanda;ZHU Lang;SU Li;LIAO Yong(China Three Gorges Construction Engineering Corporation,Chengdu 610041,China;School of Microelectronics and Communication Engineering,Chongqing University,Chongqing 400044,China)
出处
《重庆理工大学学报（自然科学）》
北大核心
2023年第8期203-211,共9页
Journal of Chongqing University of Technology：Natural Science
基金
中国三峡建工多项目集成管理系统项目(JG/2058B)。
关键词
施工安全评价
因子分析
变分贝叶斯高斯混合模型
可视化
聚类
construction safety evaluation
factor analysis
variational Bayesian Gaussians mixture model
visualization
clusterin
