基于高斯混合模型及EM算法的建筑工程数据预警治理方法

A Method for Early Warning and Governance of Construction Engineering Data Based on Gaussian Mixture Model and EM Algorithm

结合初期雨水调蓄大直径顶管工程的实际设计及施工经验,对软弱地层条件下长距离大直径平行双管曲线顶管在设计及施工过程中存在的重点难点问题进行总结,并对顶管过程中的顶力及管周摩阻力做了深入分析研究,有针对性地提出了相应的解决方案,使该顶管工程顺利贯通。建筑工程行业在现代社会中发挥着重要的经济和社会作用,然而,它也伴随着诸多风险和不确定性。为了有效地管理和预测这些风险,本文提出了一种基于高斯混合模型(GMM)和期望最大化(EM)算法的数据预警治理方法。该方法旨在通过对建筑工程数据的建模和分析,提前识别潜在的问题和风险,从而改善工程项目的管理和决策。

The construction engineering industry plays an important economic and social role in modern society,however,it is also accompanied by many risks and uncertainties.In order to effectively manage and predict these risks,this paper proposes a data early warning governance approach based on Gaussian mixture model(GMM) and expectation maximization(EM) algorithm.This approach aims to improve the management and decision-making of engineering projects by modeling and analyzing construction engineering data to identify potential problems and risks in advance.