| 摘 要: 在复杂施工环境的施工项目中,由于受地质条件、气候条件及设备性能等多重因素影响,大型工程装备的分析选择需要耗费大量精力。为解决此问题,提出一种面向复杂施工环境的知识图谱与随机森林优化大型工程装备推荐方法。首先,利用知识图谱提取关于装备选择的20个主要影响因素及其重要性占比;其次,使用一种基于随机森林算法和贝叶斯优化的融合算法,构建优化目标函数计算各类装备适应性评分;最后,根据算法给出最优装备推荐,为施工项目管理者提供科学指导。 |
| 关键词: 大型工程装备 环境适应性 知识图谱 随机森林算法 贝叶斯优化 |
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中图分类号: TP301.6
文献标识码: A
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| 基金项目: 基金项目:国家重点研发计划项目(2021YFB3301600) |
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| Knowledge Graphand Random Forest-Based Optimization Method for RecommendingLarge Engineering Equipmentin Comple xConstruction Environments |
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XU Haijie, HE Lili, ZHENG Junhong
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(School of Information Science and Engineering, Zhejiang Sc-i Tech University, Hangzhou 310018, China)
zxc1832622189@163.com; llhe@zju.edu.cn; zdzhengjh@sohu.com
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| Abstract: In construction projects under complex environments, the analysis and selection of large engineering equipment require significant effort due to the influence of multiple factors such as geological conditions, climatic conditions, and equipment performance. To address this issue, this paper proposes a knowledge graph and random fores-t based optimization method for recommending large engineering equipment in complex construction environments. First, a knowledge graph is used to extract 20 main influencing factors for equipment selection and their
importance proportions. Second, a fusion algorithm based on the random forest algorithm and Bayesian optimization is employed to construct an optimized objective function for calculating the adaptability scores of various types of equipment. Finally, the algorithm provides optimal equipment recommendations, offering scientific guidance for construction project managers. |
| Keywords: large engineering equipment environmental adaptability knowledge graph random forest algorithm Bayesian optimization |
