【摘要】 为探究建筑施工人员安全能力的作用机制和关键影响因素，以社会资本和行为动机理论为依据，建立基于组织网络和个体行为的多指标数据集；借鉴成熟量表构建安全能力指标体系，通过问卷调查向中国境内若干建筑工程项目的457名施工人员获取数据；采用基于熵权法与变异系数法的多指标综合评价法进行指标权重计算和筛选；采用梯度提升决策树(GBDT)等5种机器学习方法分别建立回归预测模型，利用网格搜索法进行超参数优化，以提升模型的预测能力。结果表明：集成学习方法优于传统机器学习方法，GBDT算法在所有方法中拟合性能最佳；权重优化后的数据集可在一定程度上提升模型的预测精度；施工人员的安全参与行为和组织中社会资本的结构与认知维度对其安全能力影响较大；此外，个体特征中的工作年限和教育水平也与安全能力存在一定相关性。更多还原

【Abstract】 In order to explore the mechanism and key influencing factors of the safety competency of construction workers, this study established a multi-indicator dataset based on organizational network and individual behavior, referring to social capital and behavioral motivation theory. A safety competency indicator system was constructed based on previous scales, and data were obtained from 457 construction workers in China through a questionnaire survey. The multi-indicator comprehensive evaluation method based on the entropy weight and coefficient of variation method was used to calculate and screen the indicators. Then, five machine learning methods, such as GBDT, were used to establish regression models, respectively. The grid search method was used for hyperparameter optimization to improve the prediction ability of the model. The results show that the ensemble learning method is superior to the traditional machine learning method, and the GBDT algorithm has the best fitting performance. The optimized dataset can improve the prediction accuracy of the model. The safety participation and the structural and cognitive dimension of social capital significantly affect the construction workers’ safety competency. In addition, their working experience and education level also have a correlation with their safety competency.更多还原