混凝土抗压强度的高低直接影响着建筑物的安全和稳定性,传统的混凝土抗压预测方式周期长且成本高。针对解决混凝土抗压强度分析所面临的材料成分、分析过程复杂等问题,提出了一种基于证据推理(Evidential Reasoning, ER)和置信规则库(Be...混凝土抗压强度的高低直接影响着建筑物的安全和稳定性,传统的混凝土抗压预测方式周期长且成本高。针对解决混凝土抗压强度分析所面临的材料成分、分析过程复杂等问题,提出了一种基于证据推理(Evidential Reasoning, ER)和置信规则库(Belief Rule Base)的混凝土抗压强度预测方法。该方法首先利用随机森林(RF)算法得出部分指标的重要度,利用证据推理算法赋权融合各项指标。其次利用置信规则库专家系统将混凝土抗压指标中定性知识与定量的数据相结合,建立置信规则库预测模型。然后采用投影协方差矩阵自适应进化策略算法(P-CMA-ES)优化模型的参数。最后通过UCI数据库混凝土抗压强度数据集,对提出的方法进行了验证。实验结果表明,该方法保证了模型推理的透明,本文提出的预测方法具有较高的精度且具有一定的可解释性。The compressive strength of concrete directly affects the safety and stability of buildings, and the traditional concrete compressive strength prediction method has a long period and high cost. In order to solve the problems of material composition and complex analysis process faced by concrete compressive strength analysis, a concrete compressive strength prediction method based on Evidential Reasoning (ER) and Belief Rule Base was proposed. Firstly, the random forest (RF) algorithm is used to obtain the importance of some indicators, and the evidence inference algorithm is used to empower and fuse various indicators. Secondly, the confidence rule base expert system is used to combine the qualitative knowledge and quantitative data in the concrete compressive index, and the confidence rule base prediction model is established. Then, the projection covariance matrix adaptive evolution strategy algorithm (P-CMA-ES) was used to optimize the parameters of the model. Finally, the proposed method is verified by the concrete compressive strength dataset of the UCI database. Experimental results show that the proposed method ensures the transparency of model reasoning, and the prediction method proposed in this paper has high accuracy and interpretability.展开更多
文摘混凝土抗压强度的高低直接影响着建筑物的安全和稳定性,传统的混凝土抗压预测方式周期长且成本高。针对解决混凝土抗压强度分析所面临的材料成分、分析过程复杂等问题,提出了一种基于证据推理(Evidential Reasoning, ER)和置信规则库(Belief Rule Base)的混凝土抗压强度预测方法。该方法首先利用随机森林(RF)算法得出部分指标的重要度,利用证据推理算法赋权融合各项指标。其次利用置信规则库专家系统将混凝土抗压指标中定性知识与定量的数据相结合,建立置信规则库预测模型。然后采用投影协方差矩阵自适应进化策略算法(P-CMA-ES)优化模型的参数。最后通过UCI数据库混凝土抗压强度数据集,对提出的方法进行了验证。实验结果表明,该方法保证了模型推理的透明,本文提出的预测方法具有较高的精度且具有一定的可解释性。The compressive strength of concrete directly affects the safety and stability of buildings, and the traditional concrete compressive strength prediction method has a long period and high cost. In order to solve the problems of material composition and complex analysis process faced by concrete compressive strength analysis, a concrete compressive strength prediction method based on Evidential Reasoning (ER) and Belief Rule Base was proposed. Firstly, the random forest (RF) algorithm is used to obtain the importance of some indicators, and the evidence inference algorithm is used to empower and fuse various indicators. Secondly, the confidence rule base expert system is used to combine the qualitative knowledge and quantitative data in the concrete compressive index, and the confidence rule base prediction model is established. Then, the projection covariance matrix adaptive evolution strategy algorithm (P-CMA-ES) was used to optimize the parameters of the model. Finally, the proposed method is verified by the concrete compressive strength dataset of the UCI database. Experimental results show that the proposed method ensures the transparency of model reasoning, and the prediction method proposed in this paper has high accuracy and interpretability.
