论文部分内容阅读
建筑工程地质勘察钻孔质量评价是一项多目标、多因素、多层次的复杂工程。在总结传统影响钻孔质量各类因素的基础上,构建了建筑工程地质勘察钻孔质量综合评价指标体系,综合运用BP神经网络理论建立了钻孔质量BP网络评价模型。该模型能够很好地反映各钻孔质量评价指标与综合评价结果之间复杂的非线性关系,降低钻孔质量评价过程中的主观因素影响。为了验证该评价模型的可靠性与实用性,对评价模型进行了检测并进行实例应用,结果表明可将其用于建筑工程地质勘察钻孔质量评价。
Construction Engineering Geological Survey Drilling quality evaluation is a multi-objective, multi-factor, multi-level complex project. On the basis of summarizing the traditional factors that affect the quality of the borehole, this paper constructs a comprehensive evaluation index system of the drilling quality of the geological survey of construction engineering, and establishes a BP neural network evaluation model based on BP neural network theory. The model can well reflect the complicated nonlinear relationship between each drilling hole quality evaluation index and comprehensive evaluation result and reduce the influence of subjective factors in drilling quality evaluation. In order to verify the reliability and practicability of the evaluation model, the evaluation model was tested and applied as an example. The results show that it can be used for drilling quality evaluation of geological engineering geological survey.