碾压混凝土坝层间结合质量智能评价方法

针对碾压混凝土坝层间结合质量的可靠评价和动态控制问题,提出一种碾压混凝土坝层间结合质量智能评价方法。主要内容包括:①建立以碾压结合面上、下热层本体含湿率及压实度为评价参数,90 d龄期劈拉强度为评价目标的层间结合质量评价指标体系,合理表征现场碾压混凝土层间结合效果;②采用反距离加权(Inverse Distance Weighted,IDW)插值法对采用智能设备抽样检测的离散参数进行全仓面优化赋值,并以不同数量样点序列、不同大小网格划分下的参数模拟精度量化分析其空间不确定性;③基于Bagging算法集成BP神经网络构建了层间结合质量智能评价模型。研究成果应用于乌弄龙碾压混凝土坝典型施工仓层间结合质量动态评价,结果表明提出的智能评价方法不仅在考虑参数科学性及空间不确定性的基础上实现了层间结合质量动态准确评价,而且初步集成施工信息智能感知、传输及评价。该智能评价方法可为碾压混凝土层间结合质量动态精准评价提供参考。

Intelligent Evaluation of Interlayer Bonding Quality of RCC Dam

An intelligent evaluation method for the interlayer bonding quality of roller compacted concrete (RCC) dam is proposed in the light of reliable evaluation and dynamic control of interlayer bonding quality of RCC dam. (1) An evaluation indicator system with moisture content and compaction degree of RCC thermal layers as evaluation parameters and splitting tensile strength at 90 d-age of RCC core samples as evaluation target is established to reasonably characterize the interlayer bonding quality in the field. (2) The inverse distance weighted (IDW) interpolation method was employed to simulate the discrete parameters obtained by sampling detection with self-developed intelligent devices in the entire work area of RCC dam, and the spatial uncertainty was analyzed quantitatively by comparing the parameter simulation accuracy of sample sequences with different quantities and different mesh sizes. (3) The intelligent evaluation model for the interlayer bonding quality was established based on Bagging algorithm integrated with back-propagation artificial neural network (BP-ANN). The model was applied to the dynamic evaluation of the interlayer bonding quality of a typical construction warehouse of Wunonglong RCC Dam. Results suggest that the present method accurately and dynamically evaluates the interlayer bonding quality in consideration of spatial uncertainty, and also integrates the intelligent perception, transmission and evaluation of construction information.