Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (10): 2837-2848.doi: 10.13229/j.cnki.jdxbgxb.20221523

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Intelligent compaction quality assessment of earth-rock dams considering small samples uncertainty

Qing-long ZHANG1(),Nai-fu Deng1,Zai-zhan AN2,Rui MA3,Yu-fei ZHAO4()   

  1. 1.Department of Civil Engineering，University of Science and Technology Beijing，Beijing 100083，China
    2.China Electric Power Planning and Engineering Institute，Beijing 100011，China
    3.State Key Laboratory of Hydroscience and Engineering，Tsinghua University，Beijing 100084，China
    4.Key Laboratory of Construction and Safety of Water Engineering of the Ministry of Water Resources，China Institute of Water Resources and Hydropower Research，Beijing 100048，China
  • Received:2022-11-28 Online:2024-10-01 Published:2024-11-22
  • Contact: Yu-fei ZHAO E-mail:qlzhang@ustb.edu.cn;zhaoyf@iwhr.com

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Abstract:

In light of the poor real-time performance， low accuracy， weak generalization， insufficient training data and susceptible to the external changes in compaction quality assessment of earth-rock dams. This paper proposes a compaction quality assessment model based on the binary muti-population genetic algorithm fused back propagation neural network. This model addresses the problem of small sample learning in the intelligent compaction assessment of rockfill materials on-site by adjusting the transfer activation function， improving the migration updating mechanism， and constructing a training loss function considering the epistemic uncertainty and aleatoric uncertainty. The results show that the assessment performance of the proposed model is better than 9 comparison models， and the combinatorial uncertainty can improve the generalization and data error tolerance of the model， which features generalizability and application value in other engineering scenarios.

Key words: hydraulic engineering, earth-rock dam, rockfill material, compaction quality assessment, uncertainty analysis, neural network

CLC Number: 

  • TV523

Fig.1

Research framework"

Fig.2

BP network"

"

输入： V： 各种群个体数； U： 种群数； Z： 得到最优解的最大迭代周期； N： BP网络的训练迭代次数； M： 适应度差异阈值； F： 组合比例（F∈0,1）

输出： Ioptimal： 包含边权重矩阵和偏置向量的最优个体； Loptimal： 最优个体适应度

1

初始化 i=0； NΦ=0； Iu,v； V； U； Z； N； M； F；

2

while i≤Z

3

?i=i+1；

4

for j=1:U do

5

??选择操作；

6

??交叉操作；

7

??变异操作；

8

for j=1:U

9

???进行N次BP神经网络训练；

10

???计算每个个体适应度；

11

??end

12

?end

13

for k=1:MP do

14

??ifLu0,worst-Lu,best>M,?u∈N?u≠u0；

15

???NΦ=NΦ+1；

16

??end

17

for k=1:MP do

18

if NΦ<U/2

19

???Iu0,worst=Iu-1,best；

20

??else

21

???Iu0,worst=Iu0,worst+F(Iu-1,best-Iu1,best)；

22

??end

23

?end

24

for l=1:MP do

25

??筛选每个种群的最优个体Iu,best及其适应度 Lu,best；

26

?end

27

?比选筛分最优个体Ibest及其Lbest；

28

end

Fig.3

An example of dropout operation in BP network"

Fig.4

Test field on clay core wall earth-rock dam engineering site"

Fig.5

Grain curve of rockfill"

Fig.6

Process of field rolling test"

Table 1

Operation conditions of field test"

工况 编号振动频率/ kHz行车速度/ （m·s-1）行驶方向碾压遍数
1200.6反向1、3、7
2200.6正向2、6
3200.55正向4、8
4200.7反向5
5240.69反向1、3、5、7
6240.69正向2、6、8
7240.73正向4
8280.6反向1、3、7
9280.6正向4、6、8
10280.65正向2
11320.69反向1、3、5、7
12320.69正向2、4、6、8

Fig.7

Correlation analysis matrix"

Fig.8

Compaction quality assessment results of proposed model"

Table 2

Comparison of compaction quality assessment methods"

评估模型R2MAPE/％RMSE/％
RBF0.847 04.364 14.484 2
ELM0.669 110.495 79.700 1
LSTM0.750 313.929 912.802 2
BP0.706 68.179 88.164 4
PSO-BP0.669 18.237 47.684 1
DE-BP0.734 65.828 85.738 1
MPDE-BP0.847 05.194 55.249 0
GA-BP0.771 27.340 97.404 4
MPGA-BP0.903 33.408 73.241 0
本文模型0.937 83.054 72.948 3

Fig.9

Uncertainty analysis of compaction quality assessment"

Fig.10

Aleatoric uncertainty analysis under the noise with an intensity of 3"

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