首页|老采空区残余变形组合预测研究

老采空区残余变形组合预测研究

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
残余变形对下伏老采空区的重大建(构)筑物安全运维带来潜在风险,可靠的变形预测对保障地下采煤区国土空间安全再利用具有重要意义.针对残余变形预测模型因建模数据获取困难或模型自身不足而对预测结果影响显著的问题,该文提出一种基于数据结构分解的GM(1,1)-BP-RCRM-CM模型.首先采用牛顿插值法对模型训练数据进行插值处理使其等间距;然后利用对称滑动平均法将老采空区残余沉降监测数据分解为趋势项和扰动项,采用自回归模型进行监测数据序列分解后前端丢失值的端值补充;其次分别采用GM(1,1)-RCRM模型和BP-RCRM模型进行趋势项预测和扰动项预测;最后组合趋势项与扰动项预测结果作为总体预测结果,并采用平均绝对百分比误差进行模型拟合精度与预测精度评价.实验结果表明:GM(1,1)-BP-RCRM-CM模型在不同监测点位的残余沉降预测中结果一致性较高,符合预测模型随周期递进时预测精度渐次下降的客观规律,预测结果更贴合实际情况,较GM(1,1)-RCRM模型和BP-RCRM模型更稳健.
Combined prediction of old goaf residual deformation
Residual deformation brings potential risks to the safe operation and maintenance of major structures in the underlying old goaf.Reliable deformation prediction is of great significance to ensure the safe reuse of territorial space in underground coal mining areas.Aiming at the problem that the prediction model of residual deformation has a significant impact on the prediction result due to the difficulty in obtaining modeling data or the deficiency of the model itself,a GM(1,1)-BP-RCRM-CM model based on data structure decomposition is proposed in this paper.Firstly,Newton interpolation method was used to interpolate the model training data to make them equally spaced.Then the monitoring data of the old goaf residual subsidence was decomposed into trend and disturbance terms by symmetric sliding average method,and the end value of the front-end lost value after the decomposition of monitoring data series was supplemented by an autoregressive model.Secondly,GM(1,1)-RCRM model and BP-RCRM model were used to predict the trend term and the disturbance term respectively.Finally,the prediction results of trend item and disturbance item were combined as the overall prediction results,and the average absolute percentage error was used to evaluate the model fitting accuracy and prediction accuracy.The experimental results show that:The GM(1,1)-BP-RCRM-CM model has high consistency in predicting the residual settlement at different monitoring locations,which is in line with the objective law that the prediction accuracy of the prediction model decreases gradually as the period progresses.The predicted results are more in line with the actual situation,and more robust than GM(1,1)-RCRM model and BP-RCRM model.

old goafresidual deformationcombination predictiondata decompositionresidual correction

薛永安、邹友峰、冀哲、张文志

展开 >

河南理工大学测绘与国土信息工程学院,河南焦作 454000

太原理工大学矿业工程学院,太原 030024

河南省采空区场地生态修复与建设技术工程研究中心,河南焦作 454000

老采空区 残余变形 组合预测 数据分解 残差修正

国家自然科学基金山西煤基低碳联合基金重点项目国家自然科学基金山西煤基低碳联合基金重点项目教育部产学合作协同育人资助项目教育部产学合作协同育人资助项目

U1810203U22A2062020210224500922087106262449

2024

10.16251/j.cnki.1009-2307.2024.04.005

测绘科学
中国测绘科学研究院

测绘科学

CSTPCD北大核心
影响因子：0.774
ISSN：1009-2307
年,卷(期)：2024.49(4)
  • 22