Query：
学者姓名：谢秀栋
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
Abstract ：
The construction of an interval prediction model capable of explaining deformation uncertainties is crucial for the long-term safe operation of dams. High effective coverage and narrow interval coverage widths are two key benchmarks to ensure that the prediction interval (PI) can accurately quantify deformation uncertainties. The vast majority of existing models neglect to control the interval coverage width, and overly wide PIs can cause decision confusion when operators are developing safety plans for hydraulic structures. To address this problem, this paper proposes a novel interval prediction model combining bidirectional long-short-term memory network (Bi-LSTM) and split conformal quantile prediction (SCQP) for dam deformation prediction. The model uses Bi-LSTM as a benchmark regressor to extract and explain the nonlinear feature of dam deformation in the continuous time domain. SCQP is used to quantify the uncertainties in dam deformation prediction to ensure that the constructed PI can achieve high effective coverage while further improving the accuracy of the quantification of deformation uncertainties. The effectiveness of the proposed model is validated using deformation monitoring data collected from an arch dam in China. The results show that the average prediction interval effective coverage (PICP) of the proposed model is as high as 0.951 while the mean prediction interval width (MPIW) and coverage width-based criterion (CWC) are both only 5.815 mm. Compared with other models, the proposed method can construct higher-quality PIs, thus providing a better service for the safety assessment of dams.
Keyword ：
Bi-LSTM Bi-LSTM dam deformation prediction dam deformation prediction interval prediction interval prediction split conformal quantile prediction split conformal quantile prediction uncertainty quantification uncertainty quantification
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Su, Yan , Fu, Jiayuan , Lin, Weiwei et al. Dam Deformation Monitoring Model Based on Deep Learning and Split Conformal Quantile Prediction [J]. | APPLIED SCIENCES-BASEL , 2025 , 15 (4) . |
| MLA | Su, Yan et al. "Dam Deformation Monitoring Model Based on Deep Learning and Split Conformal Quantile Prediction" . | APPLIED SCIENCES-BASEL 15 . 4 (2025) . |
| APA | Su, Yan , Fu, Jiayuan , Lin, Weiwei , Lin, Chuan , Lai, Xiaohe , Xie, Xiudong . Dam Deformation Monitoring Model Based on Deep Learning and Split Conformal Quantile Prediction . | APPLIED SCIENCES-BASEL , 2025 , 15 (4) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
考虑到滑坡编录制作的耗时性,建立一种"可迁移"的滑坡易发性模型已越发重要.合理利用现有完整滑坡数据地区的样本集对无样本区域进行易发性预测具有重要意义.运用迁移成分分析(transfer component analysis,TCA)方法,结合深度学习卷积神经网络(convolutional neural network,CNN),尝试引入一种基于迁移学习域自适应方法的TCA-CNN模型,并以福建省两个库岸地区为例,提取 11个库岸相关环境因子建立滑坡空间数据库,将有样本的池潭库区易发性模型迁移至无样本的棉花滩库区进行预测,实现跨区域滑坡易发性评价.通过对棉花滩库区进行易发性预测,结果显示:(1)采用TCA方法处理后的不同研究区数据最大均值差异(maximize mean discrepancy,MMD)明显降低(0.022),数据实现近似同分布;(2)TCA-CNN模型的跨区域预测精度为 0.854,高于CNN模型(0.791),且通过历史滑坡验证其落入高、极高易发性区间的滑坡频率比占比最高(89.1%);(3)受试者工作特性(receiver operating characteristic,ROC)曲线下面积TCA-CNN模型为 0.93,高于CNN模型的 0.90.可见TCA-CNN模型能够有效运用建模区的样本数据实现对无样本区域的易发性评价,且相比于传统机器模型在进行跨区域预测时具有更高、更稳定的预测准确率,具备更强的泛化能力.
Keyword ：
卷积神经网络 卷积神经网络 库岸边坡 库岸边坡 数据缺失 数据缺失 滑坡 滑坡 滑坡易发性 滑坡易发性 灾害 灾害 迁移成分分析 迁移成分分析
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 苏燕 , 黄绍翔 , 赖晓鹤 et al. 基于迁移成分分析的库岸跨区域滑坡易发性评价 [J]. | 地球科学 , 2024 , 49 (5) : 1636-1653 . |
| MLA | 苏燕 et al. "基于迁移成分分析的库岸跨区域滑坡易发性评价" . | 地球科学 49 . 5 (2024) : 1636-1653 . |
| APA | 苏燕 , 黄绍翔 , 赖晓鹤 , 陈耀鑫 , 杨凌鋆 , 林川 et al. 基于迁移成分分析的库岸跨区域滑坡易发性评价 . | 地球科学 , 2024 , 49 (5) , 1636-1653 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
To investigate the influence of macropore parameters on the non-uniform migration and stability of slopes under rainfall, a solution model was developed based on the two-domain model and the stability coefficient field principle. This model addressed non-uniform flow and slope stability under rainfall infiltration. Using the COMSOL Multiphysics finite element platform, a corresponding model solving program was created. The numerical results were validated through indoor rainfall tests on macropore soil columns. A comparison was made between slope volume water content and point stability coefficient under conditions of uniform and non-uniform flow. Subsequently, the impact of macropore parameters (namely, the proportion of macropore domain ωf, the ratio of water conductivity between macropore and matrix domain μ, and the macropore empirical parameter rw) on slope seepage field and stability coefficient field was analyzed. The findings revealed that compared to scenarios without macropores, considering macropores led to a 7.7% increase in volume water content in the matrix domain and a 5.1% decrease in the macropore domain. Additionally, infiltration depth increased by 83.3% and 150.0%, respectively, and the shallow instability area of the slope expanded by 3.9%. Infiltration depth decreased with an increase in ωf for both the matrix and macropore domains. Conversely, with an increase in μ, infiltration depth decreased for the matrix domain and increased for the macropore domain. There was no significant relationship observed with the empirical parameter rw. At the end of the rainfall, volume water content in the matrix domain peaked, while the macropore domain increased with higher values of ωf and μ, showing minimal impact from the empirical parameter rw. Water exchange was categorized into negative exchange area, positive exchange area, and no exchange area along the profile. The equilibrium depth of water exchange aligned with the change in infiltration depth of the matrix domain. Both the negative and positive exchange areas exhibited peak values that decreased with higher ωf and increased with higher μ and rw values. Under varying parameter values, the slope experienced shallow instability failures. Higher values of ωf and μ corresponded to deeper instability layers and lower point stability coefficients, indicating that macropores were detrimental to slope stability. © 2024 Sichuan University. All rights reserved.
Keyword ：
Infiltration Infiltration Rain Rain Slope stability Slope stability
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Que, Yun , Li, Shanghui , Zhan, Xiaojun et al. Influence of Macropore Parameters on Slope Non-uniform Flow and Stability [J]. | Advanced Engineering Sciences , 2024 , 56 (3) : 122-133 . |
| MLA | Que, Yun et al. "Influence of Macropore Parameters on Slope Non-uniform Flow and Stability" . | Advanced Engineering Sciences 56 . 3 (2024) : 122-133 . |
| APA | Que, Yun , Li, Shanghui , Zhan, Xiaojun , Zhang, Jisong , Xue, Bin , Xie, Xiudong . Influence of Macropore Parameters on Slope Non-uniform Flow and Stability . | Advanced Engineering Sciences , 2024 , 56 (3) , 122-133 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
It is crucial to create a 'migratable' landslide susceptibility model considering the time-consuming process of recording landslides. However, a sample set of all known landslide data areas must be used in forecasting the susceptibility of unsampled regions adequately. In this paper, we attempt to develop a TCA-CNN model to enable trans-regional landslide susceptibility evaluation, which is based on the adaption domain of transfer learning by integrating the transfer component analysis (TCA) with deep learning convolutional neural network (CNN). The spatial database of landslides is constructed by extracting 11 environmental factors of the reservoir bank area, then the Mianhuatan reservoir area without samples is then predicted using the susceptibility model from the Chitan reservoir area with samples. The results show that: (1) The maximum mean discrepancy (MMD) of data from different study areas treated by TCA decreased significantly (0.022), and the data is approximately identically distributed; (2) The trans-regional prediction accuracy by the TCA-CNN model is 0.854, which is higher than that of the CNN model (0.791). And it can be verified that the proportion of landslide frequency falling into the high/extremely-high susceptibility interval is the highest (89.1%) in the historical landslide; (3) The area under the receiver operating characteristic (ROC) curve of the TCA-CNN model is 0.93, which is higher than that of the CNN model (0.90). It’s obvious that the TCA-CNN model can effectively use the samples data of the modeling area to realize the susceptibility evaluation of the unsampled area. Compared with the traditional machine model, TCA-CNN model has higher and more stable prediction accuracy and stronger generalization ability in cross-region prediction. © 2024 China University of Geosciences. All rights reserved.
Keyword ：
Convolution Convolution Convolutional neural networks Convolutional neural networks Deep learning Deep learning Forecasting Forecasting Landslides Landslides Neural network models Neural network models Transfer learning Transfer learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Su, Yan , Huang, Shaoxiang , Lai, Xiaohe et al. Evaluation of Trans⁃Regional Landslide Susceptibility of Reservoir Bank Based on Transfer Component Analysis [J]. | Earth Science - Journal of China University of Geosciences , 2024 , 49 (5) : 1636-1653 . |
| MLA | Su, Yan et al. "Evaluation of Trans⁃Regional Landslide Susceptibility of Reservoir Bank Based on Transfer Component Analysis" . | Earth Science - Journal of China University of Geosciences 49 . 5 (2024) : 1636-1653 . |
| APA | Su, Yan , Huang, Shaoxiang , Lai, Xiaohe , Chen, Yaoxin , Yang, Lingjun , Lin, Chuan et al. Evaluation of Trans⁃Regional Landslide Susceptibility of Reservoir Bank Based on Transfer Component Analysis . | Earth Science - Journal of China University of Geosciences , 2024 , 49 (5) , 1636-1653 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Purpose The macropore structure and seepage characteristics profoundly influence the stability of granite residual soil (GRS) slopes. However, accurately predicting the permeability of undisturbed GRS (U-GRS) is challenging owing to its complex and susceptible pore structure. Aims and methods Employing X-ray computed tomography (CT) technologies, a three-dimensional (3D) pore structure of U-GRS, was established. Permeability prediction for U-GRS samples was conducted using three simulation methods, namely, the pore network model (PNM), finite element method (FEM), and the lattice Boltzmann method (LBM), along with two empirical models (EMs)-specifically, Kozeny-Carman (K-C) and Katz-Thompson (K-T) models. Subsequently, the methods were comparatively analyzed for calculating efficiency and accuracy. Finally, a piecewise permeability prediction model (PPPM) for U-GRS based on the CT-LBM was proposed. Results The ranking of permeability estimation methods in terms of accuracy was as follows: LBM > PNM > FEM > EMs. Substantial disparity was observed in the permeabilities obtained using both FEM and EMs compared to other methods, which exhibited a deviation of up to six orders of magnitude. The PPPM demonstrated smaller prediction deviations than the EMs, with its accuracy influenced by the strategy for selecting calculation parameters. Conclusion The CT-LBM, which uses real pore structures, was employed to estimate the permeability of U-GRS. The PPPM, established based on this method, was found to be applicable for estimating U-GRS permeability.
Keyword ：
Comparative analysis Comparative analysis Granite residual soil Granite residual soil Macropore Macropore Permeability model Permeability model X-ray computed tomography images X-ray computed tomography images
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Que, Yun , Chen, Xian , Jiang, Zhenliang et al. Pore-scale permeability estimation of undisturbed granite residual soil: A comparison study by different methods [J]. | JOURNAL OF SOILS AND SEDIMENTS , 2024 . |
| MLA | Que, Yun et al. "Pore-scale permeability estimation of undisturbed granite residual soil: A comparison study by different methods" . | JOURNAL OF SOILS AND SEDIMENTS (2024) . |
| APA | Que, Yun , Chen, Xian , Jiang, Zhenliang , Cai, Peichen , Xue, Bin , Xie, Xiudong . Pore-scale permeability estimation of undisturbed granite residual soil: A comparison study by different methods . | JOURNAL OF SOILS AND SEDIMENTS , 2024 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Given the time-consuming nature of compiling landslide inventories, it is increasingly important to develop transferable landslide susceptibility models that can be applied to regions without existing data. In this study, we propose a feature-based domain adaptation method to improve the transferability of landslide susceptibility models, especially in "no sample" areas. Two typical landslide-prone areas in Fujian province, southeastern China, were chosen as research cases to test the practicality of the transfer effect. Five conventional machine learning algorithms (Support vector machines (SVM), Random Forest (RF), Logistic Regression (LOG), K-nearest neighbor (KNN), and Decision tree (C4.5)) are used to model landslide susceptibility in sampled areas (source domain), and a feature transfer-based landslide susceptibility evaluation model is constructed under coupled feature transfer methods to evaluate the susceptibility of landslide in un-sampled areas (target domain). The results showed that feature transfer can effectively improve the transferability of different machine learning models for cross-regional prediction (The indicators have improved overall by 8.49%), with SVM (increased by 13.68%) and LOG (increased by 10.19%) models showing the most significant improvements. The feature-based domain adaptive method can alleviate the burden of collecting and labeling new data, and effectively improve the assessment performance of machine learning-based landslide susceptibility models in un-sampled areas. This is a new solution for landslide susceptibility assessment in completely "no sample" areas. (c) 2024 International Association for Gondwana Research. Published by Elsevier B.V. All rights reserved.
Keyword ：
Data scarcity Data scarcity Feature domain adaptation Feature domain adaptation Landslide susceptibility Landslide susceptibility Machine learning Machine learning Reservoir bank slope Reservoir bank slope
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Su, Yan , Chen, Yaoxin , Lai, Xiaohe et al. Feature adaptation for landslide susceptibility assessment in "no sample" areas [J]. | GONDWANA RESEARCH , 2024 , 131 : 1-17 . |
| MLA | Su, Yan et al. "Feature adaptation for landslide susceptibility assessment in "no sample" areas" . | GONDWANA RESEARCH 131 (2024) : 1-17 . |
| APA | Su, Yan , Chen, Yaoxin , Lai, Xiaohe , Huang, Shaoxiang , Lin, Chuan , Xie, Xiudong . Feature adaptation for landslide susceptibility assessment in "no sample" areas . | GONDWANA RESEARCH , 2024 , 131 , 1-17 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Detecting water head is crucial in groundwater utilization, and requires quick and accurate solutions. This study employs a method combining the collocation Trefftz method (CTM) and the fictitious time integration method (FTIM) for groundwater head detection and restoration. The Laplace equation is solved using CTM, and the Trefftz basis function is linearly combined to fit the exact solution while adding characteristics length for numerical stability. The FTIM solves the nonlinear algebraic equations system for water head detection. Numerical examples quantify the method's accuracy, and boundary restoration results are compared with the Picard successive approximation method, showcasing FTIM's advantages in convergence steps and precision. The solution comparison under irregular boundary conditions further verifies the proposed method's efficacy (MAE <= 10-15). The CTM-FTIM calculated water level boundary aligns with the actual boundary, and its noise immunity is verified using real observation well data (MAE <= 10-2, Itertimes <= 5000). The CTM-FTIM method eliminates meshing needs for quick solutions in irregular regions, accurately determining water levels in the study domain using few known boundary points, solving infinite domain groundwater head detection.
Keyword ：
Fictitious time integration method Fictitious time integration method Groundwater head detection recovery Groundwater head detection recovery Noise immunity Noise immunity Observation well data Observation well data Trefftz method Trefftz method
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Su, Yan , Huang, Bin , Yang, Lingjun et al. Study on the detection of groundwater boundary based on the Trefftz method [J]. | NATURAL HAZARDS , 2024 , 120 (8) : 8057-8085 . |
| MLA | Su, Yan et al. "Study on the detection of groundwater boundary based on the Trefftz method" . | NATURAL HAZARDS 120 . 8 (2024) : 8057-8085 . |
| APA | Su, Yan , Huang, Bin , Yang, Lingjun , Lai, Xiaohe , Lin, Chuan , Xie, Xiudong et al. Study on the detection of groundwater boundary based on the Trefftz method . | NATURAL HAZARDS , 2024 , 120 (8) , 8057-8085 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
为解决传统四参数随机生长(quartet structure generation set, QSGS)方法重构模型与真实土体情况不符的缺陷，提出一种改进的QSGS方法，在综合考虑各向异性、孔隙率、土体颗粒大小情况下构建各向异性多孔介质模型.采用两点自相关函数法验证模型的有效性，并结合格子玻尔兹曼方法自编Matlab程序来模拟细观渗流过程.实验发现，各向同性模型和土体CT切片的两点相关系数最大差值是各向异性模型的2.5倍以上，且流体粒子在孔道中线处的渗流速度最大，达到0.03,在孔壁处渗流速度接近于0.结果表明，各向异性模型在孔隙结构和空间分布上更接近真实土体情况，流体在渗流后期出现大孔隙优先流现象和指进现象.
Keyword ：
四参数随机生长法 四参数随机生长法 格子玻尔兹曼 格子玻尔兹曼 水分迁移 水分迁移 细观渗流 细观渗流 重构土体模型 重构土体模型
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 阙云 , 邱婷 , 蔡沛辰 et al. 基于改进QSGS-LBM法的各向异性重构土体的渗流模拟 [J]. | 福州大学学报(自然科学版) , 2024 , 52 (04) : 471-478 . |
| MLA | 阙云 et al. "基于改进QSGS-LBM法的各向异性重构土体的渗流模拟" . | 福州大学学报(自然科学版) 52 . 04 (2024) : 471-478 . |
| APA | 阙云 , 邱婷 , 蔡沛辰 , 谢秀栋 , 薛斌 . 基于改进QSGS-LBM法的各向异性重构土体的渗流模拟 . | 福州大学学报(自然科学版) , 2024 , 52 (04) , 471-478 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
为揭示降雨条件下大孔隙参数对斜坡水分非均匀运移与稳定性的影响，基于两域模型与稳定系数场原理，建立降雨入渗下斜坡非均匀渗流与稳定性求解模型，并借助COMSOL Multiphysics多物理场有限元平台，编制相应的模型求解程序，通过大孔隙土柱降雨试验验证数值模型的合理性，对比均匀流与非均匀流条件下斜坡体积含水率和点稳定系数，分析大孔隙参数（大孔隙占比ω_f、两域导水系数之比μ、大孔隙经验参数r_w）对斜坡渗流场及稳定系数场的影响规律。结果表明：相比不考虑大孔隙，考虑大孔隙时的基质域和大孔隙域表层体积含水率分别增长7.7%和降低5.1%，入渗深度分别增长83.3%和150.0%；边坡浅层失稳面积增大3.9%。基质域和大孔隙域入渗深度均随大孔隙占比ω_f的增大而减小；随着大孔隙域与基质域饱和渗透系数之比μ增大，两者入渗深度变化趋势相反，即μ越大，基质域入渗深度越小，大孔隙域反之；两者与经验参数r_w无显著关系。至降雨结束，基质域表层土体体积含水率已达最大值；大孔隙域则随着ω_f和μ的增大而增大，但几乎不受经验参数r_w的影响。非均匀流条件下，边坡水分交换沿着剖面从上往下分为负交换区、正交换区和无交换区，水分交换平衡深度与基质域入渗深度变化趋势一致；水分交换负交换区与正交换区的深度均存在一个峰值，并随大孔隙占比ω_f的增大而减小，随着μ和r_w的增大而增大。不同参数取值下，边坡均为浅层失稳破坏，ω_f和μ越大，失稳层深度越大，表层点稳定系数越小，因此大孔隙不利于边坡稳定。
Keyword ：
入渗深度 入渗深度 大孔隙参数 大孔隙参数 水分交换 水分交换 点稳定系数 点稳定系数 非均匀流 非均匀流
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 阙云 , 李尚辉 , 詹小军 et al. 大孔隙参数对斜坡非均匀渗流与稳定性的影响 [J]. | 工程科学与技术 , 2024 , 56 (03) : 122-133 . |
| MLA | 阙云 et al. "大孔隙参数对斜坡非均匀渗流与稳定性的影响" . | 工程科学与技术 56 . 03 (2024) : 122-133 . |
| APA | 阙云 , 李尚辉 , 詹小军 , 张吉松 , 薛斌 , 谢秀栋 . 大孔隙参数对斜坡非均匀渗流与稳定性的影响 . | 工程科学与技术 , 2024 , 56 (03) , 122-133 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
选取福州某地原状花岗岩残积土作为研究对象，基于计算机断层扫描(computer tomography, CT)技术与水平集（level set）方法，研究孔隙壁面湿润性对多孔介质水-气两相渗流特性的影响情况。结果表明：不同湿润性条件下，驱替过程均有细观“指进”现象，界面前缘形状主要以凸弧形驱进，凹弧形仅存在于渗流初期；孔隙壁面的湿润性对两相渗流过程影响较大，疏水壁面(湿润角θ>90°)会对流体产生排斥加速作用，亲水壁面(湿润角θ<90°)会对流体产生黏滞减速效应，但渗流速度并不一直随湿润角的增大而增大，而是随时间不断发生变化；最大水相饱和度出现在亲/疏水临界值90°处，趋于86.55%，残余气相饱和度为13.45%。
Keyword ：
两相流 两相流 多孔介质 多孔介质 水平集 水平集 湿润性 湿润性 计算机断层扫描 计算机断层扫描
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 蔡沛辰 , 阙云 , 谢秀栋 et al. 考虑壁面湿润性的多孔介质两相渗流特性数值研究 [J]. | 武汉大学学报(工学版) , 2023 , 56 (04) : 414-420 . |
| MLA | 蔡沛辰 et al. "考虑壁面湿润性的多孔介质两相渗流特性数值研究" . | 武汉大学学报(工学版) 56 . 04 (2023) : 414-420 . |
| APA | 蔡沛辰 , 阙云 , 谢秀栋 , 薛斌 . 考虑壁面湿润性的多孔介质两相渗流特性数值研究 . | 武汉大学学报(工学版) , 2023 , 56 (04) , 414-420 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
