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学者姓名：林威伟
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Abstract ：
我国东南沿海地区丘陵山地广布,在台风暴雨等因素激发下,各类地质灾害频发.该地区由暴雨引发的泥石流灾害具有突发性、群发性和破坏性特点,当此类灾害发生在临近河道沟谷时,可能冲击水工建筑物甚至造成堵江等严重危害.由于灾害过程的高度不确定性,有效的动力学参数反演方法对分析灾害演化特征和制定有效的减灾措施具有重要意义.本研究以"5·8泰宁泥石流"事件为案例,提出了一种基于多输出支持向量回归机(M-SVR)子模型的参数反演方法.首先,在泥石流动力学计算模型Geoflow_SPH的基础上,构建并行调用框架,对包含内摩擦角、容重、平均物源厚度的触发特征参数组合进行数值模拟,生成了包含输入参数和运动特征的初始训练样本.随后,将该样本集(1000组)按比例划分训练集和测试集,并结合网格搜索技术训练得到M-SVR子模型.再后,使用该子模型对所建反演计算细分样本集(8000组)进行预测计算,以地勘报告中记录的3处控制断面泥石流流通速度为基准,通过计算预测结果的均方误差(MSE),筛选出 MSE 值最小的参数组合作为最终的反演结果.最后,进一步分析透水格栅结构在阻滞泥石流运动和控制影响范围的作用.研究成果有助于明晰暴雨型泥石流的灾害演化规律,为减灾措施的科学应用提供理论支撑.
Keyword ：
参数反演 参数反演 多输出支持向量回归 多输出支持向量回归 泥石流 泥石流 透水格栅 透水格栅
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| GB/T 7714 | 林川 , 林彦喆 , 林威伟 et al. 暴雨型泥石流特征参数反演方法及透水格栅效能评价研究 [J]. | 水力发电学报 , 2025 , 44 (2) : 1-14 . |
| MLA | 林川 et al. "暴雨型泥石流特征参数反演方法及透水格栅效能评价研究" . | 水力发电学报 44 . 2 (2025) : 1-14 . |
| APA | 林川 , 林彦喆 , 林威伟 , 郭朝旭 , 黄学钊 , 杜哲镓 et al. 暴雨型泥石流特征参数反演方法及透水格栅效能评价研究 . | 水力发电学报 , 2025 , 44 (2) , 1-14 . |
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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
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| 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) . |
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The current evaluation models for earth-rock dam compaction quality seldom incorporate parameter uncertainty considerations. Additionally, the existing models frequently demonstrate constrained prediction accuracy and generalization capabilities. To resolve these issues, we present an intelligent evaluation method for the compaction quality of earth-rock dams that explicitly accounts for parameter uncertainty. The method utilizes a dynamic inertia weight, an adaptive factor, and a differential evolution strategy to enhance the search capability of the firefly algorithm. Furthermore, the random forest (RF) algorithm's Ntree and Mtry parameters are adaptively optimized through the improved firefly algorithm (IFA) to develop a dam compaction quality prediction model. This model aims to reveal the complex nonlinear mapping relationship between input influencing factors, such as compaction parameters, material source parameters, and meteorological factors, and the compaction quality. The proposed model improves the prediction accuracy, generalization ability, and robustness. The improved firefly optimization-based random forest (IFA-RF) is applied in practical engineering projects, and the results validate that this method can reliably and accurately predict the compaction quality of earth-rock dam construction in real time (R = 0.90107, MSE = 0.0000602, p = 0.000) and thereby guide remedial measures to ensure engineering safety and quality compliance.
Keyword ：
compaction quality compaction quality earth-rock dams earth-rock dams improved firefly optimization-based random forest improved firefly optimization-based random forest uncertainty uncertainty
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| GB/T 7714 | Lin, Weiwei , Yan, Yuling , Xu, Pu et al. Prediction Model for Compaction Quality of Earth-Rock Dams Based on IFA-RF Model [J]. | APPLIED SCIENCES-BASEL , 2025 , 15 (7) . |
| MLA | Lin, Weiwei et al. "Prediction Model for Compaction Quality of Earth-Rock Dams Based on IFA-RF Model" . | APPLIED SCIENCES-BASEL 15 . 7 (2025) . |
| APA | Lin, Weiwei , Yan, Yuling , Xu, Pu , Zhang, Xiao , Zhong, Yichuan . Prediction Model for Compaction Quality of Earth-Rock Dams Based on IFA-RF Model . | APPLIED SCIENCES-BASEL , 2025 , 15 (7) . |
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The deepwater pipelay finite element model often uses relatively small time steps for numerous iterative calculations to ensure sufficient precision, resulting in significant computational time consumption. In this paper, a novel numerical acceleration method is proposed, combining the vector form intrinsic finite element (VFIFE) method with GPU parallel techniques to address this issue. Efficient GPU computational solvers for static and dynamic analyses of deepwater J-lay and S-lay pipelines are developed by utilizing CUDA-based algorithms to handle key mechanical processes, including the calculation of pipeline internal forces and moments, top excitation, pipe-stinger roller interaction, hydrodynamic forces, and pipe-seabed soil interaction, etc. The method is applied to shell element, beam element, and refined beam element models, with coding strategies optimized for GPU parallel execution. Subsequently, localized pipeline solvers and global pipelay solvers are established to showcase the method's potential to significantly reduce computational time while maintaining accuracy. This study emphasizes the advantages of combining the VFIFE method with GPU parallel techniques and contributes efficient computational solvers for the mechanical analysis of deepwater pipeline laying.
Keyword ：
CUDA programming CUDA programming Deepwater installation Deepwater installation GPU parallel technique GPU parallel technique Offshore pipeline Offshore pipeline Vector form intrinsic finite element (VFIFE) Vector form intrinsic finite element (VFIFE)
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| GB/T 7714 | Xu, Pu , Zheng, Jixiang , Lin, Weiwei et al. Numerical acceleration method for static and dynamic analysis of deepwater laying pipelines [J]. | OCEAN ENGINEERING , 2025 , 338 . |
| MLA | Xu, Pu et al. "Numerical acceleration method for static and dynamic analysis of deepwater laying pipelines" . | OCEAN ENGINEERING 338 (2025) . |
| APA | Xu, Pu , Zheng, Jixiang , Lin, Weiwei , Hu, Yiming , Ye, Naiquan . Numerical acceleration method for static and dynamic analysis of deepwater laying pipelines . | OCEAN ENGINEERING , 2025 , 338 . |
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