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学者姓名：张挺
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Abstract ：
Granular fracture holds significant implications in material mechanics. However, the previous studies in ordinary state-based peridynamic (OSB-PD) framework often neglect the internal crystalline structure of particles or only consider limited crystal orientation. To address this gap, a novel OSB-PD model for granular fracture within polycrystalline materials is proposed, in which the periodic functions are incorporated in the PD strain energy density, taking into account the inherent random orientation in cubic crystals. By comparing energy density from PD and the classical continuum mechanics, four PD material parameters are defined. Moreover, the corresponding surface correction method in the global coordinate system is also proposed. Several numerical examples including fracture analysis of polycrystalline materials are conducted to validate the effectiveness of the proposed method. The proposed ordinary state-based peridynamic model offers a fresh perspective for investigating granular fracture behaviors within polycrystalline materials.
Keyword ：
Cubic crystals Cubic crystals Grain orientation Grain orientation Granular fracture Granular fracture Peridynamics Peridynamics Polycrystalline materials Polycrystalline materials
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| GB/T 7714 | Zhang, Ting , Gu, Tiantian , Jiang, Jin et al. An ordinary state-based peridynamic model for granular fracture in polycrystalline materials with arbitrary orientations in cubic crystals [J]. | ENGINEERING FRACTURE MECHANICS , 2024 , 301 . |
| MLA | Zhang, Ting et al. "An ordinary state-based peridynamic model for granular fracture in polycrystalline materials with arbitrary orientations in cubic crystals" . | ENGINEERING FRACTURE MECHANICS 301 (2024) . |
| APA | Zhang, Ting , Gu, Tiantian , Jiang, Jin , Zhang, Jianzhi , Zhou, Xiaoping . An ordinary state-based peridynamic model for granular fracture in polycrystalline materials with arbitrary orientations in cubic crystals . | ENGINEERING FRACTURE MECHANICS , 2024 , 301 . |
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Neural network models have been widely used in runoff forecasting, but are often criticized for their lack of physical interpretability. In this study, we present a simple but useful approach to developing hydrological models by designing neural networks based on the principles of runoff generation and concentration, which we refer to as a Hydrological Process-based Neural Network (HPNN) model. The Convolutional neural network (CNN) and softmax function are used because of their similar formula to the conventional runoff generation and unit hydrograph approach used in hydrology. We apply the HPNN model and four other benchmark models to forecast runoff in two catchments (Yutan and Chenda) in China. Results show that the HPNN model has higher computational efficiency, its parameters are interpretable and closely linked to the processes of runoff generation and concentration, and the HPNN model outperforms conventional GRU-based models.
Keyword ：
HPNN model HPNN model Neural network Neural network Physical interpretability Physical interpretability Runoff forecasting Runoff forecasting
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| GB/T 7714 | Gao, Shuai , Zhang, Shuo , Huang, Yuefei et al. A hydrological process-based neural network model for hourly runoff forecasting [J]. | ENVIRONMENTAL MODELLING & SOFTWARE , 2024 , 176 . |
| MLA | Gao, Shuai et al. "A hydrological process-based neural network model for hourly runoff forecasting" . | ENVIRONMENTAL MODELLING & SOFTWARE 176 (2024) . |
| APA | Gao, Shuai , Zhang, Shuo , Huang, Yuefei , Han, Jingcheng , Zhang, Ting , Wang, Guangqian . A hydrological process-based neural network model for hourly runoff forecasting . | ENVIRONMENTAL MODELLING & SOFTWARE , 2024 , 176 . |
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为准确获取山区小流域的降水空间分布及其资源量, 采用Kriging插值法对低分辨率卫星数据进行空间降尺度处理, 通过长短期记忆网络(Long Short-Term Memory, LSTM)将局部卫星与观测数据进行降水融合, 引入前期降水信息加强卫星与观测降水之间的时间相关性, 并利用该模型进行流域降水空间分布估计。结果表明: 从空间分布来看, 融合模型对暴雨中心位置的捕捉更加精确; 从降水量来看, 所提模型在短时强降水下的探测率和临界成功指数分别为0.60和0.50, 能够改善原始低分辨率卫星降水数据, 使其更接近实际情况; 从雨量站数量来看, 融合降水的精度随着站点数量的增加而提高, 当站点数量达到某个临界值时, 融合降水的精度趋于稳定。Kriging-LSTM模型为准确获取山区小流域的降水资源提供了新思路。
Keyword ：
Kriging插值法 Kriging插值法 山区小流域 山区小流域 长短期记忆网络(LSTM) 长短期记忆网络(LSTM) 降水空间估计 降水空间估计 降水融合 降水融合
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| GB/T 7714 | 詹昌洵 , 张挺 , 蒋嘉伟 . 基于多源数据的山区小流域降水融合模型 [J]. | 水科学进展 , 2024 , 35 (1) : 74-84 . |
| MLA | 詹昌洵 et al. "基于多源数据的山区小流域降水融合模型" . | 水科学进展 35 . 1 (2024) : 74-84 . |
| APA | 詹昌洵 , 张挺 , 蒋嘉伟 . 基于多源数据的山区小流域降水融合模型 . | 水科学进展 , 2024 , 35 (1) , 74-84 . |
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In addressing the intricate dynamic responses of pipeline conveying fluid characterized by spatiotemporal multiscales and multi-modal contributions, Fourier feature-embedded physics-information neural network (FF-PINN) is proposed. By introducing Fourier feature mapping to decompose the temporal and spatial scale information, FF-PINN precisely captures the relatively low-frequencies on the macroscopic time scale as well as the relatively high-frequencies on the microscopic scale of the pipeline's vibration. This approach significantly overcomes the spectral bias encountered by PINN when learning high-frequency information. To verify the effectiveness and accuracy of this method, the proposed FF-PINN is applied to solve the pipeline conveying fluid model with fixed support at both ends. The relative L2 error between the obtained results and the reference solution is 1.8 x 10-2, concurrently with a significant reduction in computational time. Additionally, an analysis of hyperparameter sigma selection is conducted to evaluate its impact on the performance of FF-PINN, while establishing the correspondence between hyperparameter and eigenvector frequency. The results demonstrate that choosing appropriate hyperparameters empowers FF-PINN to better learn the vibration of specific frequencies, enabling the accurate modeling of pipeline vibrations' dynamic response. It provides a potent solution for solving spatiotemporal multi-scale complexity problems involving the superposition of high-and low-frequencies.
Keyword ：
Fourier feature Fourier feature Physics-information neural network Physics-information neural network Pipeline conveying fluid Pipeline conveying fluid Spatiotemporal multi-scales Spatiotemporal multi-scales Vibration characteristics Vibration characteristics
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| GB/T 7714 | Zhang, Ting , Yan, Rui , Zhang, Siqian et al. Application of Fourier feature physics-information neural network in model of pipeline conveying fluid [J]. | THIN-WALLED STRUCTURES , 2024 , 198 . |
| MLA | Zhang, Ting et al. "Application of Fourier feature physics-information neural network in model of pipeline conveying fluid" . | THIN-WALLED STRUCTURES 198 (2024) . |
| APA | Zhang, Ting , Yan, Rui , Zhang, Siqian , Yang, Dingying , Chen, Anhao . Application of Fourier feature physics-information neural network in model of pipeline conveying fluid . | THIN-WALLED STRUCTURES , 2024 , 198 . |
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The semiconductors have created a great avenue in visible-light photocatalysis and recently insulator photocatalysis has become an appealing research spot. Herein, a novel waste eggshells derived AgBr-CaCO3 heterostructure was finely designed and constructed through a simple co-precipitation method for efficient antibiotics photo-degradation under visible light. The optimal heterostructure achieved a pseudo-first-order kinetic constant of 6.0 x 10(-2) min(-1) for tetracycline (TC) degradation, with 72 and seven-fold enhancement than eggshell (ES) and AgBr, which also exhibited superior performance towards ofloxacin and sulfamethoxazole. The density functional theory (DFT) calculations revealed that the covalent interaction of Ag-O provided a specific channel for interfacial electrons transfer from the semiconductor to the insulator and thus greatly elevated the photocatalytic activity. The highly selective .CO3- radicals generated by ES, as the main active species, also accelerated the antibiotics degradation. Furthermore, the possible degradation pathways, aquatic toxicity and mutagenicity variation of TC were thoroughly elucidated. This current study illuminated a new pathway for the design of insulator photocatalysts based upon waste solids and demonstrated its application prospect in the field of antibiotics degradation.
Keyword ：
AgBr AgBr CaCO3 CaCO3 Heterostructure Heterostructure Tetracycline Tetracycline Visible -light photocatalysis Visible -light photocatalysis
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| GB/T 7714 | Chen, Qiaoshan , Gao, Ming , Yu, Mingfei et al. Efficient photo-degradation of antibiotics by waste eggshells derived AgBr-CaCO3 heterostructure under visible light [J]. | SEPARATION AND PURIFICATION TECHNOLOGY , 2023 , 314 . |
| MLA | Chen, Qiaoshan et al. "Efficient photo-degradation of antibiotics by waste eggshells derived AgBr-CaCO3 heterostructure under visible light" . | SEPARATION AND PURIFICATION TECHNOLOGY 314 (2023) . |
| APA | Chen, Qiaoshan , Gao, Ming , Yu, Mingfei , Zhang, Ting , Wang, Jianchun , Bi, Jinhong et al. Efficient photo-degradation of antibiotics by waste eggshells derived AgBr-CaCO3 heterostructure under visible light . | SEPARATION AND PURIFICATION TECHNOLOGY , 2023 , 314 . |
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This research proposed an integrated strategy for building performance optimization from the whole life cycle perspective to explore the optimal building scheme. After the feature elimination, the ensemble learning model (ELM) was trained to obtain a high-precision model for predicting life cycle carbon emissions (LCCE), life cycle costs (LCC), and indoor discomfort hours (IDH). Then, the optimal optimization algorithm was selected among three different optimization algorithms. Finally, the best building scheme was chosen according to the newly proposed solution. The results showed that the ELM could achieve high prediction efficiency by combining input feature evaluation and screening, multi-sampling methods, and hyperparameter optimization. The R2 value of ELM can reach 0.980, while the Two-Archive Evolutionary Algorithm for Constrained multi-objective optimi-zation (C-TAEA) was the optimal optimization algorithm. The best equilibrium solution proposed in this study solved the problem of different optimization ranges of different objectives and maximized the optimization value. Finally, the best equilibrium scheme reduced the LCCE by 34.7%, the LCC by 13.9%, and the IDH by 26.6%. Therefore, this strategy can efficiently optimize building objectives and produce a more balanced and optimal building scheme, thus making it widely applicable in building performance optimization.
Keyword ：
Best equilibrium solution Best equilibrium solution Building performance optimization Building performance optimization Carbon emission Carbon emission Machine learning Machine learning Sensitivity analysis Sensitivity analysis
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| GB/T 7714 | Chen, Ruijun , Tsay, Yaw-Shyan , Zhang, Ting . A multi-objective optimization strategy for building carbon emission from the whole life cycle perspective [J]. | ENERGY , 2023 , 262 . |
| MLA | Chen, Ruijun et al. "A multi-objective optimization strategy for building carbon emission from the whole life cycle perspective" . | ENERGY 262 (2023) . |
| APA | Chen, Ruijun , Tsay, Yaw-Shyan , Zhang, Ting . A multi-objective optimization strategy for building carbon emission from the whole life cycle perspective . | ENERGY , 2023 , 262 . |
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Escalation in flash floods and the enhanced devastations, especially in the arid and semiarid regions of the world has required precise mapping of the flash flood susceptible zones. In this study, we applied six novel credal decision tree (CDT)-based ensemble models-1. CDT, 2. CDT Alternative Decision Tree (ADTree), 3. CDT- Reduced Error Pruning Tree (REPT), 4. CDT- Rotational Forest (RF), 5. CDT-FT, 6. CDT- Naive Bias Tree (NBTree). For preparing the flash flood susceptibility maps (FFSM), 206 flood locations were selected in the Neka-roud watershed of Iran with 70% as training data and 30% as testing data. Moreover, 18 flood conditing factors were considered for FFSM and a multi-colinearity test was performed for determining the role of the factors. Our results show that the distance from the stream plays a vital role in flash floods. The CDT-FT is the best-fit model out of the six novel algorithms employed in this study as demonstrated by the highest values of the area under the curve (AUC) of the receiver operating curve (ROC) (AUROC 0.986 for training data and 0.981 for testing data). Our study provides a novel approach and useful tool for flood management.
Keyword ：
Credal decision tree Credal decision tree Flash flood mapping Flash flood mapping Flood management Flood management Machine learning algorithms Machine learning algorithms Neka-roud watershed Neka-roud watershed Novel Ensemble models Novel Ensemble models
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| GB/T 7714 | Yang, Dingying , Zhang, Ting , Arabameri, Alireza et al. Flash-flood susceptibility mapping: a novel credal decision tree-based ensemble approaches [J]. | EARTH SCIENCE INFORMATICS , 2023 , 16 (4) : 3143-3161 . |
| MLA | Yang, Dingying et al. "Flash-flood susceptibility mapping: a novel credal decision tree-based ensemble approaches" . | EARTH SCIENCE INFORMATICS 16 . 4 (2023) : 3143-3161 . |
| APA | Yang, Dingying , Zhang, Ting , Arabameri, Alireza , Santosh, M. , Saha, Ujwal Deep , Islam, Aznarul . Flash-flood susceptibility mapping: a novel credal decision tree-based ensemble approaches . | EARTH SCIENCE INFORMATICS , 2023 , 16 (4) , 3143-3161 . |
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Abstract ：
Escalation in flash floods and the enhanced devastations, especially in the arid and semiarid regions of the world has required precise mapping of the flash flood susceptible zones. In this study, we applied six novel credal decision tree (CDT)-based ensemble models-1. CDT, 2. CDT Alternative Decision Tree (ADTree), 3. CDT- Reduced Error Pruning Tree (REPT), 4. CDT- Rotational Forest (RF), 5. CDT-FT, 6. CDT- Naive Bias Tree (NBTree). For preparing the flash flood susceptibility maps (FFSM), 206 flood locations were selected in the Neka-roud watershed of Iran with 70% as training data and 30% as testing data. Moreover, 18 flood conditing factors were considered for FFSM and a multi-colinearity test was performed for determining the role of the factors. Our results show that the distance from the stream plays a vital role in flash floods. The CDT-FT is the best-fit model out of the six novel algorithms employed in this study as demonstrated by the highest values of the area under the curve (AUC) of the receiver operating curve (ROC) (AUROC 0.986 for training data and 0.981 for testing data). Our study provides a novel approach and useful tool for flood management.
Keyword ：
Credal decision tree Credal decision tree Flash flood mapping Flash flood mapping Flood management Flood management Machine learning algorithms Machine learning algorithms Neka-roud watershed Neka-roud watershed Novel Ensemble models Novel Ensemble models
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| GB/T 7714 | Yang, Dingying , Zhang, Ting , Arabameri, Alireza et al. Flash-flood susceptibility mapping: a novel credal decision tree-based ensemble approaches [J]. | EARTH SCIENCE INFORMATICS , 2023 , 16 (4) : 3143-3161 . |
| MLA | Yang, Dingying et al. "Flash-flood susceptibility mapping: a novel credal decision tree-based ensemble approaches" . | EARTH SCIENCE INFORMATICS 16 . 4 (2023) : 3143-3161 . |
| APA | Yang, Dingying , Zhang, Ting , Arabameri, Alireza , Santosh, M. , Saha, Ujwal Deep , Islam, Aznarul . Flash-flood susceptibility mapping: a novel credal decision tree-based ensemble approaches . | EARTH SCIENCE INFORMATICS , 2023 , 16 (4) , 3143-3161 . |
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Deformations in dam structures can have a critical impact on dam safety and life. Accurate methods for dam deformation prediction and safety evaluation are thus highly needed. Dam deformations can be predicted based on many factors. The analysis of these influences on the deformation of the dam reveals a problem that deserves further attention: dam deformation lags behind environmental factors of the water level and temperature as well as the time lag of the temporal dam deformation data. In this paper, a hybrid deep learning model is proposed to enhance the accuracy of dam deformation forecasting based on lag indices of these factors. In particular, dam deformations are predicted using deep networks based on gated recurrent units (GRUs), which can effectively capture the temporal characteristics of dam deformation. In addition, an improved particle swarm optimization (IPSO) algorithm is used for optimizing the GRU hyperparameters. Furthermore, the complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) and the partial autocorrelation function (PACF) are exploited to select the lag factor indices. The accuracy and effectiveness of the proposed CEEMDAN-PACF-IPSO-GRU hybrid model were evaluated and compared with those of other existing models in terms of four different evaluation indices (MAE, MSE, R-2, and RMSE) and using 9-year historical data for the case of a pulp-masonry arch dam in China. The experimental results show that our model outperforms other models in terms of the deformation prediction accuracy (R-2 increased by 0.16%-9.74%, while the other indices increased by 14.55% to reach 96.69%), and hence represents a promising framework for general analysis of dam deformations and other types of structural behavior. (C) 2022 American Society of Civil Engineers.
Keyword ：
Complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) Complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) Dam deformation Dam deformation Gated recurrent unit (GRU) Gated recurrent unit (GRU) Improved particle swarm optimization (IPSO) Improved particle swarm optimization (IPSO) Partial autocorrelation function (PACF) Partial autocorrelation function (PACF)
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| GB/T 7714 | Lin, Chuan , Wang, Xiangyu , Su, Yan et al. Deformation Forecasting of Pulp-Masonry Arch Dams via a Hybrid Model Based on CEEMDAN Considering the Lag of Influencing Factors [J]. | JOURNAL OF STRUCTURAL ENGINEERING , 2022 , 148 (7) . |
| MLA | Lin, Chuan et al. "Deformation Forecasting of Pulp-Masonry Arch Dams via a Hybrid Model Based on CEEMDAN Considering the Lag of Influencing Factors" . | JOURNAL OF STRUCTURAL ENGINEERING 148 . 7 (2022) . |
| APA | Lin, Chuan , Wang, Xiangyu , Su, Yan , Zhang, Ting , Lin, Chaoning . Deformation Forecasting of Pulp-Masonry Arch Dams via a Hybrid Model Based on CEEMDAN Considering the Lag of Influencing Factors . | JOURNAL OF STRUCTURAL ENGINEERING , 2022 , 148 (7) . |
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构建高精度的变形预测模型对于大坝风险评估及防治措施制定具有极其重要的意义。传统的大坝变形预测模型鲜有针对大坝的变形滞后性特点以及变形特征因子的影响性分析与评估，这会对模型的预测精度造成较大的影响，并导致模型缺乏可解释性。针对上述问题，本文提出一种结合时间注意力机制的门控循环单元神经网络（GRU）架构。首先通过卡尔曼滤波（Kalman Filter）对原始大坝变形数据中由于监测器异常导致的随机噪声与异常值进行处理。其次，利用随机森林（RF）对各变形特征因子的重要性进行分析和评估，筛选模型输入的特征因子。最后，针对大坝变形的滞后性，利用时间注意力机制进一步提高GRU模型对时间维度上的动态特征关注度...
Keyword ：
变形滞后性 变形滞后性 大坝变形预测 大坝变形预测 时间注意力机制 时间注意力机制 深度学习 深度学习 门控循环单元神经网络 门控循环单元神经网络
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| GB/T 7714 | 苏燕 , 付家源 , 林川 et al. 基于时间注意力机制的大坝动态变形预测模型 [J]. | 水力发电学报 , 2022 , 41 (07) : 72-84 . |
| MLA | 苏燕 et al. "基于时间注意力机制的大坝动态变形预测模型" . | 水力发电学报 41 . 07 (2022) : 72-84 . |
| APA | 苏燕 , 付家源 , 林川 , 陈泽钦 , 翁锴亮 , 张挺 . 基于时间注意力机制的大坝动态变形预测模型 . | 水力发电学报 , 2022 , 41 (07) , 72-84 . |
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