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学者姓名：豆红强
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Abstract ：
受降雨作用，球状风化花岗岩类土质边坡的土-岩差异风化界面极易演化为优势渗流通道而发生渗流潜蚀，进而加速该类边坡的变形失稳，然而当前有关其渗流潜蚀作用特征、细颗粒迁移规律等的研究仍鲜见开展。基于多孔介质非饱和渗流理论，综合考虑细颗粒运移、潜蚀启动响应与非饱和渗流的耦合关系，提出一种可准确描述土-岩界面渗流潜蚀过程的数值计算框架。采用有限元方法，构建优势流作用下非饱和花岗岩残积土的渗流潜蚀模型，并以均质土柱的渗流潜蚀过程为参考，系统研究3种典型土-岩界面埋藏状态下的优势流潜蚀特性。结果表明：球状风化花岗岩类土质边坡的土-岩界面与基质渗透性存在高度差异性，湿润锋形成向下凹陷的渗透漏斗，且随着降雨的持续，湿润锋的凹陷程度愈发明显；细颗粒流失程度与土-岩界面的埋藏状态相关，其中下填土体工况的优势流潜蚀最为显著，其界面处甚至出现超孔隙水压力，最不利于该类边坡的稳定性。研究成果可为降雨条件下球状风化花岗岩类土质边坡稳定性的准确评价提供科学依据。
Keyword ：
优势流潜蚀 优势流潜蚀 土-岩界面 土-岩界面 多场耦合 多场耦合 有限元 有限元 细颗粒运移 细颗粒运移
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| GB/T 7714 | 豆红强 , 谢森华 , 简文彬 et al. 球状风化花岗岩类土质边坡土-岩界面优势流潜蚀特性研究 [J]. | 岩土力学 , 2024 , 45 (04) : 950-960 . |
| MLA | 豆红强 et al. "球状风化花岗岩类土质边坡土-岩界面优势流潜蚀特性研究" . | 岩土力学 45 . 04 (2024) : 950-960 . |
| APA | 豆红强 , 谢森华 , 简文彬 , 王浩 , 郭朝旭 . 球状风化花岗岩类土质边坡土-岩界面优势流潜蚀特性研究 . | 岩土力学 , 2024 , 45 (04) , 950-960 . |
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Background Extreme wildfires pose a serious threat to forest vegetation and human life because they spread more rapidly and are more intense than conventional wildfires. Detecting extreme wildfires is challenging due to their visual similarities to traditional fires, and existing models primarily detect the presence or absence of fires without focusing on distinguishing extreme wildfires and providing warnings.Aims To test a system for real time detection of four extreme wildfires.Methods We proposed a novel lightweight model, called LEF-YOLO, based on the YOLOv5 framework. To make the model lightweight, we introduce the bottleneck structure of MobileNetv3 and use depthwise separable convolution instead of conventional convolution. To improve the model's detection accuracy, we apply a multiscale feature fusion strategy and use a Coordinate Attention and Spatial Pyramid Pooling-Fast block to enhance feature extraction.Key results The LEF-YOLO model outperformed the comparison model on the extreme wildfire dataset we constructed, with our model having excellent performance of 2.7 GFLOPs, 61 FPS and 87.9% mAP.Conclusions The detection speed and accuracy of LEF-YOLO can be utilised for the real-time detection of four extreme wildfires in forest fire scenes.Implications The system can facilitate fire control decision-making and foster the intersection between fire science and computer science. We tested a lightweight architecture called LEF-YOLO for detecting four extreme wildfires. We found improved detection accuracy through multi-scale fusion and attention mechanism, and constructed four extreme wildfire datasets and compared these with multiple object detection models and lightweight feature extraction networks. This method is beneficial for the development of extreme wildfire field robots.
Keyword ：
convolutional neural networks convolutional neural networks deep learning deep learning extreme wildfire extreme wildfire fire safety fire safety lightweight lightweight multiscale feature fusion multiscale feature fusion object detection object detection YOLO (LEF-YOLO) YOLO (LEF-YOLO)
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| GB/T 7714 | Li, Jianwei , Tang, Huan , Li, Xingdong et al. LEF-YOLO: a lightweight method for intelligent detection of four extreme wildfires based on the YOLO framework [J]. | INTERNATIONAL JOURNAL OF WILDLAND FIRE , 2024 , 33 (1) . |
| MLA | Li, Jianwei et al. "LEF-YOLO: a lightweight method for intelligent detection of four extreme wildfires based on the YOLO framework" . | INTERNATIONAL JOURNAL OF WILDLAND FIRE 33 . 1 (2024) . |
| APA | Li, Jianwei , Tang, Huan , Li, Xingdong , Dou, Hongqiang , Li, Ru . LEF-YOLO: a lightweight method for intelligent detection of four extreme wildfires based on the YOLO framework . | INTERNATIONAL JOURNAL OF WILDLAND FIRE , 2024 , 33 (1) . |
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近年来大规模的旧房拆迁改造及道路建设导致弃土方量剧增，弃土场已成为城市建设的重要附属工程。由于弃土场边坡结构松散、密实性差，较天然边坡更易发生变形破坏，弃土场的安全管理备受关注。为实现复杂工况下弃土场稳定性的快速分析，通过搭建ArcGIS和GeoStudio的松散耦合方法，构建了一套集可视化建模和模拟计算为一体的技术框架。为改良模型可视化效果，配合虚拟钻孔和空间插值方法改良地层曲面精度，完善了弃土场的三维建模技术。采用Python语言为程序接口完成跨平台模型数据交换，实现全时空全方位的多工况弃土场边坡稳定性快速预测。闽清白樟池埔建筑弃土场的应用实例表明，此方案能够实现弃土场任意断面的高质量识别和复杂工况下稳定性变化规律的快速预测，可见松散耦合方法在弃土场稳定性分析过程中具有显著的适用性和实用价值。
Keyword ：
三维建模 三维建模 地理信息系统 地理信息系统 建筑弃土场 建筑弃土场 松散耦合 松散耦合 稳定性分析 稳定性分析
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| GB/T 7714 | 赖滨泓 , 王浩 , 豆红强 . 基于松散耦合方法的弃土场边坡三维建模及稳定性分析：以福州市闽清县白樟池埔建筑弃土场为例 [J]. | 科学技术与工程 , 2024 , 24 (03) : 1184-1191 . |
| MLA | 赖滨泓 et al. "基于松散耦合方法的弃土场边坡三维建模及稳定性分析：以福州市闽清县白樟池埔建筑弃土场为例" . | 科学技术与工程 24 . 03 (2024) : 1184-1191 . |
| APA | 赖滨泓 , 王浩 , 豆红强 . 基于松散耦合方法的弃土场边坡三维建模及稳定性分析：以福州市闽清县白樟池埔建筑弃土场为例 . | 科学技术与工程 , 2024 , 24 (03) , 1184-1191 . |
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The upper bound theorem (UBT) is widely used in the stability analysis of geosynthetic-reinforced slopes (GRSs). However, the existing research seldom considers multiple potential failure mechanisms when using the UBT to design GRSs. First, based on the upper bound theorem of limit analysis, considering multiple failure modes of GRSs, a translation slicing mechanism was constructed. The implicit equation aiming at the safety factor of GRSs was derived, and a simple and effective discrete iteration method was proposed. Second, the rationality of the method proposed in the paper was verified by comparing the existing examples and calculation methods of GRSs. The results showed that the method can consider multiple potential failure modes of GRSs and accurately determine the critical slip surface and minimum safety factor. Finally, the method examined the influences of soil mechanical parameters, reinforcement distribution patterns, and geosynthetic-reinforcement parameters on the critical failure mechanisms and corresponding minimum safety factors. The results showed that when the multiple potential failure mechanisms of GRSs are fully considered, the critical slip surface may not only be sheared from the slope toe for different distribution patterns. It further showed that it is necessary to consider multiple potential failure mechanisms in the design of GRSs. The interval of external critical failure of GRSs increased with the increase of soil cohesion and decreased with the increase of slope angle and internal friction angle. The interval of the internal critical failure of GRSs increased with the increase of slope angle and internal friction angle. The research results provide a novel idea and theoretical support for the stability calculation of GRSs.
Keyword ：
Geosynthetic-reinforced slopes Geosynthetic-reinforced slopes Potential failure mechanisms Potential failure mechanisms Safety factor Safety factor Stability analysis Stability analysis Upper bound theorem Upper bound theorem
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| GB/T 7714 | Li, Peng-Yu , Dou, Hong-Qiang , Nie, Wen-Feng . Stability Analysis of Geosynthetic-Reinforced Slopes Considering Multiple Potential Failure Mechanisms Based on the Upper Bound Theorem [J]. | INTERNATIONAL JOURNAL OF GEOMECHANICS , 2024 , 24 (2) . |
| MLA | Li, Peng-Yu et al. "Stability Analysis of Geosynthetic-Reinforced Slopes Considering Multiple Potential Failure Mechanisms Based on the Upper Bound Theorem" . | INTERNATIONAL JOURNAL OF GEOMECHANICS 24 . 2 (2024) . |
| APA | Li, Peng-Yu , Dou, Hong-Qiang , Nie, Wen-Feng . Stability Analysis of Geosynthetic-Reinforced Slopes Considering Multiple Potential Failure Mechanisms Based on the Upper Bound Theorem . | INTERNATIONAL JOURNAL OF GEOMECHANICS , 2024 , 24 (2) . |
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突发性自然灾害经常对大型工程的建筑与生产设施造成重大损失,而受损设施的维修和重建过程常出现物资供给不足、物资供给效率低以及物资分配公平性等问题.本文以某大型光伏电站为依托,从应急物资节点选址影响因素入手,建立受突发性灾害作用后的应急物资节点选址评价指标系统,采用AHP-CRITIC(analyt-ic hierarchy process-criteria importance through intercriteria correlation)组合赋权法实现应急物资存放点选址评价结果的计算.同时,基于研究区内部路网开展大型光伏场地路口至多物资存储节点的最优路径研究,并采用GIS(geographic information system)技术求解出最短运输路径.结果表明:该大型光伏场地应急物资节点选址评价结果等级为"好"与"较好"的区域主要分布在研究区北部,占总面积的33.38%;基于最短运输路径原则通过综合对比线路总长与重复路线占比,由入口3出发至各应急物资节点的路径为最优路线.研究成果可为类似大型工程的安全运营和应急管理提供技术支持和参考.
Keyword ：
GIS GIS 光伏电站 光伏电站 应急物资 应急物资 最优路线选取 最优路线选取 节点选址 节点选址
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| GB/T 7714 | 林爱军 , 李如 , 豆红强 et al. 基于AHP-CRITIC和GIS的大型光伏电站应急物资节点选址与最优路线选取 [J]. | 科技通报 , 2024 , 40 (7) : 38-45 . |
| MLA | 林爱军 et al. "基于AHP-CRITIC和GIS的大型光伏电站应急物资节点选址与最优路线选取" . | 科技通报 40 . 7 (2024) : 38-45 . |
| APA | 林爱军 , 李如 , 豆红强 , 黄思懿 . 基于AHP-CRITIC和GIS的大型光伏电站应急物资节点选址与最优路线选取 . | 科技通报 , 2024 , 40 (7) , 38-45 . |
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[目的]揭示动水头作用下花岗岩风化土侵蚀演化的动态过程，研究动态水力条件对土体内部侵蚀发展的影响，探明颗粒迁移规律与内部侵蚀机理，为深入研究花岗岩边坡的破坏模式与促滑机理提供理论依据。[方法]设计竖向土柱渗流装置，开展上升水头与正弦水头条件下花岗岩风化土柱的渗流试验，基于渗流土柱顶面与侧面的试验现象，从渗流速度变化、颗粒流失量变化和渗流前后颗粒级配、质量变化等方面，分析动水头作用下花岗岩风化土内部侵蚀的发育特征。[结果](1)花岗岩风化土粒径差异较大，在骨架间存在微小孔隙，渗流冲刷作用使得土颗粒通过土体骨架间的孔隙运移流失。在内部侵蚀过程中，细颗粒流失量相对较多，粗颗粒流失量较少。(2)土体内部侵蚀作用是渐进发展的过程，在土体薄弱区域的结构最先产生变形与破坏。试验中渗流泉眼由土柱边界开始发展到土柱中部区域，渗流通道沿着渗流方向自下而上发育，水力条件短时间内发生显著变化会造成渗流通道快速贯通。(3)土体内部侵蚀作用将随渗流时间推进而趋于稳定，但正弦水头将“激活”土颗粒运动，加剧土体的内部侵蚀作用。加大水头变化幅度或减小水头变化周期，能够加剧土体内部侵蚀作用，导致水流运移速度加快以及颗粒迁移流失量增多。[结论]水力条件变化对土体内部侵蚀作用存在显著影响，正弦水头更能加剧颗粒迁移流失。
Keyword ：
内部侵蚀 内部侵蚀 动水头 动水头 模型试验 模型试验 花岗岩风化土 花岗岩风化土
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| GB/T 7714 | 王浩 , 严耿明 , 李传东 et al. 动水头作用下花岗岩风化土的内部侵蚀机理 [J]. | 水土保持通报 , 2024 , 44 (02) : 80-90 . |
| MLA | 王浩 et al. "动水头作用下花岗岩风化土的内部侵蚀机理" . | 水土保持通报 44 . 02 (2024) : 80-90 . |
| APA | 王浩 , 严耿明 , 李传东 , 豆红强 . 动水头作用下花岗岩风化土的内部侵蚀机理 . | 水土保持通报 , 2024 , 44 (02) , 80-90 . |
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当前大型光伏电场逐步向高寒高海拔地区发展，为准确评价高寒高海拔区的冻融侵蚀灾害对大型光伏电站安全运营的影响，依托高寒高海拔区某大型光伏电站工程，通过选取合适的环境因子开展研究区冻融侵蚀强度的初步计算，并耦合人类工程量化指标提出具有针对性与合理性的光伏场地冻融侵蚀强度评价模型，探讨了多环境因子和人类工程条件下的冻融侵蚀强度与其空间分布。结果表明，该光伏场址冻融侵蚀状况总体上属于中度侵蚀，其冻融侵蚀强度分布具有一定的集中性与空间分异性，且其空间分布格局主要受地形、气候和植被因素影响。同时，光伏电站的建设对场地冻融侵蚀强度具有一定的削弱作用，研究成果可为类似大型工程的冻融侵蚀评价提供参考和借鉴。
Keyword ：
光伏电站 光伏电站 冻融侵蚀强度 冻融侵蚀强度 层次分析-优劣解距离组合方法 层次分析-优劣解距离组合方法 环境因子 环境因子 高寒高海拔 高寒高海拔
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| GB/T 7714 | 康子军 , 韩放 , 豆红强 et al. 高寒高海拔区大型光伏电站场地冻融侵蚀强度分区评价 [J]. | 自然灾害学报 , 2024 , 33 (04) : 198-210 . |
| MLA | 康子军 et al. "高寒高海拔区大型光伏电站场地冻融侵蚀强度分区评价" . | 自然灾害学报 33 . 04 (2024) : 198-210 . |
| APA | 康子军 , 韩放 , 豆红强 , 黄思懿 . 高寒高海拔区大型光伏电站场地冻融侵蚀强度分区评价 . | 自然灾害学报 , 2024 , 33 (04) , 198-210 . |
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闽东南高植被覆盖区台风暴雨型滑坡频发，探索在植被、降雨、强风作用下的滑坡失稳演化及其规律对揭示高植被覆盖区台风暴雨型滑坡的成灾机制、监测预警具有重要的理论及实际意义。以福建省永泰县洋斜滑坡为研究对象，对斜坡上的毛竹进行拉拔试验以及对根土区进行单环入渗试验，结合Green-Ampt模型和无限边坡模型，研究在台风暴雨作用下植被土坡的稳定性响应规律及其稳定性分析方法。结果表明：（1）毛竹抵御的极限风速主要在18～30 m/s的范围之内，对应8～11级风力范围。（2）毛竹迎风区土体入渗能力随着风速的增长而增长，0～12 m/s内入渗能力基本不变，12 m/s以上入渗能力迅速增长。（3）根土区湿润锋的迁移速度随着风速和降雨强度的增大而加快，台风通过植被扰动土体形成优势渗流，对入渗的影响主要在于加快了湿润锋的迁移速度。风-雨-植被协同作用下，风速是影响土体入渗能力的重要因素。（4）台风暴雨作用下风荷载通过植被加快根区土降雨入渗，从而增大湿润锋迁移速度，是台风暴雨型滑坡孕灾、成灾的重要环节。
Keyword ：
湿润锋 湿润锋 滑坡 滑坡 边坡稳定性 边坡稳定性 降雨 降雨 风荷载 风荷载
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| GB/T 7714 | 林滨强 , 章德生 , 简文彬 et al. 风驱雨作用下植被斜坡稳定性响应研究 [J]. | 岩土力学 , 2024 , (09) . |
| MLA | 林滨强 et al. "风驱雨作用下植被斜坡稳定性响应研究" . | 岩土力学 09 (2024) . |
| APA | 林滨强 , 章德生 , 简文彬 , 豆红强 , 王浩 , 樊秀峰 . 风驱雨作用下植被斜坡稳定性响应研究 . | 岩土力学 , 2024 , (09) . |
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To investigate the influence of tree roots on the triggering mechanism of shallow landslides, this study focused on a typical vegetation-covered cluster landslide in the Wuping area of China. A tree root profile investigation and a double-ring infiltration test were conducted. The undisturbed soil samples were collected for laboratory tests to measure the influence of tree vegetation on soil physical and hydraulic properties. The root reinforcement effect is limited by the depth of root distribution, with over 90% of the roots situated above the slip surface. The presence of roots increases the resistance of the soil to disintegration. The soil disintegration in the 0-80 cm layer was less than 25% after 24 h of water immersion and the soil undergoes complete disintegration at a depth of 180-200 cm within 120 s. Tree roots facilitate the infiltration of soil, and Ks at 0 m was 11.21 times than that at 2 m, and the interface between soils with roots and soils without roots may become a sliding surface. Under extreme rainfall conditions, the root system promotes water infiltration, accelerates the softening and disintegration of the soil on the sliding surface, which adversely affects the stability of landslides.
Keyword ：
disintegration disintegration heavy rainfall heavy rainfall hydraulic property hydraulic property root-soil root-soil Shallow landslide Shallow landslide
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| GB/T 7714 | Lin, Yunzhao , Jian, Wenbin , Wu, Yilong et al. Effect of tree roots on heavy rainfall-induced shallow landslides [J]. | GEOMATICS NATURAL HAZARDS & RISK , 2024 , 15 (1) . |
| MLA | Lin, Yunzhao et al. "Effect of tree roots on heavy rainfall-induced shallow landslides" . | GEOMATICS NATURAL HAZARDS & RISK 15 . 1 (2024) . |
| APA | Lin, Yunzhao , Jian, Wenbin , Wu, Yilong , Zhu, Zuteng , Wang, Hao , Dou, Hongqiang et al. Effect of tree roots on heavy rainfall-induced shallow landslides . | GEOMATICS NATURAL HAZARDS & RISK , 2024 , 15 (1) . |
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The current assessment index of the geological hazard vulnerability assessment for mountain road network is relatively simple, and the assessment methods used are subjective, complex, and inefficient. This study proposes a prediction model for geological hazard vulnerability assessment of mountain road network incorporating machine learning algorithms. First, based on the quantification of the characteristics of the mountain road network and the local rescue forces, an objective and reasonable index-based system of vulnerability assessment of the mountain road network was constructed by combining the population, economic, and material factors. Second, the FAHP and AHP-TOPSIS were applied for the development of the vulnerability assessment models to carry out the preliminary vulnerability assessment for different road types. Third, the results of the preliminary vulnerability assessment were used as the sample set to build a road vulnerability prediction model using SVM, RF, and BPNN algorithms. Finally, the five-fold cross-validation and statistical parameter accuracy analysis were conducted to determine the most reasonable model with the highest prediction accuracy for geological hazard vulnerability mapping of the mountain road network. The results indicated that the vulnerability prediction model based on the FAHP sample set using the RF algorithm demonstrated the highest accuracy and robustness.
Keyword ：
machine learning machine learning Mountain roads Mountain roads road network features road network features vulnerability mapping vulnerability mapping
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| GB/T 7714 | Huang, Siyi , Dou, Hongqiang , Jian, Wenbin et al. Spatial prediction of the geological hazard vulnerability of mountain road network using machine learning algorithms [J]. | GEOMATICS NATURAL HAZARDS & RISK , 2023 , 14 (1) . |
| MLA | Huang, Siyi et al. "Spatial prediction of the geological hazard vulnerability of mountain road network using machine learning algorithms" . | GEOMATICS NATURAL HAZARDS & RISK 14 . 1 (2023) . |
| APA | Huang, Siyi , Dou, Hongqiang , Jian, Wenbin , Guo, Chaoxu , Sun, Yongxin . Spatial prediction of the geological hazard vulnerability of mountain road network using machine learning algorithms . | GEOMATICS NATURAL HAZARDS & RISK , 2023 , 14 (1) . |
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