Discontinuities in rock masses critically impact the stability and safety of underground engineering.Mainstream discontinuities identificationmethods,which rely on normal vector estimation and clustering algorithms,su...Discontinuities in rock masses critically impact the stability and safety of underground engineering.Mainstream discontinuities identificationmethods,which rely on normal vector estimation and clustering algorithms,suffer from accuracy degradation,omission of critical discontinuities when orientation density is unevenly distributed,and need manual intervention.To overcome these limitations,this paper introduces a novel discontinuities identificationmethod based on geometric feature analysis of rock mass.By analyzing spatial distribution variability of point cloud and integrating an adaptive region growing algorithm,the method accurately detects independent discontinuities under complex geological conditions.Given that rock mass orientations typically follow a Fisher distribution,an adaptive hierarchical clustering algorithm based on statistical analysis is employed to automatically determine the optimal number of structural sets,eliminating the need for preset clusters or thresholds inherent in traditional methods.The proposed approach effectively handles diverse rock mass shapes and sizes,leveraging both local and global geometric features to minimize noise interference.Experimental validation on three real-world rock mass models,alongside comparisons with three conventional directional clustering algorithms,demonstrates superior accuracy and robustness in identifying optimal discontinuity sets.The proposed method offers a reliable and efficienttool for discontinuities detection and grouping in underground engineering,significantlyenhancing design and construction outcomes.展开更多
目的利用卷积神经网络分析颅脑影像,以预测高原人群的早期机能衰退。方法筛选2024年5月至2025年1月联勤保障部队第九八八医院对口支援的西藏错那县人民医院540例早期衰弱人群作为研究组,年龄40岁~55岁。300例正常人群作为对照组,年龄、...目的利用卷积神经网络分析颅脑影像,以预测高原人群的早期机能衰退。方法筛选2024年5月至2025年1月联勤保障部队第九八八医院对口支援的西藏错那县人民医院540例早期衰弱人群作为研究组,年龄40岁~55岁。300例正常人群作为对照组,年龄、性别与研究组差异无统计学意义。收集脑核磁共振成像参数,利用主成分分析筛选颅脑影响机能衰退的影像特征。对比临床机能衰退评估标准,建立卷积神经网络、支持向量机、随机森林决策树智能模型,利用受试者工作特征曲线下面积(area under the curve,AUC)、精确率、净重新分类指数、F1分数、校准曲线等对比模型性能。通过准确率、特异性、敏感度等指标对各模型的临床效果进行评价。结果利用颅脑影像参数构建的临床预测模型。卷积神经网络模型预测效能最高,其训练集及测试集AUC(95%CI)分别为0.804(0.750~0.858)、0.803(0.749~0.857),训练集特异度、敏感度及准确率分别为86.52%、87.31%及87.24%,测试集特异度、敏感度及准确率分别为86.78%、86.98%及87.30%。结论基于颅脑影像参数建立的卷积神经网络模型,能有效识别并预测高原环境对人体生理机能产生的早期衰退影响,这为建立高原地区早期健康预警系统提供了依据。展开更多
基金the National Key Research and Development Program of China(Grant No.2023YFC3009400).
文摘Discontinuities in rock masses critically impact the stability and safety of underground engineering.Mainstream discontinuities identificationmethods,which rely on normal vector estimation and clustering algorithms,suffer from accuracy degradation,omission of critical discontinuities when orientation density is unevenly distributed,and need manual intervention.To overcome these limitations,this paper introduces a novel discontinuities identificationmethod based on geometric feature analysis of rock mass.By analyzing spatial distribution variability of point cloud and integrating an adaptive region growing algorithm,the method accurately detects independent discontinuities under complex geological conditions.Given that rock mass orientations typically follow a Fisher distribution,an adaptive hierarchical clustering algorithm based on statistical analysis is employed to automatically determine the optimal number of structural sets,eliminating the need for preset clusters or thresholds inherent in traditional methods.The proposed approach effectively handles diverse rock mass shapes and sizes,leveraging both local and global geometric features to minimize noise interference.Experimental validation on three real-world rock mass models,alongside comparisons with three conventional directional clustering algorithms,demonstrates superior accuracy and robustness in identifying optimal discontinuity sets.The proposed method offers a reliable and efficienttool for discontinuities detection and grouping in underground engineering,significantlyenhancing design and construction outcomes.
文摘目的利用卷积神经网络分析颅脑影像,以预测高原人群的早期机能衰退。方法筛选2024年5月至2025年1月联勤保障部队第九八八医院对口支援的西藏错那县人民医院540例早期衰弱人群作为研究组,年龄40岁~55岁。300例正常人群作为对照组,年龄、性别与研究组差异无统计学意义。收集脑核磁共振成像参数,利用主成分分析筛选颅脑影响机能衰退的影像特征。对比临床机能衰退评估标准,建立卷积神经网络、支持向量机、随机森林决策树智能模型,利用受试者工作特征曲线下面积(area under the curve,AUC)、精确率、净重新分类指数、F1分数、校准曲线等对比模型性能。通过准确率、特异性、敏感度等指标对各模型的临床效果进行评价。结果利用颅脑影像参数构建的临床预测模型。卷积神经网络模型预测效能最高,其训练集及测试集AUC(95%CI)分别为0.804(0.750~0.858)、0.803(0.749~0.857),训练集特异度、敏感度及准确率分别为86.52%、87.31%及87.24%,测试集特异度、敏感度及准确率分别为86.78%、86.98%及87.30%。结论基于颅脑影像参数建立的卷积神经网络模型,能有效识别并预测高原环境对人体生理机能产生的早期衰退影响,这为建立高原地区早期健康预警系统提供了依据。
