2篇
您的检索式：作者名="Anthony G.Cohn"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Machine learning-based classification of rock discontinuity trace:SMOTE oversampling integrated with GBT ensemble learning显示文摘This paper presents a hybrid ensemble classifier combined synthetic minority oversampling technique(SMOTE),random search(RS)hyper-parameters optimization algorithm and gradient boosting tree(GBT)to achieve efficient and accurate rock trace identification.A thirteen-dimensional database consisting of basic,vector,and discontinuity features is established from image samples.All data points are classified as either‘‘trace”or‘‘non-trace”to divide the ultimate results into candidate trace samples.It is found that the SMOTE technology can effectively improve classification performance by recommending an optimized imbalance ratio of 1:5 to 1:4.Then,sixteen classifiers generated from four basic machine learning(ML)models are applied for performance comparison.The results reveal that the proposed RS-SMOTE-GBT classifier outperforms the other fifteen hybrid ML algorithms for both trace and nontrace classifications.Finally,discussions on feature importance,generalization ability and classification error are conducted for the proposed classifier.The experimental results indicate that more critical features affecting the trace classification are primarily from the discontinuity features.Besides,cleaning up the sedimentary pumice and reducing the area of fractured rock contribute to improving the overall classification performance.The proposed method provides a new alternative approach for the identification of 3D rock trace. | Jiayao Chen Hongwei Huang Anthony G.Cohn Dongming Zhang Mingliang Zhou | 2022 | International Journal of Mining Science and Technology2022，32，2： | 4 |
| 2 | A novel image-based approach for interactive characterization of rock fracture spacing in a tunnel face显示文摘This paper presents a novel integrated method for interactive characterization of fracture spacing in rock tunnel sections.The main procedure includes four steps:(1)Automatic extraction of fracture traces,(2)digitization of trace maps,(3)disconnection and grouping of traces,and(4)interactive measurement of fracture set spacing,total spacing,and surface rock quality designation(S-RQD)value.To evaluate the performance of the proposed method,sample images were obtained by employing a photogrammetrybased scheme in tunnel faces.Experiments were then conducted to determine the optimal parameter values(i.e.distance threshold,angle threshold,and number of fracture trace grouping)for characterizing rock fracture spacing.By applying the identified optimal parameters involved in the model,the proposed method could lead to excellent qualitative results to a new tunnel face.To perform a quantitative analysis,three methods(i.e.field,straightening,and the proposed method)were employed in the same study and comparisons were made.The proposed method agrees well with the field measurement in terms of the maximum and average values of measured spacing distribution.Overall,the proposed method has reasonably good accuracy and interactive advantage for estimating the ultimate fracture spacing and S-RQD.It can be a possible extension of existing methods for fracture spacing characterization for two-dimensional(2D)rock tunnel faces. | Jiayao Chen Yifeng Chen Anthony G.Cohn Hongwei Huang Jianhong Man Lijun Wei | 2022 | Journal of Rock Mechanics and Geotechnical Engineering2022，14，4： | 1 |
您所在的机构暂未开通此部分的功能，请联系管理员