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    Fast Automatic Segmentation of Microseismic and Blasting Events without Labeled Data Using Sparse Graph Spectral Clustering 

    Source: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2026:;Volume ( 012 ):;issue: 003:;page 04026022-1
    Author(s): Sharma, Atma; Zhang, Jie; Wu, Shuangyi; Lu, Meng; Huang, Zhong-Kai
    Publisher: American Society of Civil Engineers
    Abstract: AbstractDifferentiating between microseismic and blasting events is a classical task in underground excavation monitoring. Most of the machine learning methods utilized for this task are supervised and require labeled data. ...
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    Numerical Simulation Study on Cumulative Damage of Concrete under Repeated Penetration 

    Source: Journal of Materials in Civil Engineering:;2025:;Volume ( 037 ):;issue: 010:;page 04025339-1
    Author(s): Li, LuMeng; Li, ShuTao; Chen, YeQing; Liu, QingQing; Zhang, Huan
    Publisher: American Society of Civil Engineers
    Abstract: AbstractThe development of precision-guided weapons and their adaptation to modern warfare have shifted operational strategies toward multiple precision strikes. Given the high cost and risk of repetitive penetration ...
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    Fast Semiautomatic Change Point Detection in Deformation Monitoring Using Kullback–Leibler Divergence 

    Source: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2025:;Volume ( 011 ):;issue: 004:;page 04025057-1
    Author(s): Sharma, Atma; Zhang, Jie; Spagnoli, Giovanni; Huang, Zhongkai; Lu, Meng; Yao, Hongzeng; Wu, Shuangyi
    Publisher: American Society of Civil Engineers
    Abstract: AbstractAutomated change point detection in deformation monitoring data is desirable for engineering applications (e.g., operation, maintenance, and early warning). This study proposes a novel change point detection ...
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