Abstract: The increasing frequency of brittle failure-type geological hazards presents significant challenges for early warning.Unlike plastic failure,brittle failure lacks significant plastic strain before rupture,making it difficult to identify precursors.Studies indicate that rock bridge damage is a key factor in brittle failure.Identifying sensitive indicators of rock bridge damage and integrating multi-domain sensitivity factors can support the development of an intelligent early-warning model based on precursor recognition.Currently,the early-warning technology for brittle failure-type geological hazards is constrained by limited computing power,difficulties in multi-model collaboration,and insufficient data.Advances in cloud-edge collaboration,artificial intelligence,and multi-source data fusion technologies are expected to enhance data processing efficiency.A multi-parameter synchronous acquisition and intelligent multi-model integrated monitoring system can be established to optimize brittle failure geological hazard databases,facilitate data assimilation,and enable self-adaptive updates of monitoring and warning models.This study discusses recent research progress,identifies key technological bottlenecks,and proposes strategies to support the prevention and mitigation of brittle failure-type geological hazards.