Authors: Wei Ji , Guo-Qiang Li , Shaojun Zhu
Abstract:This paper proposes a real-time prediction method for key monitoring physical parameters (KMPPs) for early warning of fire-induced building collapse using machine learning. Since the actual load distribution and structural material properties of the burning building are usually unknown and uncertain, easy-to-measure parameters of the burning building, including easy-to-measure KMPPs (displacements and displacement velocities) of key joints, and temperatures of key structural members of the building, are incorporated as the input to predict the hard-to-measure KMPPs. The long short-term memory network is adopted in the machine learning framework. The network can be trained offline during the design stage through simulated data and used online with real-time measured data in fire. A portal frame building is numerically examined, and the results indicate that the trained agent can identify unknown and uncertain parameters and predict the hard-to-measure KMPPs with high accuracy and efficiency, enhancing the accuracy and reliability of early warning for fire-induced building collapse.