Real-time prediction of key monitoring physical parameters for early warning of fire-induced building collapse - Ji, Wei; Li, Guo-Qiang; Zhu, Shaojun (Computers & structures, 2022)
•Hard-to-measure building displacements in fire are predicted by machine learning.•Long short-term memory network is robust for predicting smooth time-series data.•Parameter identification is critical for early warning of fire-induced collapse.•Numerical example indicates early warning using proposed method is reliable. 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.