Research on fire-induced collapse emergency monitoring technology for building structures
Building fires have emerged as one of the primary disasters threatening public safety in urban areas.This research establishes an emergency monitoring method for the collapse of building structures during fires by integrating multiple technologies,including infrared thermal imaging,measurement robots,and close-range photogrammetry.The goal is to enhance the safety of civilians and fire rescue personnel.The method was employed to monitor the temperature fields and deformation patterns of buildings during fires,supporting risk analysis of potential collapses and aiding decision-making for on-site fire rescue operations.Specifically,infrared thermal imaging was used to collect temperature field data from the building fires,while measurement robots and close-range photogrammetry were utilized to obtain high-precision deformation data at critical points and multidirectional stereo deformation data of the structures under fire.A calibration model known as RIME-ELM was developed,integrating the Rime Optimization Algorithm(RIME)with the Extreme Learning Machine(ELM).This model optimized the input weight matrix and the bias matrix of the hidden layers,which are initially generated randomly by ELM,through the application of RIME.Based on the model,high-precision key point monitoring data was utilized to refine the monitoring results obtained from close-range photogrammetry,thereby enhancing the accuracy of the stereo deformation data.A solid model of the building structure was constructed to simulate fire emergency monitoring and to validate the effectiveness of this method.The results indicated that both the temperature data and the deformation data obtained through this method aligned with the general principles governing building fires.When compared to the ELM model,the correction results achieved using the RIME-ELM model demonstrated a significant reduction in the mean relative error.It was confirmed that the model is both effective and feasible,providing comprehensive and reliable data support for emergency rescue operations during building fires.
万方数据
维普
