چكيده به لاتين
Abstract In recent years, due to the occurrence of severe earthquakes and irreparable human and financial losses, seismic resilience assessment for structures and communities has received much attention. evaluative resilience assessment allows us to know to what extent critical structures of society such as hospitals, schools, bridges, etc. are resistant to earthquakes and how long it will take for them to return to their previous performance if they are damaged. In recent years, machine learning algorithms have attracted much attention in the field of risk management and assessment. The use of machine learning methods to assess the resilience index has received attention in the last few years. These methods help us improve the accuracy of the assessment and also predict the behavior of structures for future earthquakes. In this thesis, considering the great importance of hospitals during earthquakes and their service after the earthquake, we have studied a case study of a hospital located in Noor County, Mazandaran Province and examined the seismic resilience index as well as the damage to this structure. To achieve this goal, we needed to perform IDA analysis, which was performed using OpenSees software. After performing the analysis and taking the relative displacement between floors as the output of the analysis for machine training, various parameters such as earthquake magnitude, earthquake depth, soil type, structural characteristics of the structure, etc. were used as inputs. Then, using machine learning, the resilience index was evaluated based on the structural damage function.
