چكيده به لاتين
Considering the important role that buildings, roads, bridges, tunnels, dams, etc. play in the economy and human life, damage to any of these structures It can lead to personal and financial damage. Therefore, the ability to detect damage and quickly assess the amount of damage and, if necessary, to make decisions and implement measures for the reconstruction of structures or their maintenance is important and necessary. In the science of civil engineering, the phenomenon of earthquake is an important issue and monitoring the health of the structure after the occurrence of this phenomenon is very important. Visual inspection is one of the first and most basic methods that can be used to assess damages and measure the health of structures. Visual inspection requires significant financial resources as well as sending expert teams to check each structure. In recent years, due to the reasonable cost and sufficient accuracy of measurement sensors such as displacement gauges, accelerometers and strain gauges, the use of these sensors in the process of monitoring the health of structures has become very common. Also, with significant developments in computer science, artificial intelligence has become one of the most successful branches in the field of data science. These successes have provided a unique opportunity for better predictions in the field of structural engineering and earthquakes.
In this thesis, after examining the concepts of structural health monitoring and various methods of doing it, the concepts of machine learning and deep learning, as the most widely used approaches and a sub-branch of artificial intelligence, will be examined. Finally, a new method based on deep learning by combining two well-known deep neural networks will be introduced to monitor the health of civil structures. The proposed method uses the combination of two convolutional and recurrent deep neural networks as well as the raw data obtained from several sensors installed on the structure to detect damage in the structure. This method was applied to the numerical model of the University of Florida bridge, the laboratory model of Qatar University and the concrete bridge of Tianjin Yang in real scale and its efficiency was measured. The obtained results show that this method is able to classify the healthy and unhealthy state of the structure with good accuracy.
