Affiliations: [a] Department of Construction Engineering and Management, Faculty of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| [b] Department of Construction Engineering and Management, Faculty of Civil Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
| [c] Department of Civil Engineer, Monash University, Australia, Melborne
Correspondence:
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Corresponding author. Abbas Akbarpour Nikghalb Rashti, Department of construction engineering and management, faculty of civil engineering, south Tehran branch, Islamic azad university, 1777613651 Tehran, Iran. Tel.: +98 912 103 5529; E-mail: A_akbarpour@azad.ac.ir.
Abstract: Prediction of structural damage prior to earthquake occurrence provides an early warning for stakeholders of building such as owners and urban managers and can lead to necessary decisions for retrofitting of structures before a disaster occurs, legislating urban provisions of execution of building particularly in earthquake prone areas and also management of critical situations and managing of relief and rescue. For proper prediction, an effective model should be produced according to field data that can predict damage degree of local buildings. In this paper in accordance with field data and Fuzzy logic, damage degree of building is evaluated. Effective parameters of this model as an input data of model consist of height and age of the building, shear wave velocity of soil, plan equivalent moment of inertia, fault distance, earthquake acceleration, the number of residents, the width of the street for 527 buildings in the city. The output parameter of the model, which was the damage degree of the buildings, was also classified as five groups of no damage, slight damage, moderate damage, extensive damage, and complete damage. The ranges of input and output classification were obtained based on the supervised center classification (SCC-FCM) method in accordance with field data.
Keywords: Earthquake, damage degree, fuzzy model, membership function, rules of
fuzzy model
