CONSTRUCTION OF GRANULAR COMPUTING MODEL BASED ON GENERAL SIMILARITY RELATION IN SEISMIC VULNERABILITY ASSESSMENT

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KHAMES PANAH FATEMEH; DELAVAR MAHMOUD REZA; SAMADI ALINIYA HADIS; ZARE MEHDI

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Journal: JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY Year:2015 | Volume:4 | Issue:3 Page(s): 145-156

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Title

CONSTRUCTION OF GRANULAR COMPUTING MODEL BASED ON GENERAL SIMILARITY RELATION IN SEISMIC VULNERABILITY ASSESSMENT

Author(s)

KHAMES PANAH FATEMEH | DELAVAR MAHMOUD REZA | SAMADI ALINIYA HADIS | ZARE MEHDI | Issue Writer Certificate

Keywords

GRANULAR COMPUTINGQ2

UNCERTAINTYQ1

SEISMIC VULNERABILITY ASSESSMENTQ2

GRANULAR TREEQ2

Abstract

Tehran, capital of Iran, is located on few known (Mosha, North Tehran Fault and South and North Ray) and unknown faults which expose this mega city to huge earthquakes’ effects. In addition to considerable seismic hazard in Tehran the existence of old and non-standard buildings make the repercussions even worse. Determining locations and intensity of seismic vulnerability of a city is considered as a complicated disaster management problem. As, this problem generally depends on various criteria and expert’s opinions, one of the most important challenges concerned is the existence of UNCERTAINTY regarding inconsistency in expert’s view. UNCERTAINTY in seismic vulnerability map would results biases in risk management which has multilateral effects on decision makings. Some multi-criteria evaluation methods have recently been proposed to handle some aspects of uncertainties in the process of producing the seismic vulnerability map for Tehran. GRANULAR COMPUTING approach is proposed in this paper to overcome the limitation of the abovementioned existing algorithms. It can be regarded for learning classification rules by considering the two basic issues: concept formation (making granules) and concept relationships identification (relationship between granules). One of the most important features of this method with respect to previous studies is inference of more compatible rules having zero inconsistency extracted from existing training databases. Furthermore, in this approach, non-redundant covering rules will be extracted for consistent classification where one object maybe classified with two or more non-redundant rules. In this study the result of north Tehran fault hazard analysis is applied to the vulnerability assessment process and activation of other faults have been ignored. It is assumed that the northern fault of Tehran is activated and then the classification rules of seismic physical vulnerability are inducted from GRANULAR COMPUTING tree. A pilot area of Tehran Metropolitan Area located in the north of Iran was selected for the purpose of this study.

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APA: Copy

KHAMES PANAH, FATEMEH, DELAVAR, MAHMOUD REZA, SAMADI ALINIYA, HADIS, & ZARE, MEHDI. (2015). CONSTRUCTION OF GRANULAR COMPUTING MODEL BASED ON GENERAL SIMILARITY RELATION IN SEISMIC VULNERABILITY ASSESSMENT. JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY, 4(3), 145-156. SID. https://sid.ir/paper/249356/en

Vancouver: Copy

KHAMES PANAH FATEMEH, DELAVAR MAHMOUD REZA, SAMADI ALINIYA HADIS, ZARE MEHDI. CONSTRUCTION OF GRANULAR COMPUTING MODEL BASED ON GENERAL SIMILARITY RELATION IN SEISMIC VULNERABILITY ASSESSMENT. JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY[Internet]. 2015;4(3):145-156. Available from: https://sid.ir/paper/249356/en

IEEE: Copy

FATEMEH KHAMES PANAH, MAHMOUD REZA DELAVAR, HADIS SAMADI ALINIYA, and MEHDI ZARE, “CONSTRUCTION OF GRANULAR COMPUTING MODEL BASED ON GENERAL SIMILARITY RELATION IN SEISMIC VULNERABILITY ASSESSMENT,” JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY, vol. 4, no. 3, pp. 145–156, 2015, [Online]. Available: https://sid.ir/paper/249356/en

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