The application of advanced inventory techniques in urban inventory data development to earthquake risk modeling and mitigation in mid-America

Structure type classification;GIS;Geographic information systems;Building assembly valuation;Shape recognition;ANN;Artificial neural networks;Building inventory;Earthquake risk assessment modeling

Muthukumar, Subrahmanyam ; City Planning

University:Georgia Institute of Technology

Department:City Planning

关键词: Structure type classification;    GIS;    Geographic information systems;    Building assembly valuation;    Shape recognition;    ANN;    Artificial neural networks;    Building inventory;    Earthquake risk assessment modeling;   

Others  :  https://smartech.gatech.edu/bitstream/1853/26662/1/muthukumar_subrahmanyam_200812_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

The process of modeling earthquake hazard risk and vulnerability is a prime component of mitigation planning, but is rife with epistemic, aleatory and factual uncertainty.Reducing uncertainty in such models yields significant benefits, both in terms of extending knowledge and increasing the efficiency and effectiveness of mitigation planning.An accurate description of the built environment as an input into loss estimation would reduce factual uncertainty in the modeling process.Building attributes for earthquake loss estimation and risk assessment modeling were identified.Three modules for developing the building attributes were proposed, including structure classification, building footprint recognition and building valuation.Data from primary sources and field surveys were collected from Shelby County, Tennessee, for calibration and validation of the structure type models and for estimation of various components of building value.Building footprint libraries were generated for implementation of algorithms to programmatically recognize two-dimensional building configurations.The modules were implemented to produce a building inventory for Shelby County, Tennessee that may be used effectively in loss estimation modeling.Validation of the building inventory demonstrates effectively that advanced technologies and methods may be effectively and innovatively applied on combinations of primary and derived data and replicated in order to produce a bottom-up, reliable, accurate and cost-effective building inventory.