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HAZUS Earthquake Loss Estimation Methods Charles A. Kircher1; Robert V. Whitman2; and William T. Holmes3 Abstract: This paper provides background and historical perspective on the development of the earthquake loss estimation methods of the HAZUS technology referred to as HAZUS Earthquake, describes the various modules of HAZUS Earthquake, and summarizes the key concepts of these methods. HAZUS Earthquake modules calculate seismic hazard, evaluate the likelihood of various states of damage to buildings, lifelines, and other components of the built environment, and estimate both direct and indirect losses resulting from this damage. The focus of this paper is on building-related methods. The paper also describes two recent improvements to the HAZUS Earthquake technology: 1 the Advanced Engineering Building Module AEBM and 2 use of ShakeMap ground motion data. The AEBM allows expert users to develop “building-specific” damage and loss functions. ShakeMap ground motion data HAZUS may be used to rapidly assess potential damage and loss immediately following an earthquake. The paper closes with a comparison of damage and loss estimated using ShakeMap data with actual damage and loss due to the 1994 Northridge earthquake. DOI: 10.1061/ASCE1527-698820067:245 CE Database subject headings: Earthquakes; Seismic effects; Damage; Estimation; Models. منبع دانلود از پیوست kircher2006.pdf

Extreme learning machine for prediction of heat load in district heating systems Shahin Sajjadia, Shahaboddin Shamshirbandb, , , Meysam Alizamirc, Por Lip Yeeb, Zulkefli Mansord, Azizah Abdul Manafe, Torki A. Altameeme, , Ali Mostafaeipourf aDepartment of Construction Management, University of Houston, Houston, TX, USA bDepartment of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia cYoung Researchers and Elite Club, Hamedan Branch, Islamic Azad University, Hamedan, Iran dResearch Center for software technology and management (SOFTAM), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Malaysia eAdvanced Informatics School, Universiti Teknologi Malaysia, Malaysia fIndustrial Engineering Department, Yazd University, Yazd, Iran Received 25 January 2016, Revised 23 March 2016, Accepted 9 April 2016, Available online 12 April 2016 Energy and Buildings Volume 122, 15 June 2016, Pages 222–227 Highlights • District heating systems for increase in fuel efficiency. • Control and prediction future improvement of district heating systems operation. • To predict the heat load for individual consumers in district heating systems. • A process which simulates the head load conditions. • Soft computing methodologies. Abstract District heating systems are important utility systems. If these systems are properly managed, they can ensure economic and environmental friendly provision of heat to connected customers. Potentials for further improvement of district heating systems’ operation lie in improvement of present control strategies. One of the options is introduction of model predictive control. Multistep ahead predictive models of consumers’ heat load are starting point for creating successful model predictive strategy. In this article, short-term, multistep ahead predictive models of heat load of consumer attached to district heating system were created. Models were developed using the novel method based on Extreme Learning Machine (ELM). Nine different ELM predictive models, for time horizon from 1 to 24 h ahead, were developed. Estimation and prediction results of ELM models were compared with genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ELM approach in comparison with GP and ANN. Moreover, achieved results indicate that developed ELM models can be used with confidence for further work on formulating novel model predictive strategy in district heating systems. The experimental results show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms. Keywords District heating systems; Heat load; Estimation; Prediction; Extreme Learning Machine (ELM) 1-s2.0-S0378778816302766-main.pdf