TY - JOUR A1 - Harirchian, Ehsan A1 - Lahmer, Tom A1 - Rasulzade, Shahla T1 - Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network JF - Energies N2 - The latest earthquakes have proven that several existing buildings, particularly in developing countries, are not secured from damages of earthquake. A variety of statistical and machine-learning approaches have been proposed to identify vulnerable buildings for the prioritization of retrofitting. The present work aims to investigate earthquake susceptibility through the combination of six building performance variables that can be used to obtain an optimal prediction of the damage state of reinforced concrete buildings using artificial neural network (ANN). In this regard, a multi-layer perceptron network is trained and optimized using a database of 484 damaged buildings from the Düzce earthquake in Turkey. The results demonstrate the feasibility and effectiveness of the selected ANN approach to classify concrete structural damage that can be used as a preliminary assessment technique to identify vulnerable buildings in disaster risk-management programs. KW - Erdbeben KW - Maschinelles Lernen KW - earthquake damage KW - seismic vulnerability KW - artificial neural network KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200504-41575 UR - https://www.mdpi.com/1996-1073/13/8/2060/htm VL - 2020 IS - Volume 13, Issue 8, 2060 PB - MDPI CY - Basel ER - TY - JOUR A1 - Harirchian, Ehsan A1 - Lahmer, Tom A1 - Buddhiraju, Sreekanth A1 - Mohammad, Kifaytullah A1 - Mosavi, Amir T1 - Earthquake Safety Assessment of Buildings through Rapid Visual Screening JF - Buildings N2 - Earthquake is among the most devastating natural disasters causing severe economical, environmental, and social destruction. Earthquake safety assessment and building hazard monitoring can highly contribute to urban sustainability through identification and insight into optimum materials and structures. While the vulnerability of structures mainly depends on the structural resistance, the safety assessment of buildings can be highly challenging. In this paper, we consider the Rapid Visual Screening (RVS) method, which is a qualitative procedure for estimating structural scores for buildings suitable for medium- to high-seismic cases. This paper presents an overview of the common RVS methods, i.e., FEMA P-154, IITK-GGSDMA, and EMPI. To examine the accuracy and validation, a practical comparison is performed between their assessment and observed damage of reinforced concrete buildings from a street survey in the Bingöl region, Turkey, after the 1 May 2003 earthquake. The results demonstrate that the application of RVS methods for preliminary damage estimation is a vital tool. Furthermore, the comparative analysis showed that FEMA P-154 creates an assessment that overestimates damage states and is not economically viable, while EMPI and IITK-GGSDMA provide more accurate and practical estimation, respectively. KW - Maschinelles Lernen KW - Machine learning KW - Erdbeben KW - buildings KW - earthquake safety assessment KW - earthquake KW - extreme events KW - seismic assessment KW - natural hazard KW - mitigation KW - rapid visual screening Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200331-41153 UR - https://www.mdpi.com/2075-5309/10/3/51 VL - 2020 IS - Volume 10, Issue 3 PB - MDPI ER - TY - JOUR A1 - Harirchian, Ehsan A1 - Lahmer, Tom T1 - Improved Rapid Visual Earthquake Hazard Safety Evaluation of Existing Buildings Using a Type-2 Fuzzy Logic Model JF - Applied Sciences N2 - Rapid Visual Screening (RVS) is a procedure that estimates structural scores for buildings and prioritizes their retrofit and upgrade requirements. Despite the speed and simplicity of RVS, many of the collected parameters are non-commensurable and include subjectivity due to visual observations. This might cause uncertainties in the evaluation, which emphasizes the use of a fuzzy-based method. This study aims to propose a novel RVS methodology based on the interval type-2 fuzzy logic system (IT2FLS) to set the priority of vulnerable building to undergo detailed assessment while covering uncertainties and minimizing their effects during evaluation. The proposed method estimates the vulnerability of a building, in terms of Damage Index, considering the number of stories, age of building, plan irregularity, vertical irregularity, building quality, and peak ground velocity, as inputs with a single output variable. Applicability of the proposed method has been investigated using a post-earthquake damage database of reinforced concrete buildings from the Bingöl and Düzce earthquakes in Turkey. KW - Fuzzy-Logik KW - Erdbeben KW - Fuzzy Logic KW - Rapid Visual Screening KW - Vulnerability assessment KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200331-41161 UR - https://www.mdpi.com/2076-3417/10/7/2375 VL - 2020 IS - Volume 10, Issue 3, 2375 PB - MDPI CY - Basel ER -