TY  - JOUR
A1  - Kumari, Vandana
A1  - Harirchian, Ehsan
A1  - Lahmer, Tom
A1  - Rasulzade, Shahla
T1  - Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings
JF  - Buildings
N2  - The seismic vulnerability assessment of existing reinforced concrete (RC) buildings is a significant source of disaster mitigation plans and rescue services. Different countries evolved various Rapid Visual Screening (RVS) techniques and methodologies to deal with the devastating consequences of earthquakes on the structural characteristics of buildings and human casualties. Artificial intelligence (AI) methods, such as machine learning (ML) algorithm-based methods, are increasingly used in various scientific and technical applications. The investigation toward using these techniques in civil engineering applications has shown encouraging results and reduced human intervention, including uncertainties and biased judgment. In this study, several known non-parametric algorithms are investigated toward RVS using a dataset employing different earthquakes. Moreover, the methodology encourages the possibility of examining the buildings’ vulnerability based on the factors related to the buildings’ importance and exposure. In addition, a web-based application built on Django is introduced. The interface is designed with the idea to ease the seismic vulnerability investigation in real-time. The concept was validated using two case studies, and the achieved results showed the proposed approach’s potential efficiency
KW  - Maschinelles Lernen
KW  - rapid assessment
KW  - Machine learning
KW  - Vulnerability assessment
KW  - OA-Publikationsfonds2022
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220509-46387
UR  - https://www.mdpi.com/2075-5309/12/5/578
VL  - 2022
IS  - Volume 12, issue 5, article 578
SP  - 1
EP  - 23
PB  - MDPI
CY  - Basel
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  -