@article{HarirchianLahmerBuddhirajuetal.,
  author    = {Harirchian, Ehsan and Lahmer, Tom and Buddhiraju, Sreekanth and Mohammad, Kifaytullah and Mosavi, Amir},
  title     = {Earthquake Safety Assessment of Buildings through Rapid Visual Screening},
  series = {Buildings},
  volume    = {2020},
  journal   = {Buildings},
  number    = {Volume 10, Issue 3},
  publisher = {MDPI},
  doi       = {10.3390/buildings10030051},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200331-41153},
  pages     = {15},
  abstract  = {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 Bing{\"o}l 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.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{HarirchianKumariJadhavetal.,
  author    = {Harirchian, Ehsan and Kumari, Vandana and Jadhav, Kirti and Raj Das, Rohan and Rasulzade, Shahla and Lahmer, Tom},
  title     = {A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings},
  series = {Applied Sciences},
  volume    = {2020},
  journal   = {Applied Sciences},
  number    = {Volume 10, issue 20, article 7153},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/app10207153},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20201022-42744},
  pages     = {18},
  abstract  = {Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure's performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building's behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings.},
  subject      = {Erdbeben},
  language  = {en}
}
@article{HarirchianJadhavMohammadetal.,
  author    = {Harirchian, Ehsan and Jadhav, Kirti and Mohammad, Kifaytullah and Aghakouchaki Hosseini, Seyed Ehsan and Lahmer, Tom},
  title     = {A Comparative Study of MCDM Methods Integrated with Rapid Visual Seismic Vulnerability Assessment of Existing RC Structures},
  series = {Applied Sciences},
  volume    = {2020},
  journal   = {Applied Sciences},
  number    = {Volume 10, issue 18, article 6411},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/app10186411},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200918-42360},
  pages     = {24},
  abstract  = {Recently, the demand for residence and usage of urban infrastructure has been increased, thereby resulting in the elevation of risk levels of human lives over natural calamities. The occupancy demand has rapidly increased the construction rate, whereas the inadequate design of structures prone to more vulnerability. Buildings constructed before the development of seismic codes have an additional susceptibility to earthquake vibrations. The structural collapse causes an economic loss as well as setbacks for human lives. An application of different theoretical methods to analyze the structural behavior is expensive and time-consuming. Therefore, introducing a rapid vulnerability assessment method to check structural performances is necessary for future developments. The process, as mentioned earlier, is known as Rapid Visual Screening (RVS). This technique has been generated to identify, inventory, and screen structures that are potentially hazardous. Sometimes, poor construction quality does not provide some of the required parameters; in this case, the RVS process turns into a tedious scenario. Hence, to tackle such a situation, multiple-criteria decision-making (MCDM) methods for the seismic vulnerability assessment opens a new gateway. The different parameters required by RVS can be taken in MCDM. MCDM evaluates multiple conflicting criteria in decision making in several fields. This paper has aimed to bridge the gap between RVS and MCDM. Furthermore, to define the correlation between these techniques, implementation of the methodologies from Indian, Turkish, and Federal Emergency Management Agency (FEMA) codes has been done. The effects of seismic vulnerability of structures have been observed and compared.},
  subject      = {Erdbebensicherheit},
  language  = {en}
}