@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{HarirchianLahmerRasulzade,
  author    = {Harirchian, Ehsan and Lahmer, Tom and Rasulzade, Shahla},
  title     = {Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network},
  series = {Energies},
  volume    = {2020},
  journal   = {Energies},
  number    = {Volume 13, Issue 8, 2060},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/en13082060},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200504-41575},
  pages     = {16},
  abstract  = {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 D{\"u}zce 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.},
  subject      = {Erdbeben},
  language  = {en}
}
@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{HarirchianLahmer,
  author    = {Harirchian, Ehsan and Lahmer, Tom},
  title     = {Improved Rapid Visual Earthquake Hazard Safety Evaluation of Existing Buildings Using a Type-2 Fuzzy Logic Model},
  series = {Applied Sciences},
  volume    = {2020},
  journal   = {Applied Sciences},
  number    = {Volume 10, Issue 3, 2375},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/app10072375},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200331-41161},
  pages     = {14},
  abstract  = {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 Bing{\"o}l and D{\"u}zce earthquakes in Turkey.},
  subject      = {Fuzzy-Logik},
  language  = {en}
}
@article{IşıkBueyueksaracLeventEkincietal.,
  author    = {I{\c{s}}{\i}k, Ercan and B{\"u}y{\"u}ksara{\c{c}}, Ayd{\i}n and Levent Ekinci, Yunus and Ayd{\i}n, Mehmet Cihan and Harirchian, Ehsan},
  title     = {The Effect of Site-Specific Design Spectrum on Earthquake-Building Parameters: A Case Study from the Marmara Region (NW Turkey)},
  series = {Applied Sciences},
  volume    = {2020},
  journal   = {Applied Sciences},
  number    = {Volume 10, issue 20, article 7247},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/app10207247},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20201022-42758},
  pages     = {23},
  abstract  = {The Marmara Region (NW Turkey) has experienced significant earthquakes (M > 7.0) to date. A destructive earthquake is also expected in the region. To determine the effect of the specific design spectrum, eleven provinces located in the region were chosen according to the Turkey Earthquake Building Code updated in 2019. Additionally, the differences between the previous and updated regulations of the country were investigated. Peak Ground Acceleration (PGA) and Peak Ground Velocity (PGV) were obtained for each province by using earthquake ground motion levels with 2\%, 10\%, 50\%, and 68\% probability of exceedance in 50-year periods. The PGA values in the region range from 0.16 to 0.7 g for earthquakes with a return period of 475 years. For each province, a sample of a reinforced-concrete building having two different numbers of stories with the same ground and structural characteristics was chosen. Static adaptive pushover analyses were performed for the sample reinforced-concrete building using each province's design spectrum. The variations in the earthquake and structural parameters were investigated according to different geographical locations. It was determined that the site-specific design spectrum significantly influences target displacements for performance-based assessments of buildings due to seismicity characteristics of the studied geographic location.},
  subject      = {Erdbeben},
  language  = {en}
}
@article{HarirchianLahmerKumarietal.,
  author    = {Harirchian, Ehsan and Lahmer, Tom and Kumari, Vandana and Jadhav, Kirti},
  title     = {Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings},
  series = {Energies},
  volume    = {2020},
  journal   = {Energies},
  number    = {volume 13, issue 13, 3340},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/en13133340},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200707-41915},
  pages     = {15},
  abstract  = {The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 D{\"u}zce Earthquake in Turkey, where the building's data consists of 22 performance modifiers that have been implemented with supervised machine learning.},
  subject      = {Erdbeben},
  language  = {en}
}
@article{BapirAbrahamczykWichtmannetal.,
  author    = {Bapir, Baban and Abrahamczyk, Lars and Wichtmann, Torsten and Prada-Sarmiento, Luis Felipe},
  title     = {Soil-structure interaction: A state-of-the-art review of modeling techniques and studies on seismic response of building structures},
  series = {Frontiers in Built Environment},
  volume    = {2023},
  journal   = {Frontiers in Built Environment},
  number    = {Volume 9},
  editor    = {Ozturk, Baki},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  doi       = {10.3389/fbuil.2023.1120351},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20230206-49190},
  pages     = {1 -- 17},
  abstract  = {The present article aims to provide an overview of the consequences of dynamic soil-structure interaction (SSI) on building structures and the available modelling techniques to resolve SSI problems. The role of SSI has been traditionally considered beneficial to the response of structures. However, contemporary studies and evidence from past earthquakes showed detrimental effects of SSI in certain conditions. An overview of the related investigations and findings is presented and discussed in this article. Additionally, the main approaches to evaluate seismic soil-structure interaction problems with the commonly used modelling techniques and computational methods are highlighted. The strength, limitations, and application cases of each model are also discussed and compared. Moreover, the role of SSI in various design codes and global guidelines is summarized. Finally, the advancements and recent findings on the SSI effects on the seismic response of buildings with different structural systems and foundation types are presented. In addition, with the aim of helping new researchers to improve previous findings, the research gaps and future research tendencies in the SSI field are pointed out.},
  subject      = {Boden-Bauwerk-Wechselwirkung},
  language  = {en}
}