@article{KumariHarirchianLahmeretal.,
  author    = {Kumari, Vandana and Harirchian, Ehsan and Lahmer, Tom and Rasulzade, Shahla},
  title     = {Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings},
  series = {Buildings},
  volume    = {2022},
  journal   = {Buildings},
  number    = {Volume 12, issue 5, article 578},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/buildings12050578},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20220509-46387},
  pages     = {1 -- 23},
  abstract  = {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},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{LizarazuHarirchianShaiketal.,
  author    = {Lizarazu, Jorge and Harirchian, Ehsan and Shaik, Umar Arif and Shareef, Mohammed and Antoni-Zdziobek, Annie and Lahmer, Tom},
  title     = {Application of machine learning-based algorithms to predict the stress-strain curves of additively manufactured mild steel out of its microstructural characteristics},
  series = {Results in Engineering},
  volume    = {2023},
  journal   = {Results in Engineering},
  number    = {Volume 20 (2023)},
  publisher = {Elsevier},
  address   = {Amsterdam},
  doi       = {10.1016/j.rineng.2023.101587},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20231207-65028},
  pages     = {1 -- 12},
  abstract  = {The study presents a Machine Learning (ML)-based framework designed to forecast the stress-strain relationship of arc-direct energy deposited mild steel. Based on microstructural characteristics previously extracted using microscopy and X-ray diffraction, approximately 1000 new parameter sets are generated by applying the Latin Hypercube Sampling Method (LHSM). For each parameter set, a Representative Volume Element (RVE) is synthetically created via Voronoi Tessellation. Input raw data for ML-based algorithms comprises these parameter sets or RVE-images, while output raw data includes their corresponding stress-strain relationships calculated after a Finite Element (FE) procedure. Input data undergoes preprocessing involving standardization, feature selection, and image resizing. Similarly, the stress-strain curves, initially unsuitable for training traditional ML algorithms, are preprocessed using cubic splines and occasionally Principal Component Analysis (PCA). The later part of the study focuses on employing multiple ML algorithms, utilizing two main models. The first model predicts stress-strain curves based on microstructural parameters, while the second model does so solely from RVE images. The most accurate prediction yields a Root Mean Squared Error of around 5 MPa, approximately 1\% of the yield stress. This outcome suggests that ML models offer precise and efficient methods for characterizing dual-phase steels, establishing a framework for accurate results in material analysis.},
  subject      = {Maschinelles Lernen},
  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}
}