@article{PatzeltErfurtLudwig, author = {Patzelt, Max and Erfurt, Doreen and Ludwig, Horst-Michael}, title = {Quantification of cracks in concrete thin sections considering current methods of image analysis}, series = {Journal of Microscopy}, volume = {2022}, journal = {Journal of Microscopy}, number = {Volume 286, Issue 2}, doi = {10.1111/jmi.13091}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20220811-46754}, pages = {154 -- 159}, abstract = {Image analysis is used in this work to quantify cracks in concrete thin sections via modern image processing. Thin sections were impregnated with a yellow epoxy resin, to increase the contrast between voids and other phases of the concrete. By the means of different steps of pre-processing, machine learning and python scripts, cracks can be quantified in an area of up to 40 cm2. As a result, the crack area, lengths and widths were estimated automatically within a single workflow. Crack patterns caused by freeze-thaw damages were investigated. To compare the inner degradation of the investigated thin sections, the crack density was used. Cracks in the thin sections were measured manually in two different ways for validation of the automatic determined results. On the one hand, the presented work shows that the width of cracks can be determined pixelwise, thus providing the plot of a width distribution. On the other hand, the automatically measured crack length differs in comparison to the manually measured ones.}, subject = {Beton}, language = {en} } @article{ChakrabortyAnitescuZhuangetal., author = {Chakraborty, Ayan and Anitescu, Cosmin and Zhuang, Xiaoying and Rabczuk, Timon}, title = {Domain adaptation based transfer learning approach for solving PDEs on complex geometries}, series = {Engineering with Computers}, volume = {2022}, journal = {Engineering with Computers}, doi = {10.1007/s00366-022-01661-2}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20220811-46776}, pages = {1 -- 20}, abstract = {In machine learning, if the training data is independently and identically distributed as the test data then a trained model can make an accurate predictions for new samples of data. Conventional machine learning has a strong dependence on massive amounts of training data which are domain specific to understand their latent patterns. In contrast, Domain adaptation and Transfer learning methods are sub-fields within machine learning that are concerned with solving the inescapable problem of insufficient training data by relaxing the domain dependence hypothesis. In this contribution, this issue has been addressed and by making a novel combination of both the methods we develop a computationally efficient and practical algorithm to solve boundary value problems based on nonlinear partial differential equations. We adopt a meshfree analysis framework to integrate the prevailing geometric modelling techniques based on NURBS and present an enhanced deep collocation approach that also plays an important role in the accuracy of solutions. We start with a brief introduction on how these methods expand upon this framework. We observe an excellent agreement between these methods and have shown that how fine-tuning a pre-trained network to a specialized domain may lead to an outstanding performance compare to the existing ones. As proof of concept, we illustrate the performance of our proposed model on several benchmark problems.}, subject = {Maschinelles Lernen}, language = {en} } @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{LashkarAraKalantariSheikhKhozanietal., author = {Lashkar-Ara, Babak and Kalantari, Niloofar and Sheikh Khozani, Zohreh and Mosavi, Amir}, title = {Assessing Machine Learning versus a Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel}, series = {Mathematics}, volume = {2021}, journal = {Mathematics}, number = {Volume 9, Issue 6, Article 596}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/math9060596}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210504-44197}, pages = {15}, abstract = {One of the most important subjects of hydraulic engineering is the reliable estimation of the transverse distribution in the rectangular channel of bed and wall shear stresses. This study makes use of the Tsallis entropy, genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) methods to assess the shear stress distribution (SSD) in the rectangular channel. To evaluate the results of the Tsallis entropy, GP and ANFIS models, laboratory observations were used in which shear stress was measured using an optimized Preston tube. This is then used to measure the SSD in various aspect ratios in the rectangular channel. To investigate the shear stress percentage, 10 data series with a total of 112 different data for were used. The results of the sensitivity analysis show that the most influential parameter for the SSD in smooth rectangular channel is the dimensionless parameter B/H, Where the transverse coordinate is B, and the flow depth is H. With the parameters (b/B), (B/H) for the bed and (z/H), (B/H) for the wall as inputs, the modeling of the GP was better than the other one. Based on the analysis, it can be concluded that the use of GP and ANFIS algorithms is more effective in estimating shear stress in smooth rectangular channels than the Tsallis entropy-based equations.}, 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} }