Showing papers by "Bauhaus University, Weimar published in 2020"
TL;DR: This contribution focuses in mechanical problems and analyze the energetic format of the PDE, where the energy of a mechanical system seems to be the natural loss function for a machine learning method to approach a mechanical problem.
721 citations
TL;DR: A comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models suggests machine learning as an effective tool to model the outbreak.
Abstract: Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and these models are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models need to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP; and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior across nations, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. This paper further suggests that a genuine novelty in outbreak prediction can be realized by integrating machine learning and SEIR models.
256 citations
TL;DR: In this article, a physics informed neural network (PINN) algorithm for solving brittle fracture problems is presented. But, the proposed approach is limited to two problems, and it is not suitable for other problems.
251 citations
01 Jan 2020
TL;DR: The hybrid machine learning methods of adaptive network-based fuzzy inference system and multi-layered perceptron-imperialist competitive algorithm are proposed to predict time series of infected individuals and mortality rate and predict that by late May, the outbreak and the total morality will drop substantially.
Abstract: Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to the lack of essential data and uncertainty, the epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19, and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are proposed to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for 9 days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.
172 citations
TL;DR: In this article, a nonlocal operator method is proposed which is generally applicable for solving partial differential equations (PDEs) of mechanical problems, which can be regarded as the integral form "equivalent" to the differential form in the sense of nonlocal interaction model for solving the unknown field.
130 citations
20 Mar 2020
TL;DR: A brief review of DL, RL, and deep RL methods in diverse applications in economics providing an in-depth insight into the state of the art is considered and the survey results indicate that DRL can provide better performance and higher accuracy as compared to the traditional algorithms while facing real economic problems.
Abstract: The popularity of deep reinforcement learning (DRL) applications in economics has increased exponentially. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated dynamic economics systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into the state-of-the-art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems in the presence of risk parameters and the ever-increasing uncertainties.
103 citations
TL;DR: It can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.
Abstract: This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70%) and testing (30%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.
103 citations
TL;DR: A nested grid numerical model with different Whitecapping coefficient is advanced and its efficiency with three machine learning methods of artificial neural networks, extreme learning machines and support vector regression for wave height modeling is compared.
Abstract: Estimation of wave height is essential for several coastal engineering applications. This study advances a nested grid numerical model and compare its efficiency with three machine learning (ML) me...
94 citations
TL;DR: Flash as mentioned in this paper is a sequential model-based method that sequentially explores the configuration space by reflecting on the configurations evaluated so far to determine the next best configuration to explore, which reduces the effort required to find the better configuration.
Abstract: Finding good configurations of a software system is often challenging since the number of configuration options can be large. Software engineers often make poor choices about configuration or, even worse, they usually use a sub-optimal configuration in production, which leads to inadequate performance. To assist engineers in finding the better configuration, this article introduces Flash , a sequential model-based method that sequentially explores the configuration space by reflecting on the configurations evaluated so far to determine the next best configuration to explore. Flash scales up to software systems that defeat the prior state-of-the-art model-based methods in this area. Flash runs much faster than existing methods and can solve both single-objective and multi-objective optimization problems. The central insight of this article is to use the prior knowledge of the configuration space (gained from prior runs) to choose the next promising configuration. This strategy reduces the effort (i.e., number of measurements) required to find the better configuration. We evaluate Flash using 30 scenarios based on 7 software systems to demonstrate that Flash saves effort in 100 and 80 percent of cases in single-objective and multi-objective problems respectively by up to several orders of magnitude compared to state-of-the-art techniques.
86 citations
TL;DR: In this paper, a higher order nonlocal operator method for boundary value problems is developed. But the method is not suitable for nonlocal operators and only functionals based on the non-local operators (termed as operator functional) are needed to obtain the final discrete system of equations, which significantly facilitates the implementation.
82 citations
25 Apr 2020
TL;DR: The aim is to identify the epistemic injustice disabled people experience within HCI, to question the epistemological base of knowledge production leading to said injustice and to take ownership of a narrative that all too often is created without the authors' participation.
Abstract: Technology for disabled people is often developed by non-disabled populations, producing an environment where the perspectives of disabled researchers - particularly when they clash with normative ways of approaching accessible technology - are denigrated, dismissed or treated as invalid. This epistemic violence has manifest material consequences for our lives as disabled researchers engaging with work on our own states of being. Through a series of vignettes, we illustrate our experiences and the associated pain that comes with such engagement as well as the consequences of pervasive dehumanization of ourselves through existing works. Our aim is to identify the epistemic injustice disabled people experience within HCI, to question the epistemological base of knowledge production leading to said injustice and to take ownership of a narrative that all too often is created without our participation.
Swiss Federal Institute of Aquatic Science and Technology1, Monash University2, ETH Zurich3, Monash University, Clayton campus4, Swinburne University of Technology5, Fraunhofer Society6, University of Bern7, Bauhaus University, Weimar8, University of Technology, Sydney9, University of California, Berkeley10, Delft University of Technology11, Utrecht University12
TL;DR: Future research should apply a transdisciplinary research approach through socio-technical "lighthouse" projects that apply alternative urban water systems at scale to address such pressing global challenges as climate change, eutrophication, and rapid urbanization.
Abstract: Recent developments in high- and middle-income countries have exhibited a shift from conventional urban water systems to alternative solutions that are more diverse in source separation, decentralization, and modularization. These solutions include nongrid, small-grid, and hybrid systems to address such pressing global challenges as climate change, eutrophication, and rapid urbanization. They close loops, recover valuable resources, and adapt quickly to changing boundary conditions such as population size. Moving to such alternative solutions requires both technical and social innovations to coevolve over time into integrated socio-technical urban water systems. Current implementations of alternative systems in high- and middle-income countries are promising, but they also underline the need for research questions to be addressed from technical, social, and transformative perspectives. Future research should pursue a transdisciplinary research approach to generating evidence through socio-technical "lighthouse" projects that apply alternative urban water systems at scale. Such research should leverage experiences from these projects in diverse socio-economic contexts, identify their potentials and limitations from an integrated perspective, and share their successes and failures across the urban water sector.
TL;DR: An adaptive h -refined fourth-order phase field model for studying fracture using a hybrid-staggered solution scheme devised in the framework of isogeometric analysis which provides a smooth C 1 continuous discretization throughout the domain.
22 Mar 2020
TL;DR: The inherent challenges of applying the SSQ in virtual reality research are illustrated, and suggestions on how to improve the expressiveness of SSQ results in future studies are provided.
Abstract: Originally developed for military flight simulators in the 1990s, the Simulator Sickness Questionnaire (SSQ) has been widely adopted to quantify sickness elicited by modern virtual reality systems. We illustrate the inherent challenges of applying the SSQ in virtual reality research and highlight large differences that can be found in related work. Based on our observations, we conclude by providing suggestions on how to improve the expressiveness of SSQ results in future studies and encourage researchers to consider simpler measurement methods if their research questions allow.
Karlsruhe Institute of Technology1, Universidade Federal de Santa Catarina2, German-Jordanian University3, Sultan Qaboos University4, Bandung Institute of Technology5, Bauhaus University, Weimar6, University of Southampton7, Cardiff University8, Massachusetts Institute of Technology9, Khalifa University10, University of Tehran11, Ahmadu Bello University12, Heidelberg University13, Tsinghua University14, University of Southern California15, Yonsei University16, VU University Amsterdam17, University of Adelaide18, University of Seville19, Wrocław University of Technology20, Indian Institute of Technology Madras21, Eindhoven University of Technology22, University of Cuenca23, International Institute of Information Technology, Hyderabad24, Tabriz Islamic Art University25, Universiti Malaysia Sabah26, Aalborg University27, Augsburg University of Applied Sciences28, University College London29, Chalmers University of Technology30, Coventry University31, University of Sydney32, University of Copenhagen33, Technical University of Denmark34, Malaviya National Institute of Technology, Jaipur35, University of Oregon36, National Taichung University of Science and Technology37, Shahid Bahonar University of Kerman38, University of the Bío Bío39, University of Koblenz and Landau40, Instituto Politécnico Nacional41, University of California, Berkeley42, The Chinese University of Hong Kong43, Imo State University44, Uganda Martyrs University45, Santa Catarina Federal Institute of Education, Science and Technology46, University of Moratuwa47, Kaiserslautern University of Technology48, University of Concepción49, RMIT University50, Tokyo City University51, Shahid Beheshti University52, University of Wollongong53, Xi'an University of Architecture and Technology54
TL;DR: In this article, a large international collaborative questionnaire study was conducted in 26 countries, using 21 different languages, which led to a dataset of 8225 questionnaires, and significant differences appeared between groups of participants in their perception of the scales, both in relation to distances of the anchors and relationships between scales.
TL;DR: In this article, the authors used the finite element method (FEM) to calculate the vibration response of the shell structures and the singular boundary method (SBM) with near-field and far-field Green's functions to simulate the underwater acoustic radiation excited by shell structural vibration in shallow water marine environment.
TL;DR: In this article, nonlinear transient responses of porous functionally graded plate (PFGM) in hygro-thermo-mechanical environments are studied, and two different porous distributions through the thickness are considered.
Abstract: To provide reference solutions and results for structural and material design, nonlinear transient responses of porous functionally graded plate (PFGM) in hygro-thermo-mechanical environments are studied. Two different porous distributions through the thickness are considered. The material properties such as Young's modulus, Poisson's ratio and thermal conductivity are computed by a modified power law. The hygro-thermal effects are considered as nonlinear through the thickness of the plate. The geometrically nonlinear transient behaviors are expressed by adopting the von Karman relations and solved by Newmark time integration scheme. Based on a combination between the third-order shear deformation theory (TSDT) and isogeometric analysis (IGA), discretize governing equations are approximated. These approaches achieve naturally any desired degree of continuity of basis functions, so that they are easy to fulfil the C1-continuity requirement of the plate model. The formulations take into account the transverse shear deformation and account for the material properties at elevated moisture concentrations and temperature. The effects played by the moisture concentration, temperature rise, porous volume fraction, boundary conditions and thickness-to-length ratio are performed and results illustrate interesting dynamic phenomenon for PFGM in hygro-thermo-mechanical environments. With obtained results, the nonlinear characteristics of the proposed structure with porosities are based on physical parameters.
TL;DR: A comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak suggests machine learning as an effective tool to model the outbreak.
Abstract: Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak.
21 Apr 2020
TL;DR: Authors of CHI 2018-2019 papers are surveyed, asking if they share their papers' research materials and data, how they share them, and why they do not, showing that sharing is uncommon.
Abstract: Several fields of science are experiencing a "replication crisis" that has negatively impacted their credibility. Assessing the validity of a contribution via replicability of its experimental evidence and reproducibility of its analyses requires access to relevant study materials, data, and code. Failing to share them limits the ability to scrutinize or build-upon the research, ultimately hindering scientific progress. Understanding how the diverse research artifacts in HCI impact sharing can help produce informed recommendations for individual researchers and policy-makers in HCI. Therefore, we surveyed authors of CHI 2018-2019 papers, asking if they share their papers' research materials and data, how they share them, and why they do not. The results (34% response rate) show that sharing is uncommon, partly due to misunderstandings about the purpose of sharing and reliable hosting. We conclude with recommendations for fostering open research practices. This paper and all data and materials are freely available at https://osf.io/3bu6t.
TL;DR: An adaptive h -refinement scheme to locally refine the domain along the path of the growth of the crack, using a phase field parameter, ϕ, and a residual-based posteriori error estimator are the proposed convenient measures to determine the need for refinement.
TL;DR: In this article, a literature review of research undertaken on the out-of-plane behavior of masonry infilled frames is presented, which discusses the effects of bidirectional loads, openings, slenderness, boundary conditions etc.
Abstract: This paper presents a literature review of research undertaken on the out-of-plane behaviour of masonry infilled frames. This paper also discusses the effects of bidirectional loads, openings, slenderness, boundary conditions etc. As numerous researchers have reported, these effects play a crucial role in achieving arching action cause, as they can bypass or limit its effectiveness. Namely, arching action leads to additional compressive forces which resist traversal ones. This is confirmed by inertial force methods of testing, while the same cannot be claimed for inter-storey drift or dynamical methods. It is to be acknowledged that most experimental tests were carried out using inertial force methods, mostly with the use of airbags. In contrast, only a few were undertaken with dynamical methods and just two with inter-storey drift methods. It was found that inertial force and inter-storey drift methods differ widely. In particular, inertial force methods damage the infill, leaving the frame more or less intact. Conversely, drift heavily damages the frame, while infill only slightly. Openings were investigated, albeit with contrasting results. Namely, in all cases, it was found that openings do lower the deformational but not all load-bearing capacities. Furthermore, analytical models have shown contrasting results between themselves and with experimental data. Models’ stabilities were checked with single- and multi-variable parametric analysis from which governing factors, influences of frame and other parameters were identified.
TL;DR: The committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling, and the CMIS model outperforms other models with the promising results.
Abstract: Prediction models in mobility and transportation maintenance systems have been dramatically improved by using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well as their hybrids, i.e., Levenberg–Marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic algorithms (RBF-GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of the analysis have been verified through using four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE) and standard error (SE). The CMIS model outperforms other models with the promising results of APRE = 2.3303, AAPRE = 11.6768, RMSE = 12.0056 and SD = 0.0210.
01 Jul 2020
TL;DR: It is found that conservative readers are resistant to NYTimes style, but on liberals, style even has more impact than content, and style patterns of effective argumentation are learned.
Abstract: News editorials argue about political issues in order to challenge or reinforce the stance of readers with different ideologies. Previous research has investigated such persuasive effects for argumentative content. In contrast, this paper studies how important the style of news editorials is to achieve persuasion. To this end, we first compare content- and style-oriented classifiers on editorials from the liberal NYTimes with ideology-specific effect annotations. We find that conservative readers are resistant to NYTimes style, but on liberals, style even has more impact than content. Focusing on liberals, we then cluster the leads, bodies, and endings of editorials, in order to learn about writing style patterns of effective argumentation.
18 Aug 2020
TL;DR: An overview of ML algorithms used for smart monitoring is presented, providing an overview of categories ofML algorithms for smart Monitoring that may be modified to achieve explainable artificial intelligence in civil engineering.
Abstract: Recent developments in artificial intelligence (AI), in particular machine learning (ML), have been significantly advancing smart city applications. Smart infrastructure, which is an essential component of smart cities, is equipped with wireless sensor networks that autonomously collect, analyze, and communicate structural data, referred to as “smart monitoring”. AI algorithms provide abilities to process large amounts of data and to detect patterns and features that would remain undetected using traditional approaches. Despite these capabilities, the application of AI algorithms to smart monitoring is still limited due to mistrust expressed by engineers towards the generally opaque AI inner processes. To enhance confidence in AI, the “black-box” nature of AI algorithms for smart monitoring needs to be explained to the engineers, resulting in so-called “explainable artificial intelligence” (XAI). However, when aiming at improving the explainability of AI algorithms through XAI for smart monitoring, the variety of AI algorithms requires proper categorization. Therefore, this review paper first identifies objectives of smart monitoring, serving as a basis to categorize AI algorithms or, more precisely, ML algorithms for smart monitoring. ML algorithms for smart monitoring are then reviewed and categorized. As a result, an overview of ML algorithms used for smart monitoring is presented, providing an overview of categories of ML algorithms for smart monitoring that may be modified to achieve explainable artificial intelligence in civil engineering.
TL;DR: In this article, it was shown that as early as 2.5h after water addition at approx. 30°C, small amounts of Calcium Silicate Hydrate (C-S-H) form which may contribute to a bridging of the cement particles.
TL;DR: The results of modeling the susceptibility of groundwater nitrate concentration showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas.
Abstract: Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.
TL;DR: The effect of urban form on energy consumption has been the subject of various studies around the world as mentioned in this paper, and these studies indicate that building density is positively associated with energy consumption. But, these studies do not consider the effect of building density on energy efficiency.
Abstract: The effect of urban form on energy consumption has been the subject of various studies around the world. Having examined the effect of buildings on energy consumption, these studies indicate that t...
TL;DR: A new approach is developed for applications of shape optimization on the two-dimensional time harmonic wave propagation (Helmholtz equation) in acoustic problems and the obtained results are compared against previously published numerical methods using sensitivity analysis and genetic algorithms to verify the efficiency of the proposed approaches.
Abstract: In this paper, a new approach is developed for applications of shape optimization on the two-dimensional time harmonic wave propagation (Helmholtz equation) in acoustic problems. The particle swarm optimization (PSO) algorithm - a gradient-free optimization method avoiding the sensitivity analysis - is coupled with two boundary element methods (BEM) and isogeometric analysis (IGA). The first method is the conventional isogeometric boundary element method (IGABEM). The second method is the eXtended IGABEM (XIBEM) enriched with the partition-of-unity expansion using a set of plane waves. In both methods, the computational domain is parameterized and the unknown solution is approximated using non-uniform rational B-splines basis functions (NURBS). In the optimization models, the advantage of IGA is the feature of representing the three models; i.e. shape design/analysis/optimization, using a set of control points, which also represent control variables and optimization parameters, making communication between the three models easy and straightforward. A numerical example is considered for the duct problem to validate the presented techniques against the analytical solution. Furthermore, two different applications for various frequencies are studied; the vertical noise barrier and the horn problems, and the obtained results are compared against previously published numerical methods using sensitivity analysis and genetic algorithms to verify the efficiency of the proposed approaches.
TL;DR: In this article, the effects of both partial and complete nanotube agglomerations on the effective elastic properties and wave dynamics are examined within various axial and circumferential wave numbers for different wave modes by solving an eigenvalue problem.
Abstract: Dynamics of wave propagation in carbon nanotube (CNT)-reinforced piezocomposite cylindrical shells affected by nanotube agglomeration is investigated in this study for the first time by developing an analytical approach incorporating existing theories and models. The Mori-Tanaka micromechanics model in combination of the first-order shear deformation shell theory and wave propagation solution are employed to determine wave propagation characteristics of piezocomposite cylindrical shells reinforced with agglomerated CNTs. The effects of both partial and complete nanotube agglomerations on the effective elastic properties and wave dynamics are examined within various axial and circumferential wave numbers for different wave modes by solving an eigenvalue problem. It is found that nanotube agglomeration leads to the reduction of wave phase velocity as a result of decrease in the effective elastic properties. The developed methodology in this study can be used for analysis of the data of structural health monitoring by the non-destructive testing (NDT) in estimating the degree of nanotube agglomeration in nanocomposites.
03 Apr 2020
TL;DR: This paper studies the end-to-end construction of an argumentation knowledge graph that is intended to support argument synthesis, argumentative question answering, or fake news detection, among others.
Abstract: This paper studies the end-to-end construction of an argumentation knowledge graph that is intended to support argument synthesis, argumentative question answering, or fake news detection, among others. The study is motivated by the proven effectiveness of knowledge graphs for interpretable and controllable text generation and exploratory search. Original in our work is that we propose a model of the knowledge encapsulated in arguments. Based on this model, we build a new corpus that comprises about 16k manual annotations of 4740 claims with instances of the model's elements, and we develop an end-to-end framework that automatically identifies all modeled types of instances. The results of experiments show the potential of the framework for building a web-based argumentation graph that is of high quality and large scale.