1,720,969 research outputs found
Investigating the fresh and mechanical properties of wood sawdust-modified lightweight geopolymer concrete
Get PDFGeopolymer concrete has developed as a potential alternative to ordinary Portland cement-based concrete, wherein various industrial by-products have been converted as beneficial spin-offs. Apart from appropriate compressive strength in the construction sector worldwide, the durability, sound absorption, thermal conductivity, and weight of concrete are also major concerns. Lightweight geopolymer concretes have gained attention because of their superior strength, durability, lower environmental impact, and sustainable characteristics. In this view, the current study examined the feasibility of using sawdust as a natural fine and coarse aggregate substitution in fly ash (FA)-granulated blast furnace slag (GBFS) based geopolymer concrete. Four mixes with a different percentage of sawdust (25, 50, 75, and 100) substituting natural aggregate were designed to examine the effects of sawdust on fresh and hardened features of geopolymer concrete compared to those conventional FA-GBFS-based geopolymer concrete with natural aggregate. Sodium silicate (NS) and sodium hydroxide (NH) (with NS/NH ratio of 0.75) were utilized to dissolve the alumina silicate from FA and GBFS. Informational models were developed using an experimental dataset to estimate the compressive strength of geopolymer concrete mix designs. Besides, using the weight of the developed network, a global sensitivity (GS) analysis was developed to identify the sensitivity of compressive strength to the waste sawdust content. Test results confirmed that by substituting natural aggregate with 100% sawdust, there was around a 35% decrease in compressive strength. Nevertheless, the sound absorption coefficient was increased by an average of 38% in frequencies range between 1800 and 2500 HZ, and thermal conductivity decreased by around 4.5 times once the natural aggregate was substituted by 100% sawdust
Application of Component-Based Mechanical Models and Artificial Intelligence to Bolted Beam-to-Column Connections
Get PDFTop and seat beam-to-column connections are commonly designed to transfer gravitational loads of simply supported steel beams. Nevertheless, the flexural resistance characteristics of these type of connections should be properly taken into account for design, when a reliable analysis of semi-rigid steel structures is desired. In this research paper, different component-based mechanical models from Eurocode 3 (EC3) and a literature proposal (by Kong and Kim, 2017) are considered to evaluate the initial stiffness (Sj,ini) and ultimate moment capacity (Mn) of top-seat angle connections with double web angles (TSACWs). An optimized artificial neural network (ANN) model based on the artificial bee colony (ABC) algorithm is proposed in this paper to acquire an informational model from the available literature database of experimental test measurements on TSACWs. In order to evaluate the expected effect of each input parameter (such as the thickness of top flange cleat, the bolt size, etc.) on the mechanical performance and overall moment–rotation (M–θ) response of the selected connections, a sensitivity analysis is presented. The collected comparative results prove the potential of the optimized ANN approach for TSACWs, as well as its accuracy and reliability for the prediction of the characteristic (M–θ) features of similar joints. For most of the examined configurations, higher accuracy is found from the ANN estimates, compared to Eurocode 3- or Kong et al.-based formulations
Assessment of Mechanical Properties and Structural Morphology of Alkali-Activated Mortars with Industrial Waste Materials
Get PDFAlkali-activated products composed of industrial waste materials have shown promising environmentally friendly features with appropriate strength and durability. This study explores the mechanical properties and structural morphology of ternary blended alkali-activated mortars composed of industrial waste materials, including fly ash (FA), palm oil fly ash (POFA), waste ceramic powder (WCP), and granulated blast-furnace slag (GBFS). The effect on the mechanical properties of the Al2O3, SiO2, and CaO content of each binder is investigated in 42 engineered alkali-activated mixes (AAMs). The AAMs structural morphology is first explored with the aid of X-ray diffraction, scanning electron microscopy, and Fourier-transform infrared spectroscopy measurements. Furthermore, three different algorithms are used to predict the AAMs mechanical properties. Both an optimized artificial neural network (ANN) combined with a metaheuristic Krill Herd algorithm (KHA-ANN) and an ANN-combined genetic algorithm (GA-ANN) are developed and compared with a multiple linear regression (MLR) model. The structural morphology tests confirm that the high GBFS volume in AAMs results in a high volume of hydration products and significantly improves the final mechanical properties. However, increasing POFA and WCP percentage in AAMs manifests in the rise of unreacted silicate and reduces C-S-H products that negatively affect the observed mechanical properties. Meanwhile, the mechanical features in AAMs with high-volume FA are significantly dependent on the GBFS percentage in the binder mass. It is also shown that the proposed KHA-ANN model offers satisfactory results of mechanical property predictions for AAMs, with higher accuracy than the GA-ANN or MLR methods. The final weight and bias values given by the model suggest that the KHA-ANN method can be efficiently used to design AAMs with targeted mechanical features and desired amounts of waste consumption
Krill herd algorithm-based neural network in structural seismic reliability evaluation
No full textIn this research work, the relative displacement of the stories has been determined by means of a feedforward Artificial Neural Network (ANN) model, which employs one of the novel methods for the optimization of the artificial neural network weights, namely the krill herd algorithm. For the purpose of this work, the area, elasticity, and load parameters were the input parameters and the relative displacement of the stories was the output parameter. To assess the precision of the feedforward (FF) model optimized using the Krill Herd Optimization (FF-KH) algorithm, comparison of results has been performed relative to the results obtained by the linear regression model, the Genetic Algorithm (GA), and the back propagation neural network model. The comparison of results has been carried out in the training and test phases. It has been revealed that the artificial neural network optimized with the krill herd algorithm supersedes the afore-mentioned models in potential, flexibility, and precision
BAT algorithm-based ANN to predict the compressive strength of concrete - A comparative study
Get PDFThe number of effective factors and their nonlinear behaviour—mainly the nonlinear effect of the factors on concrete properties—has led researchers to employ complex models such as artificial neural networks (ANNs). The compressive strength is certainly a prominent characteristic for design and analysis of concrete structures. In this paper, 1030 concrete samples from literature are considered to model accurately and efficiently the compressive strength. To this aim, a Feed-Forward (FF) neural network is employed to model the compressive strength based on eight different factors. More in detail, the parameters of the ANN are learned using the bat algorithm (BAT). The resulting optimized model is thus validated by comparative analyses towards ANNs optimized with a genetic algorithm (GA) and Teaching-Learning-Based-Optimization (TLBO), as well as a multi-linear regression model, and four compressive strength models proposed in literature. The results indicate that the BAT-optimized ANN is more accurate in estimating the compressive strength of concrete
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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