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Journal ArticleDOI

Prediction model for optimized self-compacting concrete with fly ash using response surface method based on fuzzy classification

01 May 2019-Neural Computing and Applications (Springer London)-Vol. 31, Iss: 5, pp 1365-1373
TL;DR: It is revealed from the results that RSM has optimized the test procedures and trials needed for the proportioning of SCC so as to maximize the slump flow and compressive strength effectively than DOE and IREMSVM model have conformed.
Abstract: This paper elucidates a data predicting model using an intelligent rule-based enhanced multiclass support vector machine and fuzzy rules (IREMSVM-FR) while optimizing the test practices and trials needed for the proportioning of self-compacting concrete (SCC) using response surface methodology (RSM). The SCC requires a wide range of material content, and hence, more numbers of investigations were typically essential to select a suitable mixture to get the required properties of SCC. Taguchi’s methodology with an L18 array and three-level factor was used to reduce the number of the experiment. Four regulating elements, i.e., cement, fly ash, water powder ratio and superplasticizer, were used. Two results such as slump flow in the fresh state and the compressive strength in the hardened state at 28 days were assessed. Optimizations of the results were set by using RSM. The reactions of material parameters examined to optimize the fresh and hardened properties such as slump flow and compressive strength of SCC. The full quadratic equation of a model can be used to assess the influence of constituent materials on the properties of SCC. Moreover, these 28-days observation records are considered as SCC dataset. For predicting the properties of SCC, an existing intelligent classification algorithm IREMSVM-FR has been used. In which cement (kg), fly ash (kg), water powder ratio (W/P) and superplasticizer (l/m3) were taken as sources of data, whereas slump flow and compressive strength were the responses. It is revealed from the results that RSM has optimized the test procedures and trials needed for the proportioning of SCC so as to maximize the slump flow and compressive strength effectively than DOE and IREMSVM model have conformed.
Citations
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Journal ArticleDOI
TL;DR: This study includes the collection of data from the experimental work and the application of ML techniques to predict the CS of concrete containing fly ash, and shows high accuracy towards the prediction of outcome as indicated by its high coefficient correlation (R2) value.

103 citations

Journal ArticleDOI
TL;DR: In this paper, the compressive performance of fiber-reinforced concrete containing recycled PET chips being exposed to high temperatures, was investigated in this experimental effort. And the results demonstrated that the presence of PET chips replacing a percentage of sand by volume and steel fibers in the concrete mix lowered compressive strength of the specimens with and without the thermal loading.

94 citations

Journal ArticleDOI
TL;DR: It is concluded that the multi-objective optimization approaches need a further systematic study, and further studies of sustainable concrete optimization are needed by comparing the different chemical composition and particle characteristics.
Abstract: A comprehensive review of the statistical experimental optimization problem concerning the mixture design of various cement-based materials is presented herein. This review summarizes and discusses over 80 applications of optimum design regarding the basic test information under response surface method (RSM), including influence factor and corresponding response, statistical method, and coefficient of determination. The statistical experimental design reported in previous studies has shown that RSM is a sequential procedure to provide a suitable approximation for the mixture optimization. Then, linear, quadratic and interactive relationships of the statistical model can be evaluated available. Especially, the multi-objective optimization issues with multiple or competing performance requirements for various cement-based materials have also been reported, by considering fluidity, strength development, environmental impact, cost and durability. Overall, the results from existing publications have demonstrated that statistical inference and analysis of variance (ANOVA) are suitable for mix proportion design and process optimization of cement-based materials. The W/B ratio and mixture components are the prevalent factors in experimental design optimization, and then the fluidity and strength as the most popularly used response. Thus, theoretical optimum mixture proportioning can be used to predict valuable fresh and hardened properties. Finally, a critical discussion of the selection of design strategy, independent factors and their responses, and the experimental region involved in statistical experimental design, is provided. Based on this review, we conclude that the multi-objective optimization approaches need a further systematic study, and further studies of sustainable concrete optimization are needed by comparing the different chemical composition and particle characteristics.

60 citations

Journal ArticleDOI
TL;DR: In this paper, Gene Expression Programming (GEP), the decision tree (DT), and an artificial neural network (ANN) were used to predict the surface chloride concentrations, and the most accurate algorithm was then selected.
Abstract: Structures located on the coast are subjected to the long-term influence of chloride ions, which cause the corrosion of steel reinforcements in concrete elements. This corrosion severely affects the performance of the elements and may shorten the lifespan of an entire structure. Even though experimental activities in laboratories might be a solution, they may also be problematic due to time and costs. Thus, the application of individual machine learning (ML) techniques has been investigated to predict surface chloride concentrations (Cc) in marine structures. For this purpose, the values of Cc in tidal, splash, and submerged zones were collected from an extensive literature survey and incorporated into the article. Gene expression programming (GEP), the decision tree (DT), and an artificial neural network (ANN) were used to predict the surface chloride concentrations, and the most accurate algorithm was then selected. The GEP model was the most accurate when compared to ANN and DT, which was confirmed by the high accuracy level of the K-fold cross-validation and linear correlation coefficient (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) parameters. As is shown in the article, the proposed method is an effective and accurate way to predict the surface chloride concentration without the inconveniences of laboratory tests.

46 citations

Journal ArticleDOI
TL;DR: In this paper , the compressive strength of fly ash-based geopolymer concrete is estimated using decision tree, bagging regressor, and AdaBoost regressor with an R 2 value of 0.97.

39 citations

References
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Journal ArticleDOI
TL;DR: Field research was conducted in 2007-2008 and 2008-2009 in a rain-fed cold Mediterranean environment to examine the impact of the preceding crops alfalfa, maize, sunflower, and bread wheat on yield and N uptake of durum wheat varieties.
Abstract: Crop sequence is an important management practice that may affect durum wheat (Triticum durum Desf.) production. Field research was conducted in 2007-2008 and 2008-2009 seasons in a rain-fed cold Mediterranean environment to examine the impact of the preceding crops alfalfa (Medicago sativa L.), maize (Zea mays L.), sunflower (Helianthus annuus L.), and bread wheat (Triticum aestivum L.) on yield and N uptake of four durum wheat varieties. The response of grain yield of durum wheat to the preceding crop was high in 2007-2008 and was absent in the 2008-2009 season, because of the heavy rainfall that negatively impacted establishment, vegetative growth, and grain yield of durum wheat due to waterlogging. In the first season, durum wheat grain yield was highest following alfalfa, and was 33% lower following wheat. The yield increase of durum wheat following alfalfa was mainly due to an increased number of spikes per unit area and number of kernels per spike, while the yield decrease following wheat was mainly due to a reduction of spike number per unit area. Variety growth habit and performance did not affect the response to preceding crop and varieties ranked in the order Levante > Saragolla = Svevo > Normanno.

330 citations

Journal ArticleDOI
TL;DR: It is proposed that dual-energy X-ray absorptiometry should be performed at diagnosis of celiac disease in all women and in male aged >30 years, taking into account each risk factor in single patients.
Abstract: Atypical or silent celiac disease may go undiagnosed for many years and can frequently lead to loss of bone mineral density, with evolution to osteopenia or osteoporosis. The prevalence of the latter conditions, in case of new diagnosis of celiac disease, has been evaluated in many studies but, due to the variability of epidemiologic data and patient features, the results are contradictory. The aim of this study was to evaluate bone mineral density by dual-energy X-ray absorptiometry in 175 consecutive celiac patients at time of diagnosis (169 per-protocol, 23 males, 146 females; average age 38.9 years). Dual-energy X-ray absorptiometry was repeated after 1 year of gluten-free diet in those with T-score value 30 years, taking into account each risk factor in single patients.

280 citations

Journal ArticleDOI
TL;DR: A survey on intelligent techniques for feature selection and classification for intrusion detection in networks based on intelligent software agents, neural networks, genetic algorithms, neuro-genetic algorithms, fuzzy techniques, rough sets, and particle swarm intelligence is proposed.
Abstract: Rapid growth in the Internet usage and diverse military applications have led researchers to think of intelligent systems that can assist the users and applications in getting the services by delivering required quality of service in networks. Some kinds of intelligent techniques are appropriate for providing security in communication pertaining to distributed environments such as mobile computing, e-commerce, telecommunication, and network management. In this paper, a survey on intelligent techniques for feature selection and classification for intrusion detection in networks based on intelligent software agents, neural networks, genetic algorithms, neuro-genetic algorithms, fuzzy techniques, rough sets, and particle swarm intelligence has been proposed. These techniques have been useful for effectively identifying and preventing network intrusions in order to provide security to the Internet and to enhance the quality of service. In addition to the survey on existing intelligent techniques for intrusion detection systems, two new algorithms namely intelligent rule-based attribute selection algorithm for effective feature selection and intelligent rule-based enhanced multiclass support vector machine have been proposed in this paper.

170 citations

Book ChapterDOI
01 Jan 2018
TL;DR: The main focus of this work is to secure Authentication and Authorization of all the devices, Identifying and Tracking the devices deployed in the system, Locating and tracking of mobile devices, new things deployment and connection to existing system, Communication among the devices and data transfer between remote healthcare systems.
Abstract: This chapter proposes an efficient centralized secure architecture for end to end integration of IoT based healthcare system deployed in Cloud environment. The proposed platform uses Fog Computing environment to run the framework. In this chapter, health data is collected from sensors and collected sensor data are securely sent to the near edge devices. Finally, devices transfer the data to the cloud for seamless access by healthcare professionals. Security and privacy for patients’ medical data are crucial for the acceptance and ubiquitous use of IoT in healthcare. The main focus of this work is to secure Authentication and Authorization of all the devices, Identifying and Tracking the devices deployed in the system, Locating and tracking of mobile devices, new things deployment and connection to existing system, Communication among the devices and data transfer between remote healthcare systems. The proposed system uses asynchronous communication between the applications and data servers deployed in the cloud environment.

141 citations

Journal ArticleDOI
TL;DR: In this article, the authors analyzed the mix proportion parameters of high strength self compacting concrete (HSSCC) by using the Taguchi's experiment design methodology for optimal design, and the best possible levels for mix proportions were determined for maximization of ultrasonic pulse velocity (UPV), compressive strength, splitting tensile strength and for the minimization of air content, water permeability, and water absorption values.

124 citations