FE Modelling and Analysis of Beam Column Joint Using Reactive Powder Concrete

Nafees, Afnan; Javed, Muhammad Faisal; Musarat, Muhammad Ali; Ali, Mujahid; Aslam, Fahid; Vatin, Nikolai

doi:10.3390/cryst11111372

Cited by 16 publications

(11 citation statements)

References 35 publications

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“…The re-placement of cement with SF between 5% and 25% with water to binder ratio in the range of 0.26–0.42 increases the compressive strength by about 6–30% [ 15 , 30 ]. The compressive strength of silica fume concrete (SFC) is significantly increased by varying water–cement ratios between 10% and 20% [ 31 , 32 ]. By increasing the water–cement ratio of SFC, a decrease in the concrete’s overall strength was observed.…”

Section: Introductionmentioning

confidence: 99%

“…SFC mixtures must also be optimized in terms of cost to achieve desirable properties by effectively proportioning SFC components. Traditionally, test lots are prepared in the laboratory to fulfill these criteria and meet construction specifications [ 32 , 33 ]. As only a limited number of tests can be produced in a laboratory, experimental methods can generate well, rather than best-performing proportions of SFC mixtures.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques

et al. 2021

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Silica fume (SF) is a frequently used mineral admixture in producing sustainable concrete in the construction sector. Incorporating SF as a partial substitution of cement in concrete has obvious advantages, including reduced CO2 emission, cost-effective concrete, enhanced durability, and mechanical properties. Due to ever-increasing environmental concerns, the development of predictive machine learning (ML) models requires time. Therefore, the present study focuses on developing modeling techniques in predicting the compressive strength of silica fume concrete. The employed techniques include decision tree (DT) and support vector machine (SVM). An extensive and reliable database of 283 compressive strengths was established from the available literature information. The six most influential factors, i.e., cement, fine aggregate, coarse aggregate, water, superplasticizer, and silica fume, were considered as significant input parameters. The evaluation of models was performed by different statistical parameters, such as mean absolute error (MAE), root mean squared error (RMSE), root mean squared log error (RMSLE), and coefficient of determination (R2). Individual and ensemble models of DT and SVM showed satisfactory results with high prediction accuracy. Statistical analyses indicated that DT models bested SVM for predicting compressive strength. Ensemble modeling showed an enhancement of 11 percent and 1.5 percent for DT and SVM compressive strength models, respectively, as depicted by statistical parameters. Moreover, sensitivity analyses showed that cement and water are the governing parameters in developing compressive strength. A cross-validation technique was used to avoid overfitting issues and confirm the generalized modeling output. ML algorithms are used to predict SFC compressive strength to promote the use of green concrete.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques

et al. 2021

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“…The challenges for adopting IR 4.0 technologies in developing countries are separate from those of established countries [40,41]. The Saudi government has launched "Vision 2030" to digitally transform existing industrial manufacturing enterprises to cope with the fast-paced modern industrial era [42].…”

Section: Significance Of Current Researchmentioning

confidence: 99%

Real-Time Implementation of a Fully Automated Industrial System Based on IR 4.0 Concept

Salah¹

2021

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With the advent of modern communication and control strategies, existing industrial enterprises are now being transformed as per Industrial Revolution (IR) 4.0 standards to maximize production rates and monetary gains. To cope with the pace of the modern technological revolution, the Government of Saudi Arabia has launched “Vision 2030”. This research article presents the full automation process of an existing production line at the College of Engineering, King Saud University, as per “Vision 2030” guidelines. Initially, a production line was designed to produce flavored yogurt bottles from a user-defined flavor and plain yogurt mixture. The research project was completed in two phases. During phase I, smart sensing, control, and automation equipment were used to minimize human intervention, the so-called semi-automated mode of operation. A bottle-feeding mechanism and robotic arms were later integrated to eliminate human intervention during the second phase. Moreover, during phase II, Node-RED, Telegram Bots, and a Raspberry Pi 4 controller were used to achieve IoT-based monitoring and control as per Industry 4.0 requirements. A comparative performance analysis was conducted between semi-automated and fully automated modes of operation to demonstrate the benefits of the fully automated operational mode. The performance of the fully automated system was found to be superior in comparison with the semi-automated system.

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“…The fundamental component of any manufacturing or production enterprise is the waste management [ 26 , 27 , 28 , 29 , 30 ]. If the waste generated from the manufacturing process is not disposed of properly, it will be a major reason for serious health and environmental hazards [ 31 , 32 , 33 , 34 , 35 ]. There has been growing interest in adding together traditional and non-traditional additives for soil stabilization purposes in recent years.…”

Section: Introductionmentioning

confidence: 99%

Compaction Characteristics and Permeability of Expansive Shale Stabilized with Locally Produced Waste Materials

et al. 2022

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Waste is available in an abundant form and goes to landfill without any use, creating threats to the environment. Recent and past studies have used different types of waste to stabilize soil and reduce environmental impacts. However, there is a lack of studies on the combined use of marble dust, rice-husk ash, and saw dust in expansive shale soil. The current study tries to overcome such a gap in the literature, studying the effect of marble dust, rice-husk ash, and saw dust on expansive shale’s compaction characteristics and permeability properties. According to unified soil classification and the AAHTO classification system, the geotechnical properties of natural soil are classified as clay of high plasticity (CH) and A-7-5. Several tests are performed in the laboratory to investigate the compaction characteristics and permeability properties of expansive shale. Moreover, permeability apparatus is used to investigate the permeability properties of soil. In addition, due to the accuracy of the apparatus, the conventional apparatus has been partly modified. The experimental results show that the addition of waste to the soil has significantly improved soil stabilization, increasing permeability and decreasing plasticity indexes. In addition, there is a gradual decrease in the dry density of soil and an increase in the permeability of stabilized soil. Based on the outcomes of the current study, it claims and concludes that these waste materials can be used as soil stabilizers or modifiers, instead of being dumped in landfill, which will provide a green, friendly, and sustainable environment. The current study recommends that future researchers use various wastes in the concrete and soil to improve their compaction and mechanical properties.

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FE Modelling and Analysis of Beam Column Joint Using Reactive Powder Concrete

Cited by 16 publications

References 35 publications

Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques

Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques

Real-Time Implementation of a Fully Automated Industrial System Based on IR 4.0 Concept

Compaction Characteristics and Permeability of Expansive Shale Stabilized with Locally Produced Waste Materials

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