708 research outputs found

    Prediction of Mortar Compressive Strengths for Different Cement Grades in the Vicinity of Sodium Chloride Using ANN

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    AbstractThe compressive strength values of cement mortar usually affect by sodium chloride quantities, chemical admixtures and cement grades so that an artificial neural network model was performed to predict the compressive strength of mortar value for different cement grades and sodium chloride (NaCl) percent. A three layer feed forward artificial neural network (ANN) model having four input neurons such as cement grades, various water to cement ratio, sodium chloride solution content, one output neuron and five hidden neurons was developed to predict of mortar each compressive strength.To this aim, twelve different mixes under three sodium chloride solution of 0%, 5% and 10% submerged after 60 days has been adopted to measure compressive strength.Artificial neural network (ANN) analysis indicated that by using ANN as non-linear statistical data modeling tool, a strong correlation between the sodium chloride percent of cement mortar and compressive strength can be established. Moreover modeling tools has great influence on the different cement grade such as 42.5 and 32.5 MPa

    Numerical Study of Concrete

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    Concrete is one of the most widely used construction material in the word today. The research in concrete follows the environment impact, economy, population and advanced technology. This special issue presents the recent numerical study for research in concrete. The research topic includes the finite element analysis, digital concrete, reinforcement technique without rebars and 3D printing

    Application of artificial neural networks in performance prediction of cement mortars with various mineral additives

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    The machine learning technique for prediction and optimization of building material performances became an essential feature in the contemporary civil engineering. The Artificial Neural Network (ANN) prognosis of mortar behavior was conducted in this study. The model appraised the design and characteristics of seventeen either building or high-temperature mortars. Seven different cement types were employed. Seventeen mineral additives of primary and secondary origin were embedded in the mortar mixtures. Cluster Analysis and Principal Component Analysis designated groups of similar mortars assigning them a specific purpose based on monitored characteristics. ANN foresaw the quality of designed mortars. The impact of implemented raw materials on the mortar quality was assessed and evaluated. ANN outputs highlighted the high suitability level of anticipation, i.e., 0.999 during the training period, which is regarded appropriate enough to correctly predict the observed outputs in a wide range of processing parameters. Due to the high predictive accuracy, ANN can replace or be used in combination with standard destructive tests thereby saving the construction industry time, resources, and capital. Good performances of altered cement mortars are positive sign for widening of economical mineral additives application in building materials and making progress towards achieved carbon neutrality by reducing its emission

    Decoding and Optimizing Magnesium Phosphate Binders for Additive Construction Applications

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    This work aims to give more insight about the physical, mechanical, thermal, and chemical performance of the MPC paste composites. Our primary outcome from this research is to improve the MPC composites with different additives, including boric acid, GnP, and acetic acid for potential utilization in 3DcP applications. Furthermore, Martian and Lunar regolith simulants can be mixed with the MPC composites to create mortars as a possible extraterrestrial 3DP in-situ construction material mainly to support the NASA habitation exploration mission

    Advances in Binders for Construction Materials

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    The global binder production for construction materials is approximately 7.5 billion tons per year, contributing ~6% to the global anthropogenic atmospheric CO2 emissions. Reducing this carbon footprint is a key aim of the construction industry, and current research focuses on developing new innovative ways to attain more sustainable binders and concrete/mortars as a real alternative to the current global demand for Portland cement.With this aim, several potential alternative binders are currently being investigated by scientists worldwide, based on calcium aluminate cement, calcium sulfoaluminate cement, alkali-activated binders, calcined clay limestone cements, nanomaterials, or supersulfated cements. This Special Issue presents contributions that address research and practical advances in i) alternative binder manufacturing processes; ii) chemical, microstructural, and structural characterization of unhydrated binders and of hydrated systems; iii) the properties and modelling of concrete and mortars; iv) applications and durability of concrete and mortars; and v) the conservation and repair of historic concrete/mortar structures using alternative binders.We believe this Special Issue will be of high interest in the binder industry and construction community, based upon the novelty and quality of the results and the real potential application of the findings to the practice and industry

    DESIGN AND OPTIMIZATION OF ALKALI-ACTIVATED BINDERS FOR CONSTRUCTION APPLICATIONS

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    This research is focused on investigating the potential use of by-product waste and natural materials for improving alternative green cement mixes. The primary objective of this research is to expand the use of sustainable and more environmentally friendly cement alternatives, by enhancing their performance for construction applications. To activate the binding properties of binder under investigation, they have to be mixed with an alkaline solution in specific quantities. To broaden the use of these binders in the construction industry, parameters such as the effect of different chemical activators types and dosages, curing times and temperatures, processing techniques, and the chemical and physical composition of the source material have to be studied. The impact of those parameters will be co-related to the binder\u27s fresh and hardened performance, mainly in terms of its mechanical and rheological properties. A bottom-up multiscale characterization scheme will be conducted to study the engineered binders\u27 physical and chemical properties. Once the results are obtained, the mix-design guidelines will be published. Advancing materials for construction applications will generate large databases targeting specific design demands. Utilizing “Design of Experiment” (DOE) and machine learning tools such as Artificial Neural Network (ANN) will speed up the optimization methods and will reduce the cost and time for introducing new construction materials. Therefore, an ANN model is developed that can predict properties of interest for different binding mixtures. The developed models can be used for tailoring mixes for general construction and 3D printing processes. The fresh and hardened properties used in the ANN models are obtained from experimental measurements. After training the models, tests are performed experimentally, and the results compared to the model outputs are used to validate the model

    Machine learning algorithms in wood ash-cement-Nano TiO2-based mortar subjected to elevated temperatures

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    Mortar is subjected to high temperatures during fire attacks or when it is near heat-radiating equipment like furnaces and reactors. The physical and microstructure of mortar were considerably altered by high temperatures. In this investigation, the effects of elevated temperatures on the flexural and compressive strengths of wood ash (WA) cement mortar modified with green-synthesised Nano titanium oxide (NT) were examined. In order to produce mortar samples, the cement was replaced with 10% WA, and 1–3% NT by weight of binder were added at constant water-binder ratio. The specimens were heated to 105, 200, 400, 600, and 800 °C with an incremental rate of 10 °C per min in the electric furnace for a sustained period of 2 h to measure their strengths. The machine learning algorithm of artificial neural networks with Levenberg-Marquardt backpropagation training techniques of different network architectures was engaged to predict the compressive strength of WA-cement-NT-based mortar produced. The findings showed that higher temperatures reduced compressive strength after 400 °C and flexural strength after 200 °C. The mortar specimen with a 3% NT addition showed the highest residual compressive strength increase, ranging from 18.75 to 27.38%. Compared to compressive strength, flexural strength is more severely affected by high temperatures. The backpropagation training algorithm revealed that each hidden layer displayed its unique strong prediction. However, Levenberg-Marquardt backpropagation training technique of 7–10-10-1 network structures yielded the best performance metrics for training, validation, and testing compared to 7-10-10-10 and 7-10-1 network architectures

    Machine learning algorithms in wood ash-cement-Nano TiO2-based mortar subjected to elevated temperatures

    Get PDF
    Mortar is subjected to high temperatures during fire attacks or when it is near heat-radiating equipment like furnaces and reactors. The physical and microstructure of mortar were considerably altered by high temperatures. In this investigation, the effects of elevated temperatures on the flexural and compressive strengths of wood ash (WA) cement mortar modified with green-synthesised Nano titanium oxide (NT) were examined. In order to produce mortar samples, the cement was replaced with 10% WA, and 1–3% NT by weight of binder were added at constant water-binder ratio. The specimens were heated to 105, 200, 400, 600, and 800 °C with an incremental rate of 10 °C per min in the electric furnace for a sustained period of 2 h to measure their strengths. The machine learning algorithm of artificial neural networks with Levenberg-Marquardt backpropagation training techniques of different network architectures was engaged to predict the compressive strength of WA-cement-NT-based mortar produced. The findings showed that higher temperatures reduced compressive strength after 400 °C and flexural strength after 200 °C. The mortar specimen with a 3% NT addition showed the highest residual compressive strength increase, ranging from 18.75 to 27.38%. Compared to compressive strength, flexural strength is more severely affected by high temperatures. The backpropagation training algorithm revealed that each hidden layer displayed its unique strong prediction. However, Levenberg-Marquardt backpropagation training technique of 7–10-10-1 network structures yielded the best performance metrics for training, validation, and testing compared to 7-10-10-10 and 7-10-1 network architectures

    Machine learning algorithms in wood ash-cement-Nano TiO2-based mortar subjected to elevated temperatures

    Get PDF
    Mortar is subjected to high temperatures during fire attacks or when it is near heat-radiating equipment like furnaces and reactors. The physical and microstructure of mortar were considerably altered by high temperatures. In this investigation, the effects of elevated temperatures on the flexural and compressive strengths of wood ash (WA) cement mortar modified with green-synthesised Nano titanium oxide (NT) were examined. In order to produce mortar samples, the cement was replaced with 10% WA, and 1–3% NT by weight of binder were added at constant water-binder ratio. The specimens were heated to 105, 200, 400, 600, and 800 °C with an incremental rate of 10 °C per min in the electric furnace for a sustained period of 2 h to measure their strengths. The machine learning algorithm of artificial neural networks with Levenberg-Marquardt backpropagation training techniques of different network architectures was engaged to predict the compressive strength of WA-cement-NT-based mortar produced. The findings showed that higher temperatures reduced compressive strength after 400 °C and flexural strength after 200 °C. The mortar specimen with a 3% NT addition showed the highest residual compressive strength increase, ranging from 18.75 to 27.38%. Compared to compressive strength, flexural strength is more severely affected by high temperatures. The backpropagation training algorithm revealed that each hidden layer displayed its unique strong prediction. However, Levenberg-Marquardt backpropagation training technique of 7–10-10-1 network structures yielded the best performance metrics for training, validation, and testing compared to 7-10-10-10 and 7-10-1 network architectures
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