Experimental investigation and prediction of copper slag incorporated self‐compacting concrete using artificial neural network

Dinesh, S.; Dharmar, Brindha

doi:10.1002/suco.202100230

Cited by 4 publications

(5 citation statements)

References 37 publications

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“…Most studies showed that in addition to the concentration of chloride ions, increasing the temperature also significantly reduces the chloride ions penetration resistance. [30][31][32] The use of artificial intelligence in the field of civil engineering [33][34][35][36][37][38][39][40][41][42][43][44] today more than ever is being considered. 45 The application of artificial intelligence considerably reduces the cost and time of experimental studies, provided that there is sufficient laboratory data in this area.…”

Section: Introductionmentioning

confidence: 99%

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Retracted: A comparative study on predicting the rapid chloride permeability of self‐compacting concrete using meta‐heuristic algorithm and artificial intelligence techniques

Jia

Zhao

Esmaeili‐Falak

2022

Structural Concrete

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Obtaining a trustworthy approach to forecast the chloride penetration into self-compacting concrete via rapid test may lead to frugality in cost, time, and energy to provide a durable mix design. Different single and hybrid regression methods are developed to predict the results of rapid chloride penetration tests in the present study. Cement content, fly ash, and silica fume replacement percent with cement, temperature and fine and coarse aggregates are considered as input variables. All predicted values using expanded models have a good agreement with experimentally measured results. Evaluating the accuracy and precision of single and hybrid optimized models by five statistical performance criteria (R 2 , root mean square error, mean absolute error, mean absolute percentage error, and performance index) illustrates that the hybrid support vector regression with optimization algorithm is a high-accurate promising model for predicting the results of a rapid chloride penetration test.

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Section: Introductionmentioning

confidence: 99%

“…The use of artificial intelligence in the field of civil engineering 33–44 today more than ever is being considered 45 . The application of artificial intelligence considerably reduces the cost and time of experimental studies, provided that there is sufficient laboratory data in this area 46 .…”

Section: Introductionmentioning

confidence: 99%

Retracted: A comparative study on predicting the rapid chloride permeability of self‐compacting concrete using meta‐heuristic algorithm and artificial intelligence techniques

Jia

Zhao

Esmaeili‐Falak

2022

Structural Concrete

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“…Multiple experiments are performed to identify the fresh properties of SCC. 74,75 The most commonly used fresh state tests in SCC are slump flow and V-funnel tests. During this study, it was observed that V-funnel and slump flow experiments were applied in 90-95% of the studies related to SCC in the literature.…”

Section: Methods and Model Developmentmentioning

confidence: 99%

“…Additionally, fresh and mechanical properties were also included in the inputs of ANN alongside the composition of the SCC in order to predict the amount of required WMP. Multiple experiments are performed to identify the fresh properties of SCC 74,75 . The most commonly used fresh state tests in SCC are slump flow and V‐funnel tests.…”

Section: Methods and Model Developmentmentioning

confidence: 99%

Development of an artificial neural network model to predict waste marble powder demand in eco‐efficient self‐compacting concrete

Ulas

2022

Structural Concrete

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The marble industry produces large amount of waste at almost every stage of marble processing. This waste is always uncontrollably discharged into open areas. Therefore, the consumption of Waste Marble Powder (WMP) in concrete is very important and will provide both an economic gain for concrete industries and an opportunity to achieve eco-efficient concrete production. Self-Compacting Concrete (SCC) is mostly preferred concrete type which uses the WMP as powdered material. Thus, to increase WMP usage in the production of eco-efficient SCC, it is aimed in this study to develop a model that can predict WMP demand. To develop this model, Artificial Neural Network (ANN), an artificial intelligence method, was preferred. An ANN model was developed using a comprehensive dataset that included eco-efficient SCC mixture compositions, workability measurements, and compressive strengths. The ANN model with seven inputs and one output as WMP was successfully trained and managed to produce the correct outputs to both validation and test datasets.Mix design of SCC with aimed properties can be a long process compared to conventional concrete. The proposed ANN model could reduce time loss in the production process. ANN's success is expected to facilitate the production of eco-efficient SCC with WMP.

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“…According to the thermal conductivity of the components used in concrete at elevated temperatures, deterioration and fragmentation occur. In order to overcome this situation with minimum damage, concrete mixtures with high strength and durability and low degree of exposure to heat should be developed [2,20].…”

Section: Prior Artmentioning

confidence: 99%

Artificial neural network evaluation of concrete performance exposed to elevated temperature with destructive–non-destructive tests

Demir,

Duranay,

Demirel

et al. 2024

Neural Comput & Applic

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In this study, it is aimed to predict the performance of concretes obtained by using supplementary cementitious materials (SCM) before and after high temperature using artificial neural network. Thus, in addition to contributing to sustainable development and circular economy by using waste materials in concrete production, predicting concrete strength using artificial neural network without the need for experimental studies will provide a great advantage in practice. In addition, it will also contribute to the literature in terms of determining the optimum amount of metakaolin to be used with fly ash in concrete production. Metakaolin, silica fume and fly ash were used as SCM in different proportions in concrete mixes. Accordingly, a total of 22 concrete series were prepared, one of which was the control series. Porosity, ultrasonic pulse velocity, pressure and tensile strength tests were applied to the series at the end of 7th, 28th and 90th curing periods before high temperature. In order to determine the strength losses after elevated temperature, porosity and compressive strength tests were applied at temperatures of 400, 600 and 800 °C. Mineral additive series showed positive mechanical properties up to 20%. However, it has been observed that the use of fly ash after a certain rate causes a decrease in strength. After elevated temperature, strength loss was observed in all series due to the increase in temperature, while it was observed that the rate of being affected by elevated temperature decreased as the percentage of metakaolin increased. Optimum mineral additive usage percentages were determined as 10% fly ash and 15% metakaolin. On the other hand, the use of mineral additives above the optimum level caused the performance of the concrete to decrease. Then, the concrete compression strengths obtained at 7th, 28th, and 90th days and at 400, 600 and 800 °C temperatures are taken as the outputs of the ANN. The artificial neural network provided the closest results to experimental data. Moreover, to prove the predictive performance of ANN, a comparative analysis was made with GPR, SVM and LR and the smallest value of the RMSE value is obtained with the ANN model. Finally, a fivefold cross-validation criteria was used to objectively present the performance of the model.

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Experimental investigation and prediction of copper slag incorporated self‐compacting concrete using artificial neural network

Cited by 4 publications

References 37 publications

Retracted: A comparative study on predicting the rapid chloride permeability of self‐compacting concrete using meta‐heuristic algorithm and artificial intelligence techniques

Retracted: A comparative study on predicting the rapid chloride permeability of self‐compacting concrete using meta‐heuristic algorithm and artificial intelligence techniques

Development of an artificial neural network model to predict waste marble powder demand in eco‐efficient self‐compacting concrete

Artificial neural network evaluation of concrete performance exposed to elevated temperature with destructive–non-destructive tests

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