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

Abstract

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.

Retracted: Predicting the compressive strength of modified recycled aggregate concrete

“…17 In addition to predicting the behavior and physical and mechanical properties of RAC, optimization of parametric studies has recently been considered in some studies . 14,15,25,26,28,37,41,46,61,64 Due to the importance of determining the compressive strength (CS) of RAC as its most important mechanical properties, evaluating different prediction techniques is still continuing on the various mix designs and admixtures. 7,21,30,36,44,[57][58][59][60]63,65 As shown above, the major increase in new approaches of prediction of CS of RAC took place in the most recent year.…”

“…Predicting methods are used for predicting the mechanical properties, constitutive modeling, and the behavior of the materials more than ever 17 . In addition to predicting the behavior and physical and mechanical properties of RAC, optimization of parametric studies has recently been considered in some studies 14,15,25,26,28,37,41,46,61,64 …”

Prediction of Water Resistance of Magnesium Oxychloride Cement Concrete Based upon Hybrid-BP Neural Network

“…With the continuous development of science and technology, artificial intelligence technology [11,12] has gradually begun to be used widely in civil engineering, for example, to predict concrete's compressive strength based upon the composition of concrete materials, mixing temperature, and mixing time. Prediction of the shear capacity of RC beams strengthened with inorganic composites [13] and to predictthe compressive and flexural strengths of eco-friendly concrete containing recycled concrete aggregate [14].…”

Applying Biogeography-Based Multi-Layer Perceptron Neural Network to Predict California Bearing Capacity Value of Stabilized Pond Ash With Lime and Lime Sludge

“…ANNs are superior to most traditional procedures [21] due to their modeling capability and capacity to learn from experience. Recently, ANN has been successfully implemented in virtually every field of geotechnical engineering, including the compressive strength and Young's modulus of frozen sand [21][22][23][24][25][26][27][28][29][30][31].…”