Retracted: Predicting the compressive strength of modified recycled aggregate concrete

Abstract

Reuse and recycling of construction wastes effectively prevent further destruction of the environment and nature due to the construction industry. One of the conventional methods is the reapplication of recycled aggregates (RAs) in concrete mix design and construction. Compressive strength (CS) is a considerable representation of the mechanical properties of recycled aggregate concrete (RAC). The present study is divided into two parts of experimental and predicting. In the laboratory studies phase, RA was used with 100% replacement. Due to the proven defects in RAC, glass fibers and silica fume (variables) have been used to compensate for these defects. Different mixing designs were used to construct concrete samples, and then the CS of each specimen was measured in the laboratory. Afterward, these data were used as training and testing in various conventional, ensembled and hybrid models as the tree-based learning algorithms, rules, Lazy-learning Algorithms, Functions, and Meta classifiers. Considering the applied performance statistical criteria (R 2 , root mean squared error, mean absolute error, and mean absolute percentage error), all models have provided excellent and acceptable results, which the precision of the multilayer perceptron single model and additive regression-random Forest hybrid model have the best agreement with measured values of CS of RAC.

Retraction: Jin, J, Wu, T, Zhang, Z, Fan, J, Lee, X. Application of optimization‐based estimation analysis for predicting the compressive strength of eco‐friendly modified geopolymer concrete. Structural Concrete. 2022. https://doi.org/10.1002/suco.202201051

“…Because of this, artificial intelligence (AI) approaches rather than direct laboratory-based observations have been widely employed in the literature to forecast the mechanical characteristics and behavior of various kinds of concretes and to investigate the properties of material [33][34][35][36][37] based on the experimental efforts [38,39]. Several techniques could be applied for prediction purposes in single or hybrid formats, such as support vector regression (SVR), artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS), radial basis function (RBF), so on [40][41][42]. On the field of application of machine learning for GPC, limited articles could be mentioned.…”

Geographic division of exposure environment for concrete structure based on fuzzy clustering‐rough set information entropy

“…In fact, conditional information entropy is used in subsequent calculations. Conditional information entropy represents the uncertainty of another random variable when one random variable is known Suppose that the division of conditional attribute subset C and decision attribute D on universe U is U/C = {X 1 , X 2 , …, X m } and U/D = {d 1 , d 2 , …, d n }, Then the conditional information entropy of decision attribute D about conditional attribute C is 42 :…”

Comparison of SVR models for predicting the compaction properties of lateritic soils as novel hybrid methods

“…Recent studies show that in the field of civil engineering, the use of artificial intelligence has greatly increased and is very practical [15][16][17][18][19][20]. The algorithms used in these experimental connections have been extended to include regression analysis [13,21,22].…”