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The composition of self-compacting concrete (SCC) contains 60–70% coarse and fine aggregates, which are replaced by construction waste, such as recycled aggregates (RA). However, the complexity of its structure requires a time-consuming mixed design. Currently, many researchers are studying the prediction of concrete properties using soft computing techniques, which will eventually reduce environmental degradation and other material waste. There have been very limited and contradicting studies regarding prediction using different ANN algorithms. This paper aimed to predict the 28-day splitting tensile strength of SCC with RA using the artificial neural network technique by comparing the following algorithms: Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB). There have been very limited and contradicting studies regarding prediction by using and comparing different ANN algorithms, so a total of 381 samples were collected from various published journals. The input variables were cement, admixture, water, fine and coarse aggregates, and superplasticizer; the data were randomly divided into three sets—training (60%), validation (10%), and testing (30%)—with 10 neurons in the hidden layer. The models were evaluated by the mean squared error (MSE) and correlation coefficient (R). The results indicated that all three models have optimal accuracy; still, BR gave the best performance (R = 0.91 and MSE = 0.2087) compared with LM and SCG. BR was the best model for predicting TS at 28 days for SCC with RA. The sensitivity analysis indicated that cement (30.07%) was the variable that contributed the most to the prediction of TS at 28 days for SCC with RA, and water (2.39%) contributed the least. [ABSTRACT FROM AUTHOR]

Several types of research currently use machine learning (ML) methods to estimate the mechanical characteristics of concrete. This study aimed to compare the capacities of four ML methods: eXtreme gradient boosting (XG Boost), gradient boosting (GB), Cat boosting (CB), and extra trees regressor (ETR), to predict the splitting tensile strength of 28-day-old self-compacting concrete (SCC) made from recycled aggregates (RA), using data obtained from the literature. A database of 381 samples from literature published in scientific journals was used to develop the models. The samples were randomly divided into three sets: training, validation, and test, with each having 267 (70%), 57 (15%), and 57 (15%) samples, respectively. The coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) metrics were used to evaluate the models. For the training data set, the results showed that all four models could predict the splitting tensile strength of SCC made with RA because the R2 values for each model had significance higher than 0.75. XG Boost was the model with the best performance, showing the highest R2 value of R2 = 0.8423, as well as the lowest values of RMSE (=0.0581) and MAE (=0.0443), when compared with the GB, CB, and ETR models. Therefore, XG Boost was considered the best model for predicting the splitting tensile strength of 28-day-old SCC made with RA. Sensitivity analysis revealed that the variable contributing the most to the split tensile strength of this material after 28 days was cement. [ABSTRACT FROM AUTHOR]

The use of 3D printing is becoming more and more popular, and new applications in extrusion and photopolymerization are being discovered every day. Due to the unidirectional action of the materials used, it is necessary to develop polyfunctional composites that provide a complex of extrusion and photopolymerization processes for 3D printing technology. The current problem of 3D printing application is the availability of ineffective composite materials with deteriorated mechanical and damping properties, which makes it difficult to use existing composite materials for application in 3D printing technologies. Therefore, the aim of this study was to create a biodegradable composite material based on polylactic acid and poly(3‐hydroxybutyrate) reinforced with cellulose fibrils and plasticized with polyoxyethylene for possible application in extrusion, using the example of Maestro 3D printer. The study looked at mechanical, dynamic and thermal indicators. Except for the mechanical ones, all of the indicators tested out to be quite good. Thus, composite PLA: PHB (3:7) + 10% CFs demonstrated the highest glass transition temperature of 57°C. A 75% weight loss occurred at 373.58°C. In future, it is necessary to investigate alternative types of characteristics, modify the material's composition, or use it in applications where they are not critical. The scientific novelty of the work is the creation of a biodegradable composite material to study the properties of the composite material by extrusion for the purpose of application in 3D printing technologies. [ABSTRACT FROM AUTHOR]

One of the prime objectives of this review is to understand the role of design parameters on the mechanical properties (Compressive and split tensile strength) of Self-Compacting Concrete (SCC) with recycled aggregates (Recycled Coarse Aggregates (RCA) and Recycled Fine Aggregates (RFA)). The design parameters considered for review are Water to Cement (W/C) ratio, Water to Binder (W/B) ratio, Total Aggregates to Cement (TA/C) ratio, Fine Aggregate to Coarse Aggregate (FA/CA) ratio, Water to Solid (W/S) ratio in percentage, superplasticizer (SP) content (kg/cu.m), replacement percentage of RCA, and replacement percentage of RFA. It is observed that with respect to different grades of SCC, designed parameters affect the mechanical properties of SCC with recycled aggregates. [ABSTRACT FROM AUTHOR]

Most concrete studies are concentrated on mechanical properties especially strength properties either directly or indirectly (fresh and durability properties). Hence, the ratio of split tensile strength to compressive strength plays a vital role in defining the concrete properties. In this review, the impact of design parameters on the strength ratio of various grades of Self-Compacting Concrete (SCC) with recycled aggregate is assessed. The design parameters considered for the study are Water to Cement (W/C) ratio, Water to Binder (W/B) ratio, Total Aggregates to Cement (TA/C) ratio, Fine Aggregate to Coarse Aggregate (FA/CA) ratio, Water to Solid (W/S) ratio in percentage, superplasticizer (SP) content (kg/cu.m), replacement percentage of recycled coarse aggregates (RCA), replacement percentage of recycled fine aggregates (RFA), fresh density and loading area of the specimen. It is observed that the strength ratio of SCC with recycled aggregates is affected by design parameters. [ABSTRACT FROM AUTHOR]