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In order to better understand the effect of compositional parameters on the properties of magnesium phosphate cement (MPC) mortar, the relationship between the thickness of paste film and the workability and strength of MPC mortar is revealed. A three-parameter filling density prediction model is adopted to study the filling density of sand with different gradations. The validity of the three-parameter filling density prediction model is validated by experimental results. The thickness of the paste film of MPC mortar is calculated with different sand gradations. The results show that the thickness of paste film has a great influence on the slump flow and strength of MPC mortar. The linear positive relationship between paste film thickness and slump flow of MPC mortar. At different sand-to-binder ratios, there is no significant linear relationship between the thickness of the paste film and the mechanical properties. But under the same sand-to-binder ratio, there is an optional thickness of paste film for the strength of the MPC mortar. Comprehensively considering the workability and mechanical properties, magnesium phosphate cement mortar's optimal paste film thickness ranges from 73 µm to 74 µm. When designing the proportion of magnesium phosphate cement, the appropriate thickness of the paste film can be selected according to the different engineering types and construction environments. [ABSTRACT FROM AUTHOR]

Wind-turbine blades pose significant disposal challenges in the wind-energy sector due to the increasing demand for wind farms. Therefore, this study researched the revaluation of Raw-Crushed Wind-Turbine Blade (RCWTB), obtained through a non-selective blade crushing process, as a partial substitute for aggregates in Self-Compacting Concrete (SCC). The aim was to determine the most adequate water/cement (w/c) ratio and amount of superplasticizing admixtures required to achieve adequate flowability and 7-day compressive strength in SCC for increasing proportions of RCWTB, through the production of more than 40 SCC mixes. The results reported that increasing RCWTB additions decreased the slump flow of SCC by 6.58% per 1% RCWTB on average, as well as the compressive strength, although a minimum value of 25 MPa was always reached. Following a multi-criteria decision-making analysis, a w/c ratio of 0.45 and a superplasticizer content of 2.8% of the cement mass were optimum to produce SCC with up to 2% RCWTB. A w/c ratio of 0.50 and an amount of superplasticizers of 4.0% and 4.6% were optimum to produce SCC with 3% and 4% RCWTB, respectively. Concrete mixes containing 5% RCWTB did not achieve self-compacting properties under any design condition. All modifications of the SCC mix design showed statistically significant effects according to an analysis of variance at a confidence level of 95%. Overall, this study confirms that the incorporation of RCWTB into SCC through a careful mix design is feasible in terms of flowability and compressive strength, opening a new research avenue for the recycling of wind-turbine blades as an SCC component. [ABSTRACT FROM AUTHOR]

Self‐compacting concrete (SCC) is a specialized type of concrete that features excellent fresh properties, enabling it to flow uniformly and compact under its weight without vibration. SCC has been one of the most significant advancements in concrete technology over the past two decades. In efforts to reduce the environmental impact of cement production, a major source of CO2 emissions, silica fume (SF) is often used as a partial replacement for cement. SF‐modified SCC has become a common choice in construction. This study explores the effectiveness of soft computing models in predicting the compressive strength (CS) of SCC modified with varying amounts of silica fume. To achieve this, a comprehensive database was compiled from previous experimental studies, containing 240 data points related to CS. The compressive strength values in the database range from 21.1 to 106.6 MPa. The database includes seven independent variables: cement content (359.0–600.0 kg/m3), water‐to‐binder ratio (0.22–0.51), silica fume content (0.0–150.0 kg/m3), fine aggregate content (680.0–1166.0 kg/m3), coarse aggregate content (595.0–1000.0 kg/m3), superplasticizer content (1.5–15.0 kg/m3), and curing time (1–180 days). Four predictive models were developed based on this database: linear regression (LR), multi‐linear regression (MLR), full‐quadratic (FQ), and M5P‐tree models. The data were split, with two‐thirds used for training (160 data points) and one‐third for testing (80 data points). The performance of each model was evaluated using various statistical metrics, including the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), objective value (OBJ), scatter index (SI), and a‐20 index. The results revealed that the M5P‐tree model was the most accurate and reliable in predicting the compressive strength of SF‐based SCC across a wide range of strength values. Additionally, sensitivity analysis indicated that curing time had the most significant impact on the mixture's properties. [ABSTRACT FROM AUTHOR]

The mechanical properties of concrete, such as compressive strength CS and slump flow SL rates, are very nonlinear. For academics, it is crucial to forecast these qualities while creating new building methods. Such capabilities should be developed to lower the cost of expensive tests and increase the precision of the measurements. The goal of this study is to create an radial basis function neural network RBFNN to describe the characteristics of hardness in high‐performance concrete (HPC) mix. Metaheuristic techniques were used to enhance the RBFNN's functionality. A dataset of 181 HPC mixes comprising ecologically beneficial ingredients, such as fly ash and silica fume, was used for training and evaluating the capabilities of the proposed hybrid models. According to the modeling process based on sensitivity analysis of input parameters and the results of hybrid models, the model combined with the multiverse optimization algorithm (MVO) had a higher correlation between the predicted and observed CS and slump values than the model combined with three optimization algorithms in terms of the R2 index being the maximum value of 0.984 in the tasting phase of CS and SL estimation. While evaluating two mechanical aspects of HPC samples, the RMSE of the model coupled with the MVO algorithm reconfirmed its accuracy being 3.59. [ABSTRACT FROM AUTHOR]

Sludge ceramsite (SC) can be utilized as a lightweight aggregate in concrete, especially in external wall materials, due to the increasing volume of polluted sludge, which contributes to water system deterioration and poses greater threats to human health. The influence of the fresh mortar's slump flow on the dispersion of ceramsite was studied. The ultrasonic sound velocity, capillary water absorption rate, compressive strength, and coefficient of variation (CV) were measured in this study. Thermogravimetric (TG) analysis, ultra depth-of-field microscope scanning, X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive spectrometry (EDS) were used to analyze the performance mechanism of the ceramsite concrete. The results indicated that adding SC could reduce the fluidity of the fresh concrete, with a reduction by rates of up to 2.04%. The addition of WRA could improve the fluidity by rates of up to 60.77%. The relationship between the ultrasonic sound speed and the increasing fluidity could be deduced as a negative correlation. The water absorption was negatively correlated with the compressive strength. The concrete with a slump flow of 12.35 and 12.5 cm reached the maximum compressive strength, which had the lowest water absorption, and demonstrated internal homogeneity. The optimum slump flow was 12.35 and 12.5 cm. With the slump flow of 12.5 cm, the corresponding CV was the lowest, showing the optimum SC's dispersion. Through TG, XRD, and SEM analyses, it was verified that the addition of 0.6% WRA promoted the hydration of cement. In addition, SC increased the hydration products. [ABSTRACT FROM AUTHOR]

Formulating a mix design for 3D concrete printing (3DCP) is challenging, as it involves an iterative approach, wasting a lot of resources, time, and effort to optimize the mix for strength and printability. A potential solution is mix formulation through artificial intelligence (AI); however, being a new and emerging field, the open-source availability of datasets is limited. Limited datasets significantly restrict the predictive performance of machine learning (ML) models. This research explores data augmentation techniques like deep generative adversarial network (DGAN) and bootstrap resampling (BR) to increase the available data to train three ML models, namely support vector machine (SVM), artificial neural network (ANN), and extreme gradient boosting regression (XGBoost). Their performance was evaluated using R2, MSE, RMSE, and MAE metrics. Models trained on BR-augmented data showed higher accuracy than those trained on the DGAN-augmented data. The BR-trained XGBoost exhibited the highest R2 scores of 0.982, 0.970, 0.972, 0.971, and 0.980 for cast compressive strength, printed compressive strength direction 1, 2, 3, and slump flow respectively. The proposed method of predicting the slump flow (mm), cast, and anisotropic compressive strength (MPa) can effectively predict the mix design for printable concrete, unlocking its full potential for application in the construction industry. [ABSTRACT FROM AUTHOR]

3D Concrete Printing (3DCP) is an advanced technology in manufacturing and building sector. This technology is now a crucial component for developing cutting-edge machinery that could resolve many challenges faced by conventional building construction. Despite their benefits, the quality of materials used in 3DCP still requires further attention to meet established concrete standards. Therefore, reforming the conventional construction industry with sustainable waste sources for 3DCP technology is a strategic approach. Since the current range of 3DCP material is still restricted, expanding the selection to include more eco-friendly options could be highly beneficial. This paper aims to explores the potential in utilizing waste sources as a supplementary cementitious material (SCM) for 3DCP mixture in building construction. It reviews the recent and past research pertinently on sustainable waste (rubber, polymer, construction, industrial, recycled sand, and glass) products as supplements or additions for the 3DCP mixture. Rheological and compressive strength characteristics of the 3DCP combination are examined and contrasted with those of other waste materials. All gathered information will be examined considering the literature research to identify the combination for 3DCP to achieve improvement in building materials. Using waste as an SCM component in 3DCP mixtures supports sustainable construction practices. Waste materials have shown potential to improve the rheology (slump, workability, extrudability) and compressive strength of 3D-printed concrete. Compared to the conventional building construction method, optimising waste in a 3DCP will promote efforts to minimise waste creation and maximise the efficient use of commodities. Therefore, incorporating sustainable waste into 3DCP mixtures is a promising area of study for further research.

Abstract In this paper, an artificial intelligence approach has been employed to analyze the slump and compressive strength (CS) of high-performance concrete (HPC), focusing on its mechanical properties. The importance of assessing these critical concrete characteristics has been widely acknowledged by experts in the field, leading to the development of innovative methods for estimating parameters that typically require laboratory testing. These intelligent techniques improve the accuracy of mechanical property predictions and reduce the resource-intensive and costly nature of experimental work. The radial basis function neural network (RBFNN) is the foundational model for predicting the mechanical attributes of various HPC mixtures. To fine-tune the RBFNN’s performance in replicating the mechanical properties of HPC samples, two optimization algorithms, namely the Golden Eagle Optimizer (GEO) and Dynamic Arithmetic Optimization Algorithm (DAOA), have been employed. In this manner, both RBGE and RBDA models were trained using a dataset comprising 181 HPC samples that included superplasticizers and fly ash. The results show that DAOA has significantly improved the base model’s predictive capability, achieving a higher correlation with a value R 2 of 0.936 when estimating slump. Furthermore, RBDA exhibited a more favorable root mean square error (RMSE) in predicting compressive strength compared to RBGE, with a notable 16% difference. Ultimately, both integrated models demonstrated their effectiveness in accurately modeling the mechanical properties of HPC.

Self-compacted concrete (SCC) is one of the special types of concrete. The SCC represents one of the most significant developments in concrete technology over the previous two decades. It can compact itself using its weight without requiring vibration due to its excellent fresh characteristics, which allow it to flow into a uniform level under the impact of gravity. Since cement manufacturing is one of the largest contributors to CO2 gas emissions into the atmosphere, fly ash (FA) is used in concrete as a cement replacement. Currently, FA-modified SCC is widely utilized in construction. This research aimed to study the potential of soft computing models in predicting the compressive strength (CS) and slump flow diameter (SL) of self-compacted concrete modified with different fly ash content. Hence, two databases were created, and relevant experimental data was collected from previous studies. The first database consists of 303 data points and is used to predict the CS. The second database predicts the SL and contains 86 data points. The dependent parameters are the CS, which varies from 9.7 to 79.2 MPa, and the SL, which varies from 615 to 800 mm. The identical five independent parameters are available in each database. The ranges for CS prediction are water-to-binder ratio (0.27–0.9), cement (134.7–540 kg/m3), sand (478–1180 kg/m3), fly ash (0–525 kg/m3), coarse aggregate (578–1125 kg/m3), and superplasticizer (0–1.4%). The data ranges for the SL prediction, on the other hand, are as follows: water-to-binder ratio (0.26–0.58), cement (83–733 kg/m3), sand (624–1038 kg/m3), fly ash (0–468 kg/m3), coarse aggregate (590–966 kg/m3), and superplasticizer (0.1–21.84%). Each database has developed three models for the prediction: full-quadratic (FQ), interaction (IN), and M5P-tree models. Each database is divided into two groups, with training comprising two-thirds of the total data points and testing containing one-third. As a result, 202 training data and 101 testing data are in the first database. The other database consists of 57 data points for training and 29 for testing. Various statistical tools are used to evaluate the performance of each proposed model, such as R2 (correlation of coefficient), RMSE (root mean squared error), SI (scatter index), MAE (mean absolute error), StDev, OBJ (objective value), a-20 index, and Z-score. The results showed that the FQ and IN models have the highest accuracy and reliability in predicting the compressive strength and slump flow of FA-based SCC, respectively. Moreover, the sensitivity analysis revealed that the cement content is the most influential contributor to the mixtures. [ABSTRACT FROM AUTHOR]

In this paper, an artificial intelligence approach has been employed to analyze the slump and compressive strength (CS) of high-performance concrete (HPC), focusing on its mechanical properties. The importance of assessing these critical concrete characteristics has been widely acknowledged by experts in the field, leading to the development of innovative methods for estimating parameters that typically require laboratory testing. These intelligent techniques improve the accuracy of mechanical property predictions and reduce the resource-intensive and costly nature of experimental work. The radial basis function neural network (RBFNN) is the foundational model for predicting the mechanical attributes of various HPC mixtures. To fine-tune the RBFNN's performance in replicating the mechanical properties of HPC samples, two optimization algorithms, namely the Golden Eagle Optimizer (GEO) and Dynamic Arithmetic Optimization Algorithm (DAOA), have been employed. In this manner, both RBGE and RBDA models were trained using a dataset comprising 181 HPC samples that included superplasticizers and fly ash. The results show that DAOA has significantly improved the base model's predictive capability, achieving a higher correlation with a value R2 of 0.936 when estimating slump. Furthermore, RBDA exhibited a more favorable root mean square error (RMSE) in predicting compressive strength compared to RBGE, with a notable 16% difference. Ultimately, both integrated models demonstrated their effectiveness in accurately modeling the mechanical properties of HPC. [ABSTRACT FROM AUTHOR]

In this research, a one-hidden layer artificial neural network paradigm (ANN) was created to forecast the slump flow of ultra-highperformance concrete (UHPC). To achieve this goal, 3,200 ANNs were evaluated to estimate the fresh UHPC's slump flow utilizing 793 observations. The performance metrics measured on training and test data subsets were in the same order of magnitude, thereby pointing out the proper work of the k-fold validation procedure. The results of the connection weight approach analysis (CWA) indicated that water dosage had the highest positive importance in slump flow, preceding the superplasticizer volume ratio. Other factors that positively influenced slump flow were the water-to-powder ratio, the dosage of high-alkali glass powder, the water-to-binder ratio, and limestone concentration. The most negative influences on rheology were the high-alumina FC3R and metakaolin. The ANN accurately predicted the slump flow of UHPC, while the results of the CWA analysis were well-correlated with previous research. [ABSTRACT FROM AUTHOR]

Formulating a mix design for 3D concrete printing (3DCP) is challenging, as it involves an iterative approach, wasting a lot of resources, time, and effort to optimize the mix for strength and printability. A potential solution is mix formulation through artificial intelligence (AI); however, being a new and emerging field, the open-source availability of datasets is limited. Limited datasets significantly restrict the predictive performance of machine learning (ML) models. This research explores data augmentation techniques like deep generative adversarial network (DGAN) and bootstrap resampling (BR) to increase the available data to train three ML models, namely support vector machine (SVM), artificial neural network (ANN), and extreme gradient boosting regression (XGBoost). Their performance was evaluated using R2, MSE, RMSE, and MAE metrics. Models trained on BR-augmented data showed higher accuracy than those trained on the DGAN-augmented data. The BR-trained XGBoost exhibited the highest R2 scores of 0.982, 0.970, 0.972, 0.971, and 0.980 for cast compressive strength, printed compressive strength direction 1, 2, 3, and slump flow respectively. The proposed method of predicting the slump flow (mm), cast, and anisotropic compressive strength (MPa) can effectively predict the mix design for printable concrete, unlocking its full potential for application in the construction industry.

The workability regulation of fresh self-compacting concrete (SCC) is very important for its on-site construction and even hardened performance. To further improve the accuracy of predicting the workability parameters of fresh SCC through the initial mix proportion, two common machine learning calculation models, i.e. BP neural network and support vector regression (SVR) were used to analyze the potential functional relationship between the input variables of mix proportion and slump flow, L-box filling ratio and V-funnel flow time. Before calculation, the data set was divided into training set, verification set and test set according to the proportion of 60%, 20% and 20%. Three machine learning performance evaluation indexes, namely determination coefficient (R2), mean square error (MSE) and mean absolute percentage error (MAPE), were used to characterize and compare their prediction accuracy differences. Through sensitivity analysis, the change trends of the single variable on the results were studied. The results show that BP neural network has good learning ability for the slump flow. The prediction results of SVR model for SCC slump flow, L-box filling ratio and V-funnel flow time are higher than those of BPNN model. Taking the V-funnel flow time as an example, the overall data set R2, MSE and MAPE based on SVR are 0.921 3, 0.860 2 and 14.519, respectively, which are higher than the corresponding 0.916 2, 1.128 1 and 18.007 of BPNN, respectirely. The results of parameter sensitivity analysis show that the higher the cement content is, the lower the slump flow, the higher the L-box filling ratio and V-funnel flow time are. At the same time, the slump flow of SCC will be improved with the increase of water-to-binder ratio and superplasticizer content. [ABSTRACT FROM AUTHOR]

Self-compacted concrete (SCC) is a special type of concrete; it is a liquid mixture appropriate for structural elements with excessive reinforcement without vibration. SCC is commonly produced by increasing the paste volume and cement content. As cement production is one of the huge factors in releasing CO2 gas into the atmosphere, by-product materials such as fly ash are utilized as a cement replacement in concrete. In addition to the positive environmental impact, fly ash can maintain an excellent fresh and mechanical property. Incorporating fly ash into self-compacted concrete is widely applied in practice. However, its application is frequently limited by a lack of knowledge about the mixed material gained from laboratory tests. The most significant mechanical property for all concrete types is compressive strength (CS); also, the slump flow diameter (SL) in the fresh state is a crucial property for SCC. Hence, developing an accurate and reliable model for predicting the CS and SL is very important for saving time and energy, as well as lowering the cost. This research study proposed a projection of both the CS and SL of SCC modified with fly ash by three different model approaches: Nonlinear regression (NLR), Multi-Linear regression (MLR), and Artificial Neural Networks (ANN). In this regard, two different datasets were collected and analyzed for developing models: 308 data samples were used for predicting the CS, and 86 data samples for the SL. Each database included the same five independent parameters. The ranges for CS prediction were: cement (134.7–583 kg/m3), water-to-binder ratio (0.27–0.9), fly ash (0–525 kg/m3), sand (478–1180 kg/m3), coarse aggregate (578–1125 kg/m3), and superplasticizer (0–1.4%). The dependent parameter (CS) ranged from 9.7 to 81.3 MPa. On the other hand, the data ranges for the SL prediction included independent parameters such as cement (83–733 kg/m3), water-to-binder ratio (0.26–0.58), fly ash (0–468 kg/m3), sand (624–1038 kg/m3), coarse aggregate (590–966 kg/m3), and superplasticizer (0.087–21.84%). Also, the dependent parameter (SL) ranged from 615 to 800 m. Various statistical assessment tools, such as the coefficient of determination (R2), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Objective value (OBJ), and Scatter Index (SI), were used to evaluate the performance of the developed models. The results showed that the ANN model best predicted the CS and SL of SCC mixtures modified with fly ash. Furthermore, the sensitivity analysis demonstrated that the cement content is the most effective factor in predicting the CS and SL of SCC mixtures. [ABSTRACT FROM AUTHOR]

The compressive strength and slump of concrete have highly nonlinear functions relative to given components. The importance of predicting these properties for researchers is greatly diagnosed in developing constructional technologies. Such capacities should be progressed to decrease the cost of expensive experiments and enhance the measurements' accuracy. This study aims to develop a Radial Basis Function Neural Network (RBFNN) to model the hardness features of High-Performance Concrete (HPC) mixtures. In this function, optimizing the predicting process via RBFNN will be aimed to be accurate, as the aim of this research, conducted with metaheuristic approaches of Henry gas solubility optimization (HGSO) and Multiverse Optimizer (MVO). The training phase of models RBHG and RBMV was performed by the dataset of 181 HPC mixtures having fly ash and superplasticizer. Regarding the results of hybrid models, the MVO had more correlation between the predicted and observed compressive strength and slump values than HGSO in the R2 index. The RMSE of RBMV (3.7 mm) was obtained 43.2 percent lower than that of RBHG (5.3 mm) in the appraising slump of HPC samples, while, for compressive strength, RMSE was 3.66 MPa and 5 MPa for RBMV and RBHG respectively. Moreover, to appraise slump flow rates, the R2 correlation rate for RBHG was computed at 96.86 % while 98.25 % for RBMV in the training phase, with a 33.30% difference. Generally, both hybrid models prospered in doing assigned tasks of modeling the hardness properties of HPC samples. [ABSTRACT FROM AUTHOR]

High‐performance concrete (HPC) as a highly sophisticated aggregate in constructional projects has made modeling given mechanical properties a very complex problem. Declaring by many studies, mechanical features of HPC are not only characterized by the maximum size of coarse aggregate and water amount since influencing by the other components. Using fly‐ash and silica fume as the key constituents can simultaneously increase the hardness aspects and the environmental effects. Considering the compressive strength and slump flow of concrete should be investigated before performing any practical practices. Artificial intelligence approaches with precise and low‐cost methods can replace the costly experimental ways. Therefore, the present paper has aimed to link a prediction model with optimization algorithms to accurately appraise the hardness properties of HPC samples rarely found in literature like this way. In this regard, a machine learning approach of Support Vector Regression using two kernels of Gaussian and radial basis function is coupled with matheuristic algorithms to optimize the modeling process of compressive strength and slump flow of HPC samples. The internal settings of SVR would be tuned at optimal rate by optimizers to function efficiently. To investigate the performance of hybrid frameworks developed in this research, several indicators evaluated the results of hybrid models. Therefore, the R2 of the models was calculated averagely at 0.91 with a maximum difference rate of 11% for the testing phase. While the RMSE index assessed the models with higher values of 16.56 mm for slump and 12.86 MPa for compressive strength. Generally, using smart approaches with high‐accuracy performance has been proposed to be used instead of physical procedures increasing the productivity of concrete compressive strength in terms of time, energy, and cost criteria. [ABSTRACT FROM AUTHOR]

The ultra-high performance concrete (UHPC) was prepared by using the limestone coarse aggregate and ordinary Portland cement. The effects of sand ratio, water to cement ratio (W/C), cementitious materials, type and content of fiber, dosage of water reducer and defoamer on the mechanical and rheological properties of the UHPC were investigated. The correlations among influence factors and performances of UHPC were discussed. Moreover, the internal relationship between loading rate and elasticity modulus of UHPC was also studied. The results indicate that the compressive strength of UHPC first increases and then decreases with the increase of sand ratio, and a maximum compressive strength of UHPC was found at the sand ratio of 41%. The water reducer content of fresh UHPC firstly decreases and then increases with the increase of sand ratio. The compressive strength of UHPC firstly increases and then decreases with the increase of W/C, which also increases with the increase of cementitious materials. The elasticity modulus of UHPC decreases with increase of sand ratio and cementitious materials content, and the compressive strength and elasticity modulus of UHPC cured for 28 d increase with the increase of defoamer dosage. The slump flow of fresh UHPC first increases and then decreases with the increase of water reducer dosage. An optimum fiber content of UHPC can enhance the tensile strength and adhesive property, which can play the restraining deformation and toughening effect.

Different properties of Self-compacting concrete (SCC) containing plastic waste aggregate (PWA) have been experimentally studied by researchers. However, most of these works focused on examining the properties of one type of PA. In the present paper, the influence of four different types; namely Polyvinyl chloride (PVC), Heat-treated plastic (PEL), Mixed plastic (Mix), and polyethylene terephthalate (PET) as a fine aggregate (FA) replacement; on fresh properties of SCC was examined. Results indicated that changing the PWA geometry influenced different properties of SCC. All concrete samples with PVC and PEL plastic were in the range of EFNARC classification (classified in VS2/PA2 class), causing no blocking in V-funnel and L-box test. Meanwhile, mixed plastic up to 7.5% and PET up to 5% fall within VS2/VF2 class; otherwise, the mixture was outside the range of EFNRAC standards. The best plastic waste aggregate regarding all new properties was PVC confirming all requirements for a successful SCC, causing no blocking or segregation. Thus, 10% was selected as the optimum percentage. Furthermore, PET was the worst, for PET-7.5% significant increase in the V-funnel (57.6 sec) and reduction in H2/H1 ratio (0.58) was obtained besides blocking in L-box tests, segregation, and bleeding in slump flow test. Thus, more than 5% is not recommended when using PET in Self-compacting concrete.

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Self-consolidating concrete (SCC) is a concrete that able to flow and consolidate under its own weight, and it is cohesive enough to fill spaces of almost any size and shape without segregation or bleeding. In this study, workability and strength characteristics of seven SCC mixes were examined and compared with two additional vibrated mixes of normal and high strength. For this purpose, the flowability, deformability, and passing ability of fresh concrete mixes were tested through slump test, slump flow, T500, and the J-ring tests. Furthermore, the hardened concrete specimens were tested for mechanical properties with the variation in shape and size of the specimens at six different ages. The results revealed that addition of micro-silica is more effective in improving concrete workability and strength than blended micro-silica and fly ash. A well-designed SCC could have an excellent flow (730 mm) and passing ability (ΔH = 4 mm), without sacrificing the early strength (22.3 MPa in 1 day), or long-term strength (107.7 MPa in 90 days). Results also showed that the compressive strength and the tensile strength of SCC mixes were less affected by specimen shape and size compared to conventional concrete mixes.

The relation between slump flow and yield stress of ultra-high performance concrete (UHPC) mixtures was studied with theoretical analysis and experimentation. The relational expression between slump flow and yield stress of UHPC mixtures was built and then verified with a rheological test. The results showed that the prediction model, as a function of cone geometry of dimensionless slump flow and dimensionless yield stress of the UHPC mixtures, was constructed based on Tresca criteria, considering the geometric relation of morphological characterization parameters before and after slump of the UHPC mixtures. The rationality and applicability of the dimensionless prediction model was verified with a rheological test and a slump test of UHPC mixtures with different dosages of polycarboxylate superplasticizer. With increase in polycarboxylate superplasticizer dosage, yield stress of the two series of UHPC mixtures (large/small binding material consumption) gradually decreased, leading to a gradual increase in slump flow. Based on the prediction model of dimensionless slump flow and dimensionless yield stress, the relational expression between slump flow and yield stress of the UHPC mixtures was built. The comparison result showed that the calculated data was consistent with the experimental data, which provided a new method for predicting yield stress of UHPC mixtures with a slump test. [ABSTRACT FROM AUTHOR]

Self-compacting concrete (SCC) has been developed as a type of concrete capable of filling narrow gaps in highly reinforced areas of a mold without internal or external vibration. Bleeding and segregation in SCC can be prevented by the addition of superplasticizers. Due to these favorable properties, SCC has been adopted worldwide. The workability of SCC is closely related to its yield stress and plastic viscosity levels. Therefore, the accurate prediction of yield stress and plastic viscosity of SCC has certain advantages. Predictions of the shear stress and plastic viscosity of SCC is presented in the current study using four different ensemble machine learning techniques: Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), random forest, and Categorical Gradient Boosting (CatBoost). A new database containing the results of slump flow, V-funnel, and L-Box tests with the corresponding shear stress and plastic viscosity values was curated from the literature to develop these ensemble learning models. The performances of these algorithms were compared using state-of-the-art statistical measures of accuracy. Afterward, the output of these ensemble learning algorithms was interpreted with the help of SHapley Additive exPlanations (SHAP) analysis and individual conditional expectation (ICE) plots. Each input variable's effect on the predictions of the model and their interdependencies have been illustrated. Highly accurate predictions could be achieved with a coefficient of determination greater than 0.96 for both shear stress and plastic viscosity. [ABSTRACT FROM AUTHOR]

The mix proportion, phase diagram components and mechanical properties of ultra-high performance mortar (UHPM) based on simplex-centroid design method were investigated. The effects of material components, sand/binder ratio (S/B) and water/binder ratio (W/B) on the strength and fluidity of UHPM were studied. Simultaneously, the relationship between water reducer dosage and slump flow of UHPM was investigated as well as the mix proportion design and performance prediction. The results showed that the simplex-centroid design method can be used to determine the relationship between influence factors and performance of UHPM. The differences of strength phase diagram for different mix proportion of UHPM were manifested in central position of minimum strength, strength contours and gradient, and position of maximum strength. There has a quadratic function relationship between water reducer dosage, slump flow and W/B. The slump flow of UHPM increases with the increase of water reducer dosage. The UHPM can be prepared based on simplex-centroid experimental design method.

Self-Compacting Concrete (SCC) is an innovative concrete that flows under its weight, completely filling formwork even in areas of congested reinforcement without the need for vibration to attain adequate compaction. To achieve the SCC, a lower content of aggregates and high content of Portland Cement (PC) are required, which in turn consumes a lot of energy and emits greenhouse gases like carbon dioxide (CO2) and Methane (CH4) to the atmosphere during the PC production. In this study, Cassava Peel Ash (CPA) was used as supplementary cementitious material to partially replace the PC by 5%, 10%, 15%, 20%, and 25%. Response Surface Methodology (RSM) was used to develop a predicting model for the compressive strength of CPA and SCC. This study investigates modeling the workability properties of SCC made with the CPA. The result findings showed that both slump flow and passing ability are affected with the addition of CPA when more than 15% of the cement content is replaced with CPA while the segregation resistance improves as the content of CPA increases. The utilization of CPA in SCC is optimized at 5% replacement of cement with CPA and 28 days of curing age to achieve a compressive strength of grade 35. The developed RSM model showed a high degree of relationship between the variables, responses and can be used to predict the compressive strength of the CPA and SCC accurately.

The slump flow test is a widespread, frequently used and standardized test of workability and especially consistency in the field of concrete processing. While good quality information on yield stress can be derived from it, there is a lack of information on viscosity to date. The outlined paper demonstrates, how to derive plastic viscosity from an advanced evaluation of an automated test set up extended by video recordings of the slump flow test. Cementitious pastes and self-compacting concretes are investigated in a systematic study. The new approach for determining viscosity is introduced by evaluating the data from sectional radial flow velocity recordings with a corresponding modelling strategy. The methodology can also be adapted to the conventional slump flow test without additional technical equipment. Finally, the plastic viscosity is derived within defined constraints by calculating an idealised shear rate and shear stress of a flow curve. Furthermore, the influence of the lifting process and mix composition on the accuracy of the test is demonstrated by comparing conventional and automated mini-slump flow tests, which is useful for applying the test in quality control. [Display omitted] • Automated slump flow evaluation method for flowable cementitious mixes. • Viscosity determination of flowable cementitious mixes using the slump flow test. • Determination of dispersion measures in the slump flow test, categorized by material, process and human influences. [ABSTRACT FROM AUTHOR]

Abstract The aim of this research is to provide a quantitative method for evaluating concrete segregation. Because of various conditions of concrete materials, mix proportions, and delivery, concrete can be segregated. The acquisition inspection executed in construction field for supplied ready-mixed concrete is an important quality control process for concrete. Among the inspections conducted at the project site, segregation of concrete mixture should be evaluated before placing the concrete mixture, currently a qualitative inspection on concrete segregation was conducted. For a normal concrete mixture with slumping behavior, shear slump or collapse slump often occur as an indication of segregation. The suggested evaluation index of segregation for normal concrete (EISN) was induced from the shape of the concrete slumping: relation between the maximum distance of flow and the minimum distance of flow. To evaluate the feasibility of EISN, two different concrete mixture conditions were tested. The recommended EISN parameter of segregation is 1.09 using the three grades of concrete quality. This new quantitative method of evaluating segregation of the concrete mixture is expected to contribute to a more efficient quality control in concrete construction.

The sustainability of UHPC necessitates and requires incorporating supplementary cementitious materials (SCMs) and eco-efficient materials. In this study, silica fume (SF), fly ash (FA), and granulated blast furnace slag (GBFS) were the utilized supplementary cementitious materials. Also, blast furnace cement (CEM III) was used as eco-efficient material. CEM III gains its sustainability benefits via containing about 36–65% granulated slag in its composition. This study aims to develop UHPC mixture proportions using SCMs and CEM III so that UHPC may be made more sustainable and affordable to a wider variety of applications. The largest possible part of the amount of Portland cement (PC) could be replaced with sustainable environmentally friendly materials. The main replacement material used in this study was blast furnace cement, where the type (CEM III/A) was used, which involves 50% granulated blast furnace slag (GBFS) in its combination. Thus, this cement is financially and environmentally sustainable. The PC was completely replaced by this sustainable cement in the majority of the mixtures of this study; and in other mixtures, a part of the PC weight was partially replaced by CEM III. On the other hand, FA and GBFS were also utilized as cement replacements from PC or CEM III main binder in the residual mixtures. Fifty-five different concrete mixes with SF contents of 15%, 20%, and 25%; and cement replacement percentages of 0%, 10%, 20%, 30%, 40%, and 50% were prepared with locally available materials. The mixes were tested for slump flow and mechanical properties. The findings of mechanical properties indicated that the optimum level for partial replacement of cement by CEM III, and GBFS was 10%, but for FA was 20%. The minimum 28 days compressive strength values of affordable and sustainable ultra-high-performance concrete were 160.9, 150.5, and 152.3 MPa which were detected at 50%, 30%, 20% replacement percentages of FA, CEM III, and GBFS respectively

The aim of this study is to investigate the fresh and hardened properties of the self-compacting heavy-weight concrete (SCHWC) and to develop a mathematical model for the prediction of these properties. The binder was the Portland cement and fly ash (FA). Barite aggregate was used to achieve the heavy-weight concrete (HWC). A polycarboxylate based super plasticizer was used to increase workability and reach self-compacting feature. To research the fresh and hardened properties SCHWC many concrete mixes were prepared accordingly with "water-cement ratios", "total aggregate-cement ratios", and "fly ash-cement ratios". These samples were tested to get the slump-flow, V-funnel, 7 and 28-day compressive strength values. The Response Surface Methodology (RSM) was used to develop regression equations using these experimental results. It is observed that the estimated values obtained with RSM are compatible with those obtained by the experimental method for the fresh and hardened properties of SCHWC. [ABSTRACT FROM AUTHOR]

Ultra-high-performance concrete (UHPC) results from the mixture of several constituents, leading to a highly complex material in both, fresh and hardened state. The higher number of constituents, together with a higher number of possible combinations, relative proportioning and characteristics, makes the behavior of this type of concrete more difficult to predict. The objective of the research is to build four analytical models, based on artificial neural networks (ANN), to predict the 1-day, 7-day, and 28-day compressive strengths and slump flow. Recycled glass powder milled to different particle size, fluid catalytic cracking residue (FCC) and different particle size limestone powder was used as partial replacements for Portland cement and silica fume. The ANN models predicted the 1-day, 7-day, and 28-day compressive strengths and slump flow of the test set with prediction error values (RMSE) of 2.400 MPa, 2.638 MPa, 2.064 MPa and 7.245 mm respectively. The results indicated that the developed ANN models are an efficient tool for predicting the slump flow and compressive strengths of UHPC while incorporating silica fume, limestone powder, recycled glass powder and FCC. [ABSTRACT FROM AUTHOR]

This paper explores one of the major environmental concerns which is disposal or recycling of the waste materials”. “Marble processing plants produce millions of tons of waste dust in the powder form every year”. “Having a considerable high degree of fineness in comparison to cement, marble powder may be utilized as filler for the production of Self-Compacting Concrete (SCC)”. “This research paper aimed at developing an eco-friendly and workable self-compacting concrete with the maximum amount of marble powder”. For this, a control mix and four other mixes with varying amounts of marble powder as 5%, 10%, 15%, and 20% are prepared. These mixes are then tested for their fresh properties by slump flow, j-ring, and V-funnel at T 5minutes. Compressive strength is used to evaluate the hardened concrete. It was found that up to 15 % marble powder addition, the fresh and hardened properties of the concrete mix did not vary considerably. However, it was also found that with the addition of marble powder the compressive strength decreased.

The fact that self-compacting concrete (SCC) does not require any supplementary compaction to fill in every nook and corner of the structure without compromising with strength and durability makes it much more futuristic and desirable over conventional concrete. Present study highlights the behavioural changes in SCC for PQC applications at macro and micro levels with the incorporations of wollastonite micro-fiber; proposed to be used for restoration of deteriorated pavement quality concrete slab. Wollastonite micro-fiber was incorporated as part replacement of fine aggregates in proportions of 10–50% with an offset of 10%. Different properties of SCC mixes such as flow-ability, segregation resistance and filling ability were investigated in fresh state while mechanical properties including compressive strength, flexural strength and hardened density were studied in hardened states. The SCC mixes were also investigated for estimating effect of incorporating wollastonite micro-fiber in hydrated states of cement mortar. Inclusions of wollastonite micro-fiber in SCC enhanced the cohesiveness of the mix thereby improving the density and reducing its water absorption. SCC mixes with wollastonite micro-fiber showed higher flexural and comparable compressive strength parameters than those of conventional SCC mix. SCC mix with 30% wollastonite micro-fiber as a replacement of fine aggregates provides similar strength and better repair prospects as compared to conventional SCC or normal concrete mix.

Three-dimensional (3D) printing, also known as additive manufacturing, is a revolutionary technique, which recently has gained a growing interest in the field of civil engineering and the construction industry. Despite being in its infancy, 3D concrete printing is believed to reshape the future of the construction industry because it has the potential to significantly reduce both the cost and time of construction. For example, savings between 35 and 60% of the overall cost of construction can be achieved by using this technique due to the possibility of relinquishing the formwork. Moreover, this innovation would free up the architectural gesture by offering a wider possibility of shapes. However, key challenges should be addressed to make this technique commercially viable. The effect of mixture composition on the rheological properties of the printed concrete/mortar is vital and should be thoroughly investigated. This paper investigates the effect of using red mud, nanoclay, and natural fibers on the fresh and rheological properties of 3D-printed mortar. The rheological properties were evaluated using the penetrometer test, flow table test, and cylindrical slump test. The estimated yield stress values were then calculated based on the cylindrical slump test. Further, relationships between the tested parameters were established. The main findings of this study indicate that the use of an optimum dosage of a nanoclay was beneficial to attain the required cohesion, stability, and constructability of the printed mortar. The use of natural fibers reduced pulp flow by improving cohesion with a denser fiber network and reducing the cracks. With respect to red mud, it may be appropriate for printable mortar, but more testing is still required to optimize its use in a printable mixture. A printability box to define the suitability of mixtures for 3D printing was also established for these mixtures. [ABSTRACT FROM AUTHOR]

The objective of this research is to analyze the mechanical resistance to compression of conventional specimens and specimens with the addition of red ceramic and waste of ethylene poly terephthalate (PET). The methodology applied has been based on the experimental dosage method of the São Paulo State Technology Research Institute (IPT). Three traces have been made: rich trace (1:0.5:3.5:0.45), medium trace (1:0.8:4.2:0.54) and poor trace (1:1.1:4.9:0.65), with percentages 0%, 2%, 3% and 4% of recycled PET fiber mixed with the red ceramic waste. For concreting, the materials have been mixed in a concrete mixer with a capacity of 120 liters in 20 min for each trace. Like this, 36 specimens have been made in cylindrical form 10 cm in diameter and 20 cm in height, with ages of initially and 28 days, both before the breaking load test, spending the first 24 hours immersed in tanks with water, remaining until the day of its rupture, according to NBR 5738 (ABNT, 2008). Slump flow tests, T500 time tests, workability have been performed, as well as the mechanical tests of Compressive strength and Compressive strength/density. PET fiber/red ceramic has offered greater workability than conventional concrete aggregate, using the same w/c ratio. A 4-12% reduction in density has been achieved with the PET fiber/red ceramic compared to the conventional aggregate concrete. With the 4% PET fiber/red ceramic replacing conventional aggregates at a 0.45 a/c ratio, compressive strength of 37.56 MPa has been obtained, the highest among all traces. [ABSTRACT FROM AUTHOR]

Ultrafine tailings (UFT) characterized by the average particle size <19 μm are increasingly discharged from mining process. Due to the large specific surface area and high content of clay components for UFT, cemented ultrafine tailings backfill (CUFTB) usually exhibits the poor flowability and strength, which poses an obstacle to the practical application in mine filling. Therefore, this study aims to explore the feasibility of modifying CUFTB through incorporating cold-bonded tailings lightweight aggregates (CBTLWAs). A series of mini slump cone, unconfined compressive strength (UCS), digital image correlation (DIC), microanalysis tests were conducted to investigate the effects of CBTLWAs with uniform particle sizes (d =2, 4, 6, 8 mm) and full particle gradations (Talbot coefficients n =0.2, 0.4, 0.6, 0.8) on slump flow (SF), UCS, microstructure, and deformation behaviour of CUFTB. Results indicate that adding CBTLWAs as a partial substitute for UFT effectively improves the flowability and strength of CUFTB. With CBTLWAs dosage increases from 15 to 40 wt%, the SF values increase steadily, and the highest values are observed when d =2 mm and n =0.6. After curing of 3–180 days, the UCS values exceed 1.0 MPa, and the highest values are found in CUFTB when d =8 mm and n =0.4 or 0.6. There is an optimal dosage (20–30 wt%) for CBTLWAs depending on the particle sizes and gradations. Hydration reactions of IWRs-based binder generated C-S-H, calcite, and ettringite. CBTLWAs bonded with CUFTB matrix to form a skeleton structure, hindering the propagation of cracks and enhancing the ductility. CUFTB incorporating CBTLWAs exhibits a hybrid mode of shear and tensile failure. The utilization of CBTLWAs shows a promising solution to address the challenges associated with performance modification for CUFTB. This provides a more environmentally responsible approach to solid wastes management and contributes to the sustainability of mining industry. [Display omitted] • Lightweight aggregates are used to modify flowability and strength of CUFTB. • Effect of uniform size and particle size gradation of CBTLWA are investigated. • There are optimal particle size, gradation, and dosage for performance improvement. • CUFTB containing CBTLWA show the hybrid mode of shear and tensile failure. [ABSTRACT FROM AUTHOR]

The aim of this research is to provide a quantitative method for evaluating concrete segregation. Because of various conditions of concrete materials, mix proportions, and delivery, concrete can be segregated. The acquisition inspection executed in construction field for supplied ready-mixed concrete is an important quality control process for concrete. Among the inspections conducted at the project site, segregation of concrete mixture should be evaluated before placing the concrete mixture, currently a qualitative inspection on concrete segregation was conducted. For a normal concrete mixture with slumping behavior, shear slump or collapse slump often occur as an indication of segregation. The suggested evaluation index of segregation for normal concrete (EISN) was induced from the shape of the concrete slumping: relation between the maximum distance of flow and the minimum distance of flow. To evaluate the feasibility of EISN, two different concrete mixture conditions were tested. The recommended EISN parameter of segregation is 1.09 using the three grades of concrete quality. This new quantitative method of evaluating segregation of the concrete mixture is expected to contribute to a more efficient quality control in concrete construction. [ABSTRACT FROM AUTHOR]