Concrete Mixture Proportions Research Articles

The Application of X-Ray Fluorescence to Assess Proportions of Fresh Concrete

Any transportation infrastructure system is concerned with durability and performance issues. The proportioning and uniformity control of concrete mixtures are critical factors that directly affect the longevity and performance of concrete pavements. Currently, the only means available to monitor mix proportions of any batch are to track batch tickets created at the batch plant. This does not take into account potential errors in loading materials into storage silos, calibration errors, and addition of water after dispatch. Therefore, there is a need for a rapid, cost-effective, and reliable field test that estimates the proportions of as delivered concrete mixtures. In addition, performance based specifications will be more easily implemented if there were a way to readily demonstrate whether any given batch is similar to the proportions already accepted based on laboratory performance testing. This paper describes a preliminary investigation into the potential use of a portable x-ray fluorescence (XRF) technique to assess the proportions of concrete mixtures as they are delivered. Tests were conducted on the raw materials, paste and mortar samples using a portable XRF device. There is a reasonable correlation between the actual and calculated mix proportions of the paste samples, but data on mortar samples was less reliable.

Reverse design for mixture proportions of recycled brick aggregate concrete using machine learning-based meta-heuristic algorithm: A multi-objective driven study

Construction and Demolition Wastes (CDW) have a significant impact on global waste streams. Brick waste stands out as a prominent type of CDW, and numerous studies have explored its recycling for the creation of environmentally-friendly concrete. Reverse design of recycled brick aggregate concrete (RBAC) mixture proportion is presented in this paper with a focus on four key objectives, that is: compressive strength, cost, and environmental elements (i.e., energy consumption and carbon emission). Based on compiled experimental datasets of 374 samples, the back propagation neural network (BP), random forest (RF), and four meta-heuristic algorithm optimization models were constructed to achieve the desired compressive strength objective. In all machine learning (ML) methods, the compressive strength of RBAC can be predicted with high accuracy, with the SSA-BP (optimized back propagation neural network model using the sparrow search algorithm) model achieving superior results (i.e., NSE=0.91, RPD=3.2). The SSA-BP is therefore used as the objective function for compressive strength. The economic objective is primarily influenced by material costs, and the objective functions of energy consumption and carbon emission are determined by various aspects of production, transportation, and their mixing processes. In order to obtain the optimal RBAC design, the Non-Dominated Sorting Genetic Algorithm (NSGA-III) was implemented considering imperative constraints. Results indicate that cement amount and recycled brick aggregate (RBA)-to-natural aggregate proportion have a positive impact on the compressive strength. The suggested design framework allows for the creation of RBAC composite designs with varying levels of RBA substitution rates and strength targets, providing valuable guidance for tackling the CDW challenge and optimizing RBA usage.

Optimizing the Carbon Footprint of Performance-Engineered Concrete Mixtures

This paper describes a process to optimize concrete mixture proportions to meet specific performance criteria through the minimization of a user-defined objective function that considers material cost and CO2 cost. The performance criteria impose constraints in the form of required mechanical properties (e.g., compressive strength), microstructural parameters (e.g., porosity or calcium hydroxide content), transport properties (i.e., the formation factor), and durability predictions (e.g., time to corrosion initiation or time to critical saturation). A thermodynamics-based model is used to predict reaction products and these results are used to predict performance. A feasible region of mixture proportions satisfying these constraints is identified after varying the water-to-binder ratios, cement replacement levels using limestone or supplementary cementitious materials, and air contents. The objective function is minimized to identify the optimal mixture proportions that meet all constraints while minimizing the material cost and CO2 footprint.

Study on the use of different machine learning techniques for prediction of concrete properties from their mixture proportions with their deterministic and robust optimisation

The construction industry relies so heavily on concrete that it's crucial to precisely forecast and optimize the strength and workability of concrete mixtures, while reducing costs as much as possible. For this objective, this study tries to predict and optimize the compressive strength and workability (slump) of concrete by using deterministic and robust optimization approaches, so as to determine the optimum concrete mixture proportions, while minimizing cost. Specifically, strength and slump were predicted based on concrete mixture proportions with five different machine learning techniques—support vector machine (SVM), artificial neural network (ANN), fuzzy inference system (FIS), adaptive fuzzy inference system (ANIS), and genetic expression programming (GEP), based on a dataset comprising two hundred concrete mixtures, which has various levels of key ingredients, including cement, water, fine aggregate, coarse aggregate, and size of coarse aggregate, along with their associated measures of strength and workability. These ingredients were used as input parameters, while compressive strength and slump (representing workability) served as output parameters for each mix proportion. Experimental investigations were conducted on fifteen distinct concrete mixes to validate the performance of the five networks, finding that ANFIS can yield the best results both for training and validation. This study provides valuable insights for predicting concrete properties and optimizing concrete mixture proportions, thus helping to maximize strength and workability while minimizing costs.

Multi-objective optimization design of recycled aggregate concrete mixture proportions based on machine learning and NSGA-II algorithm

This paper employs Support Vector Regression (SVR), Random Forest Regression (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) algorithms to establish the compressive strength prediction models for Recycled Aggregate Concrete (RAC) and analyze the influence of ten inputs on RAC compressive strength. Combined with the best prediction model, the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-II) is applied for multi-objective optimization of mixture proportions in RAC addressing cost, carbon emissions, and compressive strength as key objectives. The results demonstrate that the RAC compressive strength prediction model using the GB algorithm exhibits the highest accuracy, with the R2 value of 0.99 for the training set and 0.85 for the testing set. Feature importance and SHAP analysis reveal that cement and water contents are the primary factors affecting the compressive strength of RAC. Among the three mineral admixtures including Silica Fume (SF), Fly Ash (FA), and Ground Granulated Blast Furnace Slag (GGBFS), SF exhibits superior improvement in the compressive strength of RAC compared to FA and GGBFS. Partial dependency plot analysis indicates that concrete strength remains unaffected by recycled coarse aggregate content within the range of 0 to 300 kg/m3. Pareto fronts of a tri-objective mixture optimization problem for RAC are successfully obtained through the GB model combined with the NSGA-II algorithm, which can guide the optimization of RAC preparation.

Sustainable Structural Lightweight Concrete with Recycled Polyethylene Terephthalate Waste Aggregate

Plastic is a widely consumed material with a high decomposition time, occupying significant space in landfills and dumps. Thus, strategies to reuse plastic waste are imperative for environmental benefit. Plastic waste is a promising eco-friendly building material for cement-based composites due to its reduced specific gravity and thermal conductivity. However, this waste reduces the composites’ mechanical strength. This work aims to produce and evaluate lightweight concretes made with only lightweight aggregates and mostly recycled plastic aggregates. Initially, an optimized dosage approach for lightweight concrete is presented. The mixture proportion of the lightweight concrete was based on the performance of mortars with the complete replacement of natural aggregate by recycled polyethylene terephthalate (PET) aggregates. The PET aggregates showed irregular shapes, impairing workability and providing lightweight concretes with around 18% water absorption and 21% void index. However, the concretes presented significantly low-unit weight, approximately 1200 kg/m3. This work presented a structural lightweight concrete (ACI 213-R) using only lightweight aggregates and mostly plastic waste aggregate, with a compressive strength of up to 17.6 MPa, a unit weight of 1282 kg/m3, and an efficiency factor of 12.3 MPa·cm3/g. The study shows that with an optimum dosage, reusing plastic waste in concrete is a viable alternative contributing to environmental sustainability.

Prediction of concrete abrasion depth and computational design optimization of concrete mixtures

The failure of concrete for hydraulic structures due to the excessive abrasion depth has caused great economic losses. Despite the tremendous work that has been done, the durability design of hydraulic concrete structures concerning abrasion damage is still a difficult task, especially with a long service life aiming at economic benefit and environmental sustainability. In this paper, machine learning methodologies including random forests (RFs) and artificial neural networks (ANNs), are used to establish prediction models based on concrete mixture proportions, curing age, and hydraulic conditions. Furthermore, RF models are coupled with multiple objective particle swarm optimization (MOPSO) algorithms to perform computational design optimization of concrete mixtures. The optimal solutions are generated based on 5000 randomly generated mixture proportions, among which three optimal designs of concrete mixtures are selected to guide field applications on account of concrete durability requirements and economic benefits.

Machine learning-based approach for optimizing mixture proportion of recycled plastic aggregate concrete considering compressive strength, dry density, and production cost

The extensive use of plastics has resulted in significant environmental challenges due to non-biodegradable waste. Researchers explore the integration of plastic waste into construction materials, specifically as aggregates in concrete. As the inclusion of plastic aggregates can influence physical and mechanical properties of concrete, a comprehensive analysis of key concrete properties affected by plastic aggregate content should be conducted. Considering the intricate relationship between mixture proportion and properties of plastic aggregate concrete, a random forest (RF) model, a machine learning algorithm, is employed to predict compressive strength and dry density. The RF model, trained on an extensive dataset from diverse sources, demonstrates notable accuracy with low mean absolute percentage errors for compressive strength (7.2 %) and dry density (2.8 %). Moreover, the RF model provides the importance of plastic aggregate content in determining the concrete properties. Subsequently, the trained RF model is applied to optimize mixture proportions, securing compressive strengths of 20 and 30 MPa. The optimization process takes into account factors such as plastic aggregate content, production cost, dry density, and compressive strength. In comparison to experimental results from previous studies, the optimized mixtures exhibit a high strength-to-density ratio, indicating superior structural efficiency.

Anticorrosive Efficiency of the AISI 316 SS in Sustainable Ecological Concrete Manufactured with SCBA-SF Exposed to Magnesium Sulphate

In this research, it was evaluated the anticorrosive efficiency of AISI 316 SS embedded in Sustainable Ecological Concrete (SEC) manufactured with partial substitutions of Portland Cement by combinations of SCBA and SF in 10%, 20%, and 30%. For the electrochemical evaluation, the Sustainable Ecological Concretes (SEC) were exposed to solution at 3.5% of MgSO4, these aggressive ions are found in soils, industrial or marine environments and that interact with the civil works that are built in these places. The dosage or proportioning of the Sustainable Ecological Concrete (SEC) mixtures was carried out as indicated by ACI 211.1. The anticorrosive efficiency of the AISI 316 SS was evaluated through the tests of the potential of corrosion (Ecorr) and corrosion rate (Icorr) during a period of 180 days of exposition to the aggressive medium. The values of Ecorr indicate in the AISI 316 SS a 10% of corrosion risk and uncertainty at the end of monitoring, according to the norm ASTM C-876-15, in all the mixtures, but the values of Icorr in the specimens manufactured with SEC indicate resistance to sulfate corrosion more than 10 times compared to conventional concrete and AISI 1018 steel.

Bina Betonunun Karışım Oranı için Kaba-AHP ve MOORA Tabanlı Taguchi Optimizasyonu

In the construction industry, materials that are used for producing construction concrete and their mixture proportions are an important decision-making and optimization problem for concrete manufacturers. Amounts (mixture proportions) of the materials, which are used in order to obtain a mixture of high strength and functional concrete, can positively or negatively affect performance criteria which are individually determined for each customer. Furthermore, the effect and significance of each performance criterion on the concrete may differ from each other. This paper proposes the Rough-AHP approach to determine the significance levels and weights of the performance criteria decided according to the customer demands and the MOORA-based Taguchi approach to calculate the optimal mixture proportion of the concrete for the customer demand. It is aimed to determine the best design of the experiment to minimize the experiment duration and cost rather than to perform all experimental designs.

Multi-objective optimization of concrete mix design based on machine learning

This study proposes a multi-objective optimization (MOO) framework for optimizing concrete mixture proportions. Advanced methods such as K-fold cross-validation, Bayesian hyperparameter optimization, regression feature elimination and C-TAEA algorithm are used to develop suitable ML models. The results show that compared to the commonly used ML algorithms, the application of 5-fold cross-validation, hyperparameter and regression feature elimination significantly improves the prediction accuracy of compressive strength to 0.993 and binder intensity to 0.980. Moreover, quantitative correlations between different mix design parameters and concrete performances can be clearly observed. By using the proposed MOO model with C-TAEA algorithm, the errors between the predicted and tested values are only around 4%, exhibiting high prediction accuracy and the ability to solve multi-objective mixture optimization problems.

Innovative image analysis-based methods for the estimation of conventional concrete mixture proportions from hardened concrete

The importance of concrete mix design is not hidden from anyone. Therefore, extracting mixture proportions from hardened concrete can be considered as a brilliant tool for various purposes such as controlling the accuracy of construction and evaluating the reasons for the inappropriate performance of concrete. For this purpose, an innovative and comprehensive method was developed in which images of the macrostructure and microstructure of hardened concrete were utilized. At first, the volume ratios of aggregates, cement paste, and air voids were estimated using the results of analyzing microstructure images containing images of black and white treated concrete cross sections scanned with a resolution of 2400 dpi and Backscattered electron microscopy (BSE) images at 500 × magnification. Subsequently, with regard to the aggregates volume ratio and images of the macrostructure of concrete, scanned with a resolution of 600 dpi, volume ratios of coarser aggregates (retained on the 0.3 mm sieve) and finer aggregates (passing the 0.3 mm sieve) were calculated. For determining aggregate size distribution, the sieve simulation and optimizing rules (SSOR) method was developed. Water to cement ratio was estimated by having volume ratios of cement paste components extracted from processing BSE images. Also, cement content was estimated using a proposed equation by having volume ratios of cement paste and water to cement ratio. Thus, using the proposed methods in this study, volume ratios of aggregates, cement paste, and air voids and aggregate size distribution, water to cement ratio, cement content, and water content can be estimated for an unknown hardened concrete. The statistical comparison between the actual mixture proportions and the results obtained from performing the proposed methods on samples made with three different aggregate size distributions and a range of cement content (300–500 kg/m3), and water to cement ratio (0.32–0.6) highlights the brilliant performance of the introduced methods.

Use of operational research techniques for concrete mix design: A systematic review

Traditional methods for designing concrete mixtures provide good results; however, they do not guarantee the optimum composition. Consequently, applying operational research techniques is motivated by an increasing need for designers to proportion the concrete's raw materials that satisfy the concrete performance requirements such as mechanical properties, chemical properties, workability, sustainability, and cost. For this reason, many authors have been looking for mathematical programming and machine learning solutions to predict concrete mix properties and optimise concrete mixtures. Therefore, a comprehensive review of operational research techniques concerning the design and proportioning of concrete mixtures and a classification framework are presented herein.

Prediction of strength characteristics of cement composite using artificial neural network

The design of concrete is still time-consuming, reliant on experience, and unpredictable. Concrete is a heterogeneous, complex substance comprised of cement, water, fine and coarse aggregate, as well as chemical and mineral admixtures. Due to the unpredictability of concrete materials such as cement, fine and coarse aggregates, the conventional concrete mixture proportion algorithms were trial and error, which wastes money, labor, and time. This might result in the erroneous computation of important variables such as the compressive and tensile strength of the concrete, necessitating the use of an Artificial Neural Network (ANN) in cement composite. ANN is inspired by the learning ability of the human brain, which is their highly interconnected and parallel nature, giving them the ability to learn from prior instances while simultaneously capturing unknown associations, making them a versatile modeling tool for concrete designs. The computed strengths would be more precise because ANN utilizes the preceding instances to produce excellent designs. This article focuses on the use of artificial neural networks as a computational tool for simulating complicated functional interactions among varied elements that impact the compressive strength of concrete as well as its behavioral characteristics. Using the capabilities of ANN, the design plot and the required corrective equations are developed to create a user-friendly approach for determining the strength of concrete, as well as its behavioral characteristics. ANN also gives a greater degree of accuracy than the currently available regression models and a reduction in the number of highly dispersed erroneous predictions. Since ANN is resilient and dependable, the results are 99.5% effective. Consequently, the review suggests that ANN may be utilized efficiently in the mix proportioning of concrete to reach the desired strength with little cost and labor waste.

Similarities and probability distributions of chloride convection zone depth in concrete exposed to cyclic drying-wetting environments

A methodology using parabolic fitting was proposed to regress three points in chloride profile and determine the depth of convection zone in concrete. Based on exposure tests, the influences of environmental exposure, exposure time and mixture proportion of concrete on probability distributions and statistical parameters of convection depth were studied. The similarity in probability distribution of convection depth was analyzed by the Kullback-Leibler divergence. It was found that the mean, coefficient of variation and distribution type of convection depth are significantly influenced by environmental condition. Convection depth in natural environment mostly follows the Gumbel (Type I) distribution, while it follows the Beta distribution in simulated environment. The probability density functions of convection depth in simulated environment at early exposure have high similarities with those in natural environment at later exposure.

Natural and recycled aggregate concrete containing rice husk ash as replacement of cement: Mechanical properties, microstructure, strength model and statistical analysis

This study investigated the effects of the replacement ratio of natural aggregates (NAs) by recycled aggregate (RAs), the replacement ratio of cement by rice husk ash (RHA), and their interaction on compressive properties of recycled aggregate concrete (RAC). The RAs used consisted of 37.5 wt% recycled brick aggregates and 62.5 wt% recycled concrete aggregates from construction and demolition waste. To optimise the properties of RAC mixture, a full factorial design of experiment was applied in designing the concrete mixture proportion. The two factors considered were: the replacement ratio (wt.%) of RA with five levels (0%, 30%, 50%, 70% and 100%) and the replacement ratio (wt.%) of RHA with four levels (0%, 10%, 20% and 30%). Axial compression tests based on the full factorial experiment were conducted on cubic and prismatic samples at 28 days to evaluate their compressive properties. In addition, further cubic concrete groups were also tested in axial compression at different concrete ages (i.e., 3, 7, 28, 56 and 91 days) to understand the effect of concrete age on the compressive strength. Statistical analyses including ANOVA, post-hoc pairwise comparisons and effect size (Cohen's d) computation were performed to evaluate the experimental results. The results indicated that the compressive strength of concrete at 28 days was significantly affected by the replacement ratio of RA and RHA (both p-values <0.0001), as well as their interaction (p-value = 0.0001). For the four different replacement ratios (0%, 10%, 20% and 30%) of RHA considered in this study, the optimised ratio was 10%, which resulted in 0.4% and 4.9% increases in compressive strength of NAC and RAC (RA replacement ratio = 100%), respectively. RHA contributed more to the strength improvement of RAC than to that of NAC, both at 28 days and at 91 days. A new compressive strength model was developed for concrete containing RAs and RHA (or for other supplementary cementitious materials such as fly ash, silica fume, metakaolin and ground granulated blast slag). The validation with the experimental results from the literature showed a good accuracy of this model for concrete strength prediction, with 4.45% mean absolute percentage error and 2.3 root mean square error.

Effect of Aggregate Size and Compaction on the Strength and Hydraulic Properties of Pervious Concrete

Pervious concrete is one of the emerging sustainable materials that has recently gained the attention of many researchers. The importance of pervious concrete mainly depends on its application and on a modern integrated approach in which it is employed to reduce the effects of flooding. The main goal of this experimental analysis is to study the significance of aggregate size and the degree of compaction on the mechanical and hydraulic properties of pervious concrete. Eleven concrete mixture proportions were investigated by controlling the constituents with different aggregate fractions. The important variables considered were the aggregate sizes, viz., 0/4 mm, 4/8 mm, and 8/16 mm, with four different degrees of compaction. The porosity of the concrete structure was obtained by the partial filling of the voids in the aggregates with cement paste. The ingredients of the pervious concrete were also varied to study their significance and to evaluate the predominant factor that controls the mechanical and hydraulic properties based on the test results. Tests were conducted to determine properties such as compacting factor, compressive strength, splitting tensile strength, abrasion resistance, porosity, and hydraulic conductivity. The study revealed that the degree of compaction was one of the critical factors governing the strength and hydraulic properties of the pervious concrete; the maximum strength and minimum hydraulic conductivity were achieved with a higher degree of compaction. The test results imply that the cement content is the predominant factor determining the fresh and tensile properties of the pervious concrete, rather than the size of the aggregates used. In addition, the results also illustrated that the highly compacted pervious concrete samples made with 4/8 mm aggregates exhibited improved abrasion resistance and strength properties, but slightly reduced hydraulic conductivity, despite the designed porosity.

Review of supplementary cementitious materials with implications for age-dependent concrete properties affecting precast concrete

This paper presents a comprehensive review of existing research examining the effects of supplementary cementitious materials on age-dependent concrete properties with the most profound implications for precast concrete production. The review covers the physical and chemical properties of selected types of supplementary cementitious materials, concrete mixture proportions, concrete curing methods, testing procedures, and test results related to the fresh properties and strengths of a range of concrete mixtures. Although the use of supplementary cementitious materials in precast concrete products is common, the detailed information provided in this paper may facilitate more widespread use of these materials to enhance concrete properties and comply with sustainability initiatives.

Desorption behavior and mechanism of chloride ions in fresh concrete mixtures under electromigration

Detecting chloride ions in concrete mixtures is the final checkpoint to assess whether the chloride-ion content of hardened concrete is qualified. This paper devises a method to rapidly detect the chloride-ion content of fresh concrete mixtures based on electromigration and then compares it with traditional detection methods. The test results show that the electrochemical method for analyzing the chloride-ion content of concrete is accurate and suitable for various concrete mixture proportions. Moreover, parameter sensitivity analysis demonstrates the dissolution behavior of chloride ions in the concrete mixture when different detection methods are applied. Further, thermogravimetry–differential thermogravimetry (TG–DTG) analysis and scanning electron microscopy–energy-dispersive spectrometry (SEM–EDS) are used to study the desorption mechanism of chloride ions under the action of an electric field at a micro-level. The results of this paper provide technical means for rapidly detecting the chloride-ion content of fresh concrete mixtures, which is vital to ensuring the durability of concrete structures.

Water Transport through Cracked Concrete Structures—Effect of Mixture Proportion on Separating Crack Geometry and Permeability

The increase in fluid transport due to separating cracks can lead to significant deterioration in the durability of reinforced concrete structures. Besides reinforcement and stress state, concrete mixture proportion has a significant effect on crack geometry. In this study, we investigated concrete mixtures with different aggregate size and shape, aggregate gradation, cement type and water-to-cement ratio with regard to crack geometry and resulting water permeation. Besides surface-crack width and length, we determined inner-crack width variation over depth and tortuosity by X-ray micro-computed tomography. Furthermore, we conducted permeation tests for each specimen. Among the mixture components tested, aggregates have the strongest effect on crack geometry and flow rate. Increasing aggregate size results in increasing tortuosity and decreasing flow rate. Furthermore, the replacement of round with angular aggregates results in slightly higher flow rates for a given crack width.