Design Of High-performance Concrete Research Articles

Accurate compressive strength prediction using machine learning algorithms and optimization techniques

Numerous components' complex interrelationships and interconnectedness present a formidable obstacle in developing mix designs for high-performance concrete (HPC) formulation. The effectiveness of machine learning (ML) algorithms in resolving this paradox has been illustrated. However, they are classified as opaque black-box models due to the lack of a discernible correlation between blend ratios and compressive durability. The present study proposes a semi-empirical methodology that integrates various techniques, including non-dimensionalization and optimization, to overcome this constraint. The methodology exhibits a noteworthy level of accuracy when forecasting compressive strength (CS) across a spectrum of divergent datasets, thus evincing its extensive and all-encompassing efficacy. Moreover, the precise relationship that semi-empirical equations convey is of great significance to practitioners and researchers in this field, especially with respect to their predictive abilities. The determination of CS in concrete is a critical facet of the design of HPC. An exhaustive comprehension of the intricate interplay between manifold factors is requisite to attain an ideal blend proportion. The study’s findings indicate that RF can accurately predict CS. Moreover, the combination of optimization algorithms significantly enhances the model’s effectiveness. Among the three Optimization Algorithms under consideration, the COA optimizer has exhibited superior performance in augmenting the accuracy and precision of the RF prediction model for CS. As a result, RFCO obtained the more suitable value of R2 and RMSE obtained 0.998 and 0.88, alternatively.

Strength property estimation of high-performance concrete with novel admixtures using optimal regression-based approaches

Concrete is known as one of the most important materials in the world. Concrete composites consisting of cement, water, aggregates, and some additives are used to improve the properties of concrete. These concrete have a certain compressive strength that can be increased depending on the type of concrete. In line with these ideas, high-performance concrete (HPC) has been produced, which can have high compressive strength by adding materials such as fly ash, silica fume, etc. This type of concrete is used in bridges, dams, and special constructions. However, obtaining the mixture design of HPC is problematic and complex, for this reason, the machine learning methods can make it easy to achieve the output by saving time and energy. This study has used support vector regression (SVR) to predict the compressive strength of HPC. Moreover, this study provided two meta-heuristic algorithms for obtaining suitable and optimized results, which are contained the artificial hummingbird algorithm (AHA) and Sine Cosine Algorithm (SCA). The model by coupling with algorithms created the hybrid method in the framework of SVR-AHA and SVR-SCA. Furthermore, some criteria indicators have been used for determining the most desirable hybrid model, which is included coefficient of correlation (R2), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and weight absolute percentage error (WAPE). As a result, the AHA algorithm could have a more satisfying association model with the SVR model, and the results were RMSE = 2.00 (MPa), R2 = 98.59%, MAE = 0.717 (MPa), MAPE = 1.22 (MPa), and WAPE = 0.114 (MPa).

Effect of binder strengthening using micro-silica on mechanical and absorption characteristics of HPC reinforced with reclaimed jute fibres

The incorporation of dispersed fibres in the binder matrix is proven to be beneficial to the tensile performance of high-performance concrete (HPC). However, the insertion of new/virgin fibres in the ‘concrete matrix’ significantly adds to the final cost and embodied carbon of HPC. Therefore, the environment-friendly design of fibre-reinforced HPC remains the main challenge. In this study, the influence of different volume fractions (Vol.) of ‘jute fibre’ (JF) was examined on the engineering performance of HPC. The effect of micro-silica/silica fume (SF) inclusion as a secondary binder was also analysed on the performance of JF-reinforced HPC. To investigate the mechanical performance of designed mixes, compression, flexure, and splitting-tensile tests were conducted at 28 and 91 days. Non-destructive durability performance indicators, such as ‘water-absorption (WA)’, ‘rapid chloride-ion permeability (RCIP)’, ‘electrical-resistivity (ER)’, and ‘ultrasonic-pulse velocity (UPV)’ were also examined. It was found that the utilisation ratio/efficiency of JF addition improved with the ageing of samples. Moreover, the inclusion of SF proved to be beneficial in the strength gain due to JF addition. The combined use of 0.3% vol. of JF and SF improved the ‘compressive strength (CS)’ by 26–30%. The inclusion of 0.3% vol. of JF improved the ‘splitting-tensile strength (STS)’ of plain HPC by 43% and 23% with and without the SF inclusion, respectively. The JF incorporation showed a minor decrease in the imperviousness of HPC. SF incorporation helped minimise the damaging effect of JF incorporation on the ER, WA resistance, and UPV value of HPC. Owing to the addition of SF, the WA capacity of JF-reinforced mixes was decreased by up to 30% at 28 days and up to 50% at 91 days. While the RCIP values of JF-reinforced mixes were dropped by 40–60% (depending on the JF content) owing to the superior pozzolanic and filler effect of SF.

Splitting tensile strength prediction of sustainable high-performance concrete using machine learning techniques.

Splitting tensile strength is one of the commonly used mechanical properties in the design of high-performance concrete (HPC) structures. To achieve accurate prediction of splitting tensile strength of HPC, two optimized machine learning models GA-ANN and GS-SVR were employed to predict the target tensile strength on 714 sets of sample data with 12 features of HPC as input variables. The appropriate initial weights and thresholds of the ANN model and hyper-parameters of the SVR model were first obtained by genetic algorithm and grid search, respectively. Then, the optimized models were used to train and test the dataset, and the performance of the models was evaluated and compared using evaluation metrics. The results showed that the prediction accuracy of the optimized GA-ANN model was higher than that of the pre-optimized ANN model, but still inferior to that of the GS-SVR model. Compared with the known machine learning models in the literature, the GS-SVR model proposed in this paper has smaller prediction error, higher prediction accuracy, and better performance. The strong generalization ability of the GS-SVR model to unseen test data indicates its great potential in predicting the tensile strength and is recommended as an alternative method for splitting tensile strength prediction of HPC. Additionally, a parametric analysis based on the Shapley additive explanations approach (SHAP) was proposed to investigate the importance and contribution of these input variables on the output splitting tensile strength.

Fusion-Learning-Based Optimization: A Modified Metaheuristic Method for Lightweight High-Performance Concrete Design

In order to build high-quality concrete, it is imperative to know the raw materials in advance. It is possible to accurately predict the quality of concrete and the amount of raw materials used using machine learning-enhanced methods. An automated process based on machine learning strategies is proposed in this paper for predicting the compressive strength of concrete. Fusion-learning-based optimization is used in the proposed approach to generate a strong learner by pooling support vector regression models. The SVR technique proposes an optimization method for finding the kernel radial basis function (RBF) parameters based on improving the innovative gunner algorithm (AIG). As a result of AIG's diverse solutions, local optima are effectively avoided. Therefore, the novelty of our research is that, in solving the uncertainty of predicted outputs based on integrated models, we use fusion-learning-based optimization to improve regression discrimination. We also collected a standard dataset to analyze the proposed algorithm, and subsequently, the dataset was designed from concrete laboratory tests on 244 samples, seven features, and three outputs. Different regression intensities are determined by correlation analysis of responses. Regression fusion is sufficiently accurate to estimate the number of desired outcomes examined based on the appropriate input data sample. The best quality concrete can be achieved with an error rate of less than 5%.

Mix design of high performance concrete with different mineral additions

Purpose The purpose of this paper is to present the modeling of statistical variation of experimental data using the design of experiments method to optimize the formulation of a high performance concrete (HPC) using materials that are locally available in Algeria. For this, two mineral additions (natural pozzolana and limestone filler [LF]) were used. Both additions are added by substitution of cement up to 25%. To better appreciate the effect of replacing a part of cement by natural pozzolana and LF and to optimize their combined effect on the characteristics of HPC, an effective analytical method is therefore needed to reach the required objective. Design/methodology/approach The experimental part of the study consisted of substituting a portion of cement by various proportions of these additions to assess their effects on the physico-mechanical characteristics of HPC. A mixture design with three factors and five levels was carried out. The JMP7 software was used to provide mathematical models for the statistical variation of measured values and to perform a statistical analysis. These models made it possible to show the contribution of the three factors and their interactions in the variation of the response. Findings The mixture design approach made it possible to visualize the influence of LF and pozzolanic filler (PF) on the physico-mechanical characteristics of HPC, the developed models present good correlation coefficients (R2 = 0.82) for all studied responses. The obtained results indicated that it is quite possible to substitute a part of cement with LF and PF in the formulation of a HPC. Thanks to the complementary effect between the two additions, the workability could be improved and the strengths drop could be avoided in the short, medium and long term. The optimization of mixture design factors based on the mathematical models was carried out to select the appropriate factors combinations; a good agreement between the experimental results and the predicted results was obtained. Originality/value The coefficient of PF in Cs28 model is closer to that of LF than in Cs7 model, thanks to the complementary effect between LF and PF at the age of 28 days. It was found that the optimal HPC14 concrete (10%LF–5%PF) provides the best compromise between the three responses. It is also worth noting that the use of these two local materials can reduce the manufacturing costs of HPC and reduce carbon dioxide emissions into the atmosphere. This can be an important economic and environmental alternative.

A Bayesian machine learning approach for inverse prediction of high-performance concrete ingredients with targeted performance

High-performance concrete (HPC) plays an important role in improving the sustainability and reliability of buildings and infrastructures. Machine learning predictive models have been used for predicting concrete performance from ingredients, however it remains a challenge to achieve inverse prediction of ingredients from targeted performances. This study proposes an in-house coded informatics-based materials analysis framework to enable computational design of HPC with targeted strength performance. The Gaussian processes (GP) emulator is used to construct the surrogate predictive model based-on 453 experimental measurements. The validity of the GP emulator is assessed using the leave-one-out cross-validation (LOO-CV) and also a separate validation dataset. The variance-based global sensitivity analysis, Sobol indices, is applied to understand the impact of physical ingredients on the HPC performances. The results suggest that the trained GP emulator can provide sufficiently accurate and reliable predictions, as well as reflect the real-world physicochemical nature of HPC materials. The inverse material design is achieved by the Bayesian inference method with a Markov chain Monte Carlo stochastic sampling method, the Metropolis-Hastings (MH) algorithm. Combining with the Bayesian inference method, the proposed design framework can infer a list of potential HPC formulae of a targeted performance, each evaluated by the likelihood of resulting in the targeted strength. The data-driven material analysis and design framework proposed in this study provides a novel approach to achieve performance-based design of HPC, with the potential to maximise resource efficiency and reduce economical cost. The methodology presented in this study can also be extended to be applied to a wide range of construction materials, targeting difference service performances including durability.

High-Performance Concretes Intended for Deep Foundations of Constructions

The term high-performance concrete (HPC) can be used to classify any concrete mixture that possesses some added value. One such category of concrete is concrete mixtures used in pile foundation, which must only allow a low degree of water separation (bleeding). The basic components of production of these concretes with high utility properties include the usability of active and passive admixtures and their mutual combinations. As part of this research, analysis of aspects affecting water separation from fresh concrete mixture, including dosing, and the type of individual raw materials, was performed. This article aims to utilise these results to analyse the impact of individual components and to provide a comprehensive theory for how to optimise the design of low-bleeding HPC based on the appropriate selection and mixing ratio of cement and admixture. A set of different raw materials and the effect of their mutual mixing ratios on bleeding and other mechanical and physical parameters of produced mixtures were verified. The results of this experiment demonstrate the clear relationship between the amount of binders up to size 0.25 mm and amount of water that separates under pressure. Very important aspect is morphology of grains used binders. These knowledge of realized experiment could be easily applied into the practise, same as designed methodology of usage combination of active and passive admixtures to create a maximum cement matrix compaction.

Preparation and Verification of Properties of Alkali-Activated Composite

The paper deals to create alkali activated composite based on the recipe of high-strength concrete. The paper includes the review of conditions for high-strength concrete proposing and preparation. Principles of design of high performance concrete based on Portland cement as a binder were described. The experiment was focused on the strength investigation of composites with different ratios of cement to slag, and also the effect of used activator on strength parameters was investigated.

Strength and sulphate resistance of high performance concrete containing different fineness of Palm oil fuel ash

This research aims to study the effect of different fineness of Treated Palm oil fuel ash (T-POFA) on the strength and sulphate resistance of high-performance concrete (HPC). For this research, besides the control mix, two mixes with fineness of 45 μm and 150 μm of POFA were prepared with 20% replacement with ordinary Portland cement (OPC). For sulphate resistance, the concrete specimens were immersed in 5% sodium sulphate solution to investigate the sulfate resistance in accordance with appropriate ASTM standards. In addition to that, the higher fineness of T-POFA gives better resistance towards sulphate attack due to its pozzolanic reaction. From the tests that were carried out, the results showed that 20% of 45 μm T-POFA replacement in HPC concrete gives the lowest in compressive strength loss at 120 days compared with 150 μm replacement and control HPC concrete in sulphate attack. It is noticed that, the compressive strength of 45 μm T-POFA shows increment in compressive strength after 120 days of immersion due to the pozzolanic reactions that took place. This in turn gives extra layer of C-S-H to concrete which strengthened the bond. Therefore, the higher fineness of T-POFA gives better resistance towards sulphate attack. This result suggests that concrete design of high performance concrete (HPC) contains POFA 45 μm is better to be used as a cement replacement compared with POFA 150 μm and control concrete for sulphate resistance.

Design of high performance concrete with multiple performance requirements for #2 Dongting Lake Bridge

Proper mixture design of concrete is of great significance for the quality of the concrete materials and structures. For #2 Dongting Lake Bridge in China, several performance requirements were required for designed concrete mixtures, including compressive strength, drying shrinkage, cracking resistance, alkali-aggregate reaction, permeability, and carbonation resistance. In this study, a mixture design method of concrete with multiple performance requirements was used to design C55 concrete for the project. The relationships between cement-fly ash-slag ternary cementitious composition and different properties were established based on the seven-batch factorial design method. Based on the requirements for different properties, a composition range meeting all requirements could be obtained. The verification tests showed that the designed mixture was suitable for the construction of #2 Dongting Lake Bridge.

Packing and film thickness theories for the mix design of high-performance concrete

A high-performance concrete (HPC) is required to have superior performance in various aspects such as workability, strength, durability, dimensional stability, segregation stability, and passing ability. The mix design of HPC is rather complicated because the number of ingredients in HPC is usually more than those in conventional concrete and some of the required properties are conflicting with each other in the sense that improvement in one property would at the same time cause impairment of another property. However, there is still lack of understanding regarding how the various mix parameters should be optimised for achieving best overall performance. Most practitioners are still conducting mix design primarily through trial concrete mixing, which is laborious, ineffective, and often unable to timely respond to fluctuations in the properties of raw materials. To address these issues, the authors have been developing the packing and film thickness theories of concrete materials, in order to revamp the mix design philosophy of HPC in terms of the water film thickness (WFT), paste film thickness (PFT), and mortar film thickness (MFT) in the concrete. Based on the findings from an extensive experimental programme, suitable ranges of WFT, PFT, and MFT have been recommended.

Manufacture of High-Performance Concrete Made with Powdered Polyester Resin Waste and Carboxylic-Ester Based Superplasticizer

A high-performance concrete design is presented that incorporates powdered polyester resin waste as a fine aggregate material in partial substitution of sand, with additions of both a superplasticizer based on carboxylic ethers and silica fume. The potential application of the powdered polyester resin and its properties as an aggregate are both analysed as well as the mechanical behaviour, in both the fresh and the hardened states, of the concrete. Both the flexural and the compressive strength of the concrete were shown to depend on the amount of waste used in substitution of the sand. The results of the research show that the designed concrete reaches the resistances of a high performance concrete with values ​​above 50 MPa. On the other hand, the rest of its properties are equivalents, both in the fresh and hardened state, proportional to the amount of powdered polyester resin that is added. However, to achieve this, the use of superplasticizers additives and admixtures of silica fume is necessary.

Options for Improving the Workability of High Performance Concrete

A low water-cement ratio is one of the main characteristics of high performance concrete thanks to high strength of concrete is achieved. However, it leads to deterioration of the rheological properties and therefore it is necessary to use a plasticizing admixtures. Other options to influence workability are introduced in this article. There is studied the correct selection of cement, the temperature of water, the particle size distribution of aggregates (packing density) and the use of ground granulated blast furnace slag (GGBS). The performed experiments show a greater or lesser influence all studied options on the workability. Therefore this options is appropriate to keep in mind during mix design of high performance concrete.

Development and Mix Design of HPC and UHPFRC

Application of HPC (High performance concrete) is very popular and modern solution in current architecture. Higher mechanical and durability properties allow using of thin-walled cross-sections bringing savings of materials and internal space of buildings. This paper deals with development of HPC and UHPFRC (Ultra high performance fiber reinforced concrete) mix design and impact of composition to final mechanical properties. Mix design is focused first on the influence of various additives such as fly ash, silica fume and quartz flour and then to different dosage of steel fibers.

Multi-Physics Modeling of Fire-Induced Damage in High-Performance Concrete

The response of high-performance concrete (HPC) to rapid heating due to exposure to fire with air temperatures exceeding 700°C is analyzed. The analysis focuses on coupled thermal-mechanical-transport processes at the mesoscale in the first 10 minutes of exposure during which heating rates on the order of 10 °C/minute and temperature gradients on the order of 15 °C/cm are involved. The driving forces for damage considered are thermomechanical stresses and internal pore pressure resulting from the expansion of water within the material. The HPC is modeled as a two-phase composite consisting of a cementitious matrix and a population of quartz aggregate particles. Mesostructures with aggregate sizes of 400-1600 μm and aggregate volume fractions of 10-30% are considered. To capture the development of stresses and pore pressure, the cementitious matrix is modeled using a coupled thermal-mechanical-transport formulation and the aggregate is modeled using a thermal-mechanical formulation. Simulations show that the composition of the mesostructures significantly influences the time and spatial distribution of damage. Materials with smaller aggregate sizes and the lower effective permeability are found to exhibit more rapid property degradation. The time to failure and depth of thermal spall are quantified as functions of structural variables. This framework and the mesostructure-response relations obtained serves as a tool for the design of HPC that are more resistant to fire-induced damage.

Use of Stone Dust in the Design of High Performance Concrete

This research evaluated the suitability of stone dust in the design and production of High Perfor-mance Concrete (HPC). HPC mix was designed, tested, costed and a comparison of concrete classes used in the market (Class 25, 30 and 35) done using Cost Benefit Analysis (CBA). The cost benefit was analyzed using Internal Rate of Return (IRR) and Net Present Value (NPV). Laboratory tests established the properties concrete obtained from the design mix. Compressive strength, slump, and modulus of elasticity were tested and analyzed. Structural analysis using BS 8110 was done for a 10 storey office building to establish the structural member sizes. Members obtained from concrete Classes 25, 30, 35 and the new compressive strengths from HPC (Class 80) were obtained and compared. Analysis was done for structural members’ sizes and area freed as a result of de-signing with HPC as well as the steel reinforcement used. To justify the initial cost of HPC if ado- pted, the Cost Benefit Analysis (CBA) was used to estimate increased costs versus income resulting from increased let table space created. The minimum class of concrete used in design was limited to Class 25 N/mm2. The research shows that it is possible to manufacture high strength concrete using locally available stone dust. The stone dust sampled from Mlolongo quarries achieved a characteristic strength of 86.7 N/mm2 at a water cement ratio of 0.32. With the results structural analysis of a 10 storey office structures with columns spaced at 8 meters center to center was de-signed using the four classes and results compared. There was a reduction of columns from 1.2 m wide to 0.65 m wide (over 45%) when concrete class changes from Class 25 to Class 80 creating over 3% of the total space area per floor. Cost benefit analysis using Net Present Value (NPV) and Internal Rate of Return (IRR) presented business case for the use of HPC. With Class 80, the IRR was at 3% and NPV being 8% of the total initial investment. The steel reinforcement increased by 8.64% using Class 30, 11.68% using Class 35 and reduced by 8.37% at Class 80. Further analysis needs to be done to understand the trend of steel reinforcement keeping all the member sizes the same. In this study the member sizes were optimized based on the steel reinforcement and serviceability. This paper provides useful information to design Engineers and Architects and inform future design of multi storey structures.

Study on Application of Concrete Admixture Technology

Mix design of high performance concrete was determined based on the pumping simulation test. It was indicated by test and field application that the trimixture technique and the double mixture technique can improve the pump of concrete, reduce the dosage of cement and the hydration heat; adding expansion agent can prevent harmful cracks in the concrete due to shrinkage stress, improve the compactness and impermeability. The reasonable construction technologies were used in mixing, transportation, pouring, vibrating and curing of the concrete in order to ensure quality of the construction.

Multi-Objective Optimized Design of High-Performance Concrete Based on Matlab

Based on the Compressive strength, workability and raw material consumption of concrete, a mathematical model is established for mix proportion design of high performance concrete (hereinafter referred to as HPC) with chlorine ion’s diffusion coefficient and per cubic meter cost as its optimized objectives. Matlab’s Sequential Quadratic Programming method is used for the solution of constrained nonlinear problems, with which the calculation and optimization of mix proportion of concrete are solved satisfactorily. HPC with optimized design can meet all requirements for its type and has obvious economic benefit.

Rheological behavior of high performance concrete with mineral admixtures and their blending

Rice husk is a waste product that causes pollution and disposal problem. India is the second largest producer of rice in the world and effective use of rice husk ash in concrete is promising. Silica fume and fly ash are also waste products in some other processes and are excellent mineral admixtures of concrete. The effect of these admixtures on rheological behavior of high performance concrete have been investigated and presented in this paper. It has been observed that yield stress decreases as the replacement level of rice husk ash and fly ash increases. On the other hand, silica fume seems to be a better material for moderate plastic viscosity needed for the design of high performance concrete. For low yield stress and moderate plastic viscosity, blending of equal masses of silica fume and rice husk ash seems to be a suitable admixture at different replacement levels.