Mix Design Process Research Articles

Development of aggregate gradation based on asphalt film thickness and aggregate structure

This study is an attempt to make the aggregate blending methodology, used during the mix design process of asphalt mixture, independent of control points. The proposed procedure involves optimization of aggregate gradation based on the concept of apparent asphalt film thickness (AAFT) and balance within the aggregate structure. The underlying hypothesis was validated by comparing the volumetrics and performance of asphalt mixtures, prepared using two generated aggregate gradations, with a control asphalt mixture. The results indicated that the generated aggregate gradations had similar rutting, fatigue, and moisture sensitivity properties to the control mix. Three field trial sections were laid using the proposed procedure. Preliminary investigation revealed comparable to better density and Marshall characteristics than the conventional mixture. It is envisaged that the results and discussions from the present study can be used with the approach of balanced mix design for choosing an aggregate gradation with desirable performance attributes.

Mix design determination procedure for geopolymer concrete based on target strength method

This study presents the development and validation of a mix design determination procedure for geopolymer concrete to achieve the desired compressive strength. The procedure integrates artificial neural network (ANN) model developed based on a comprehensive data base from literature, data clustering, and parameter optimization techniques to enhance accuracy and reliability. Experimental validation is undertaken to demonstrate the mix design determination procedure’s capability to accurately predict mix designs for geopolymer concrete based on the target compressive strength, validating its efficacy for mix proportion determination. The integration of chemical oxide content in fly ash, curing time, curing temperature, and activator properties results in a 15.9% improvement in prediction accuracy for the training dataset and a 68.3% enhancement for the testing dataset, compared to the base ANN model that includes only the weight of fly ash and activator properties. Employing data clustering techniques enables the identification of prior estimates for the mix design parameters related to specific fly ash types and target compressive strength, streamlining the mix design process by analyzing pertinent data subsets. Parameter optimization ensures refined mix proportions, achieving the desired target strength economically while minimizing material waste and cost. The development of a user interface facilitates easy manipulation of mix designs, catering to users of varying expertise levels. Additional options for deeper insights into geopolymer concrete characteristics can be integrated into the mix design determination procedure. To assess the mix design determination procedure's ability to generalize effectively, a variety of fly ash samples with distinct chemical compositions were utilized, differing from those already present in the database. This approach allows for a thorough evaluation of the mix design determination procedure's performance when presented with fly ash compositions it has not encountered before. By doing so, this provides insights into the adaptability of the mix design determination procedure beyond the limitations of the training and testing datasets.

Proposed simplified methodological approach for designing geopolymer concrete mixtures

The development of geopolymer concrete offers promising prospects for sustainable construction practices due to its reduced environmental impact compared to conventional Portland cement concrete. However, the complexity involved in geopolymer concrete mix design often poses challenges for engineers and practitioners. In response, this study proposes a simplified approach for designing geopolymer concrete mixtures, drawing upon principles from Portland cement concrete mix design standards and recommended molar ratios of oxides involved in geopolymer synthesis. The proposed methodology aims to streamline the mix design process while optimizing key factors such as chemical composition, alkali activation solution, water content, and curing conditions to achieve desired compressive strength and workability. By leveraging commonalities between Portland cement concrete and geopolymer concrete, this approach seeks to facilitate the adoption of geopolymer concrete in practical construction applications. The proposed mix design guidelines have been validated through examples for concrete cured under different conditions, including outdoor and oven curing. Future research should focus on validating the proposed methodology through experimental studies and exploring cost-effective alternatives for alkali activation solutions to enhance the feasibility and scalability of geopolymer concrete production. Overall, the proposed simplified approach holds promise for advancing the utilization of geopolymer concrete as a sustainable alternative in the construction industry.

Automated, economical, and environmentally-friendly asphalt mix design based on machine learning and multi-objective grey wolf optimization

The increasing impact of the greenhouse effect on ecosystems is prompting transportation agencies to seek methods for reducing CO2 emissions during pavement construction and maintenance. Additionally, the laboratory mix design process, which involves selecting aggregate gradation and binder content, is time-consuming and labor-intensive. To accelerate the traditional mix design procedure, this study presented a mix design procedure that can automatically determine gradation and binder content based on machine learning (ML) and a meta-heuristic algorithm. Specifically, ML approaches were employed to model the relationship between volumetric properties (mixture bulk specific gravity (Gmb) and air void (VV)) and both mixture component properties and mixture proportion, based on a dataset collected from literature with 660 mixture designs. Integrated with the prediction of ML models and the modified multi-objective grey wolf optimization (MOGWO) algorithm, an automatic asphalt mix design was proposed to pursue three goals, including VV, cost, and CO2 emission. The results indicated that least squares support vector regression (LSSVR) and eXtreme gradient boosting (XGBoost) achieved the highest prediction accuracies (correlation coefficient: 0.92 for VV and 0.96 for Gmb). The MOGWO algorithm successfully found the 26 optimal mix designs for the case of VV vs. cost vs. CO2 emission. Compared to the traditional laboratory design, the optimal mixture with VV of 4% achieves a cost saving of 2.46% and a reduction of 4.03% in carbon emission. The volumetric properties of the mixtures output by the approach also align closely with values measured in a laboratory.

Developing interpretable machine learning model for evaluating young modulus of cemented paste backfill

Cemented paste backfill (CPB), a mixture of wet tailings, binding agent, and water, proves cost-effective and environmentally beneficial. Determining the Young modulus during CPB mix design is crucial. Utilizing machine learning (ML) tools for Young modulus evaluation and prediction streamlines the CPB mix design process. This study employed six ML models, including three shallow models Extreme Gradient Boosting (XGB), Gradient Boosting (GB), Random Forest (RF) and three hybrids Extreme Gradient Boosting-Particle Swarm Optimization (XGB-PSO), Gradient Boosting-Particle Swarm Optimization (GB-PSO), Random Forest-Particle Swarm Optimization (RF-PSO). The XGB-PSO hybrid model exhibited superior performance (coefficient of determination R2 = 0.906, root mean square error RMSE = 19.535 MPa, mean absolute error MAE = 13.741 MPa) on the testing dataset. Shapley Additive Explanation (SHAP) values and Partial Dependence Plots (PDP) provided insights into component influences. Cement/Tailings ratio emerged as the most crucial factor for enhancing Young modulus in CPB. Global interpretation using SHAP values identified six essential input variables: Cement/Tailings, Curing age, Cc, solid content, Fe2O3 content, and SiO2 content.

Prediction of the Skid Resistance of Recycled Hot Mix Asphalt Mixture

Typically, the skid resistance of pavement surfaces is used to indicate road safety levels. The initial value and long-term behavior of skid resistance can be reportedly determined by modulating asphalt mixture properties during their mix design process. In this study, a skid-resistance prediction equation for recycled asphalt concrete was formulated based on ANOVA test results. An accelerated polishing device was used to replicate the abrasion encountered by road surfaces owing to vehicle–tire interactions for characterizing the skid resistance of recycled asphalt concrete. Notably, key parameters, including initial and terminal values and reduction rate of skid resistance induced by vehicle-related abrasion, were determined. These findings suggest that the initial values and reduction rate of skid resistance can be estimated from the mixed binder properties and initial MTD value. However, the final/terminal skid resistance predicted using aggregate gradations has a limited degree of confidence. To further improve the accuracy of the prediction equation, additional data collection and validation tests are required. In addition, field tests using recycled asphalt concrete are necessary to refine the accuracy and precision of the prediction equations for practical road construction scenarios. This study represents a significant step toward the development of safe and effective road infrastructure.

A framework for low-carbon mix design of recycled aggregate concrete with supplementary cementitious materials using machine learning and optimization algorithms

This study presents a framework for designing low-carbon and cost-effective mixtures of recycled aggregate concrete (RAC) with supplementary cementitious materials by integrating machine learning and grey wolf optimizer algorithms. The concrete mix design process considers key performance parameters such as compressive strength, chloride ion penetration resistance, and carbonation resistance. A dataset comprising 5306 data samples from 154 scientific resources is collected from the literature to train the machine learning models. Four different techniques, namely random forest, extreme gradient boosting (XGBoost), light gradient boosting (LightBoost), and category boosting (CatBoost) are employed to model the compressive strength, chloride ion penetration, and carbonation resistances of RAC. The best-performing models are then utilized to optimize the RAC mix design by minimizing the cost and reducing the carbon footprint. The results indicate that the CatBoost model demonstrates better predictive performance for the compressive strength and carbonation resistance, while the XGBoost and LightBoost models perform better in estimating chloride ion penetration resistance. Furthermore, the adoption of low-carbon mix design principles leads to a reduction in carbon footprint by 5.0% to 31.5% compared to cost-effective mix design for different compressive strength targets, with varying permeability levels. The framework provides a promising approach for designing environmentally friendly RAC mixtures while considering economic and durability factors.

Comparative Study on the Mechanical Properties of Steel Fiber Reinforced Self-Compacting Concrete

Self-compacting concrete (SCC) is a flowable concrete which can consolidate under its own weight without the need of external vibration. The highly fluid nature of SCC makes it suitable for placing in difficult environments like congested reinforcement and thinner sections. Use of SCC can also help in decreasing noise pollution at worksites which is caused by vibration of concrete. SCC is used in bridges, buildings, and tunnel construction. The addition of small closely spaced and uniformly dispersed fibres to concrete would act as a crack resistor and would substantially improve its properties. This type of concrete is known as fibre reinforced concrete. In fact, the fibre reinforcement mechanisms can convert the brittle behaviour of this cement-based material into a pseudo-ductile behaviour up to a crack width that is acceptable under the structural design point-of-view. Fibre addition, however, increases the complexity of the mix design process, due to the strong perturbation effect that steel fibres cause on fresh concrete flow. In the present study, the effect of steel fibres on fresh and hardened properties are studied by adding steel fibres of different percentages such as 0%, 1% and 2% for M70 grade of concrete. An experimental investigation has been carried out to determine different properties like workability and strength of Fiber Reinforced Self Compact Concrete and Self- Compacting Concrete (SCC). For testing the properties of SCC and Fiber Reinforced SCC (FRSCC) in fresh state slump-flow, T50 Test, L-box, U-box, and V-funnel tests were conducted. Whereas for testing the properties in hardened state compressive strength, split tensile strength and flexural strengths are carried out. Detailed studies have revealed that the Steel Fiber Reinforced Self Compacting Concrete made with the Steel fibres displays a better performance. The investigations and results are presented from the study. Index Terms: Self compacting concrete, Steel fibre reinforced concrete, Super plasticizer, Silica fume, Fly ash, Steel fibre.

Mix design process for sustainable self-compacting geopolymer concrete

Faster growth of infrastructure emphasizes the utilization of eco-friendly construction materials with a low carbon footprint. The objective of this experimental study is to propose a mix design methodology for self-compacting geopolymer concrete (SCGC) considering various influencing parameters recommended for self-compacting concrete (SCC) and geopolymer concrete (GPC). The experimental test results depict that SCGC had similar flow deformability with comparatively lesser flow speed compared to SCC. The increase in a sodium hydroxide (NaOH) molarity from 8 M to 16 M negatively affected the flow deformability as well as the flow speed of SCGC. The optimum compressive strength of 37 MPa was attained for SCGC having silica sand as fine aggregate, NaOH concentration of 16 M, fly ash to sand ratio (FA/S) of 0.75, and cured for 48 H at 80 °C. A mathematical equation is proposed to calculate Young's modulus of SCGC which is around 15 GPa. Young's modulus of SCGC was lesser than OPC concrete recommended by the ACI 318, however, Poisson's ratio of SCGC was found in the recommended range of OPC concrete. A flow chart is proposed for the mix design of SCGC with consideration of experimental test results in fresh and hardened stages.

Effect of fibre diameter and tensile strength on the mechanical, fracture, and fibre distribution properties of eco-friendly high-strength self-compacting concrete

The development of Steel Fibre-Reinforced Self-Compacting Concrete (SFR-SCC) is a complex task with significant practical implications for the construction industry. However, the absence of established design guidelines and recommendations, and the limited availability of design methodologies, present a significant challenge. Achieving the targeted rheological and mechanical properties while simultaneously minimising production costs requires a comprehensive understanding of the optimal blend of fibre type and quantity and the coarse aggregate content. This paper addresses the impact of steel fibre properties on the rheological and fundamental mechanical properties of eco-friendly high-strength self-compacting concrete (HSSCC), using 40% ground granulated blast furnace slag (GGBS) as a cement replacement. The research focused on 30 mm long hooked-end steel fibres with tensile strengths of 1345 MPa and 3070 MPa, and diameters of 0.55 mm and 0.38 mm, respectively. In addition, the study investigated the combined effect of coarse aggregate content and steel fibre type on the performance of eco-friendly HSSCC. The fresh properties of eco-friendly HSSCC were assessed using the slump flow and J-ring tests. Furthermore, mechanical and fracture properties such as compressive strength, splitting tensile strength, elastic modulus, four-point flexural strength, and three-point flexural strength on notched prisms were also evaluated. The distribution and alignment of steel fibres in the HSSCC were assessed using image analysis techniques, which showed that the steel fibre diameters were a crucial factor in the fibre dispersion. The results demonstrated that using steel fibres with higher tensile strength and smaller diameter significantly enhanced the splitting tensile strength, flexural strength, and fracture energy, compared to steel fibres with larger diameters and lower tensile strengths. Additionally, the study indicated that the elastic modulus and fracture energy of eco-friendly HSSCC reinforced with both types of steel fibre are highly dependent on the content of coarse aggregate used. The study highlighted that the coarse aggregate proportion and the steel fibre characteristics were two vital factors that influenced the rheological and mechanical properties of HSSCC and must be considered during the mix design process to optimise the desired properties while also minimising production costs.

Computational Complexity and Its Influence on Predictive Capabilities of Machine Learning Models for Concrete Mix Design.

The design of concrete mixtures is crucial in concrete technology, aiming to produce concrete that meets specific quality and performance criteria. Modern standards require not only strength but also eco-friendliness and production efficiency. Based on the Three Equation Method, conventional mix design methods involve analytical and laboratory procedures but are insufficient for contemporary concrete technology, leading to overengineering and difficulty predicting concrete properties. Machine learning-based methods offer a solution, as they have proven effective in predicting concrete compressive strength for concrete mix design. This paper scrutinises the association between the computational complexity of machine learning models and their proficiency in predicting the compressive strength of concrete. This study evaluates five deep neural network models of varying computational complexity in three series. Each model is trained and tested in three series with a vast database of concrete mix recipes and associated destructive tests. The findings suggest a positive correlation between increased computational complexity and the model's predictive ability. This correlation is evidenced by an increment in the coefficient of determination (R2) and a decrease in error metrics (mean squared error, Minkowski error, normalized squared error, root mean squared error, and sum squared error) as the complexity of the model increases. The research findings provide valuable insights for increasing the performance of concrete technical feature prediction models while acknowledging this study's limitations and suggesting potential future research directions. This research paves the way for further refinement of AI-driven methods in concrete mix design, enhancing the efficiency and precision of the concrete mix design process.

A prediction of the printability of concrete through Artificial Neural Networks (ANN)

Digital Fabrication with Concrete (DFC) is acquiring potential because of the need of a technological advancement in the construction industry. The rheology of concrete finds evident meaning, because of the necessity of “handling” concrete in its early ages, when still fluid, and allowing it to immediately start developing its mechanical performance for the building process to advance. In order to make the material suitable for the printing process, it must comply with the printability requirements that are governed by parameters including yield stress and tensile and shear strength. Nowadays, the study behind the mix of a concrete material that fits all the printing requirements is still something that goes through an iterative trial and error process, due to the difficulty and the novelty that 3D Concrete Printing (3DCP) represents. For this reason, it can be helpful to develop a model, based on experience and the literature, that is able to predict if, for a given concrete mix design and printing process, all the requirements tied to 3DCP are satisfied. The employment of Artificial Intelligence (AI) can represent a solution to it. Applications of AI are very wide: referring to Civil Engineering, AI can be applied to concrete science and technology to handle several different topics including mix proportions, workability and overall mechanical performance, being instrumental to establish and assess multi-parameter correlations among the different involved material and process input parameters. AI tries to mimic the human brain, that is made up of many nerve cells driven by neurons which are in control of the external stimuli. The purpose of the paper is to analyse the printability through the implementation of AI techniques, designing a neural network between the parameters that control the mix composition and rheological properties of printable concrete mixes in the printability window.

Laboratory testing and field application of devulcanized rubber/SBS composite modified asphalt

Styrene-butadiene-styrene copolymer (SBS) and recycled tire rubber are the typical polymers for asphalt modification. The dispersion of crumb tire rubber (CTR) dominates the composite effect of rubberized asphalt. And the multiscale of CTR in mixtures always challenges the design and construction of asphalt mixtures. The multiscale dispersion of CTR can be controlled by pre-devulcanization. In this study, lightly devulcanized rubber (LDR) with various dispersive scales in asphalt from microns to dozens of microns, and deeply devulcanized rubber (DDR) with micro-nano scales were prepared and analyzed, respectively. The devulcanized rubber (DR) was further compounded with SBS to prepare composite modified asphalt. The influences of rubber content on the structure and properties of modified asphalt and mixtures were investigated, and the dispersive structure model of asphalt mixture was also established. Furthermore, a high viscoelastic rubberized asphalt was prepared with 4% SBS and 20% DDR in the field plant and paved in a field project, a new first-grade national highway. The results showed that the devulcanization of CTR can help the dispersion of CTR in asphalt by improving the compatibility among CTR, SBS, and asphalt. And the dispersive scale and the content of DR can affect the binder content, high and low temperature performance of asphalt mixtures. The field project showed that the high viscoelastic rubberized asphalt exhibited similar mix design and construction process but better high and low temperature performance compared to SBS modified asphalt.

An Experimental Study on Partial Replacement of GGBS with Cement and Utilizing Demolished Coarse Aggregate in Concrete

bstract: The majority of municipal solid wastes are made up of debris from construction and demolition projects, including recycled construction aggregate (RCA). This paper presents the findings of an experimental and analytical examination of the creation of environmentally friendly concrete. It is a good idea to replace natural material with aggregate from old, demolished structures to reduce debris and preserve the environment. To reduce consumption, recycled aggregate can be utilized in place of natural aggregate. To address these issues, an attempt is undertaken in the current experimental work to substitute GGBS in varied proportions for cement. The purpose of this research is to demonstrate the findings of an investigation into the applicability of GGBS in the manufacturing of concrete by using construction and demolition debris as a recycled resource. Ground granulated blast furnace slag (GGBS) and recycled concrete aggregates are the recycled materials employed in this study. A concrete mix of grade M25 was created through the mix design process. GGBS was partially replaced with wastedestroyed aggregate in the amounts of 0, 10, 20, 30, and 40% to create the mixes. Cubes of 150x150x150mm3 were poured. The compressive strength of the concrete cube specimens was measured by smashing the cube after 7, 14, 28, 60, and 90 days of curing for various curing intervals. The compression test results on the cubes show that strength increases with the amount of slag added up to the maximum value. Compressive strength was seen to grow when GGBS content increased up to 30%, but after the replacement level was above 30%, strength began to deteriorate. The outcomes of GGBS with conventional concrete and concrete made entirely of demolished aggregate without GGBS are compared to the outcomes of GGBS with concrete containing demolished aggregate

A Performance-Based Design Framework for Enhanced Asphalt Concrete in the Caribbean Region

The majority of recent research in has been focused on the introduction, development, and evaluation of performance-based specifications for the design of asphalt concrete mixtures. In the Caribbean, most laboratories only use the Volumetric Marshall Mix design methodology. Road designers, engineers, consultants, and contractors have all highlighted the many shortcomings and inadequacies of the volumetric design mix selection process. The consensus is that there is a need to a develop process that addresses the service’s performance and common distress on asphalt pavements through performance-based mix design. Performance-Based Mix Design (PBMD) has been used as a practical alternative to the volumetric mix design technique in evaluating pavement distresses by incorporating performance testing in conjunction with volumetric parameters into the mix design process. This study describes the findings of our comprehensive laboratory research, which was conduced to facilitate the development of a Performance-Based Mix Design framework for asphalt concrete mixtures. The research methodology involved the collection and integration of qualitative and quantitative data through case studies and experimental work. Two control and two PBMD mixtures were designed to evaluate both the volumetric and performance properties that address distresses of permanent deformation (rutting), fatigue cracking, stiffness, and moisture susceptibility. The PBMD framework led to the development of performance test criteria for permanent deformation, stiffness modulus, fatigue cracking, and an acceptable tensile strength ratio. The development and implementation of the PMBD framework for the Caribbean region could exemplify a significant step towards achieving sustainable asphalt pavements in the region. The framework, which incorporates best-practice solutions, is anticipated to increase awareness about the variables that affect the performance of asphalt mixtures.

Experiment investigation on mix proportion optimization design of anti-cracking stone filled with cement stabilized macadam

Semi-rigid base asphalt pavement is widely used in China for asphalt pavement construction. However, the presence of reflection cracks remains a significant issue associated with semi-rigid bases. Large Stone Filled with Cement Stabilized Macadam (LSFC) has been identified as an effective solution for inhibiting reflection cracks. However, the construction of LSFC using large particle size coarse aggregates can be challenging. Additionally, the existing mix design process lacks clarity and results in low strength. This paper aims to optimize the design method for anti-cracking Stone Filled with Cement Stabilized Macadam (SFC) by selecting appropriate materials and improving design process. Initially, a mix proportion for SFC is designed using coarse aggregates with common particle sizes employed in engineering applications. The filling proportion of the coarse aggregates, cement dosage, and mass proportion of the cement mortar are gradually determined through compaction and 7-day unconfined compressive strength tests, resulting in the complete mix proportion for SFC. Subsequently, the strength performance, fatigue performance, dry shrinkage performance, and temperature shrinkage performance of SFC are evaluated and compared with common Cement Stabilized Macadam (CSM). The results indicate that while the strength and fatigue properties of SFC are slightly inferior to those of common CSM, it exhibits superior dry shrinkage performance. Moreover, SFC demonstrates better overall crack resistance compared to common CSM, despite having slightly inferior temperature shrinkage performance.

Review of geopolymer concrete in high grade infrastructure construction

The construction industry must ensure a new occupant experience that is connected, safe, and comfortable. Due to its versatility, concrete is the most frequently used building material worldwide. Cement is an essential ingredient of concrete, but it is not considered environmentally friendly. The production of Portland cement consumes a considerable amount of energy and emits a significant amount of carbon dioxide. By creating and utilizing alternative materials, like geopolymers, construction may become much more sustainable. Cement production has a significant environmental impact, which must be reduced by developing new binder types. With the use of various industrial byproducts, the workability, impacts of temperature change, usage of admixtures, use of fibers, and effects of the water-binder ratio of geopolymer concrete are all investigated. It indicates that the regulated geopolymer concrete mix's compressive strength is inferior to that of geopolymer concrete with additional hooked-end steel fibers. As a recent innovation in concrete, Geopolymer is a substance in which fly ash, which is activated by an alkaline solution to act as a binder, completely replaces cement. Geopolymer concrete was analyzed with different ratios of activators for strength analysis. To select the correct ingredients for geopolymer concrete to achieve the desired strength at the desired workability, the grading of geopolymer concrete following the Indian Standard Code has been the subject of experimental effort. Assessing the amount and fineness of fly ash, the amount of water, or the amount of alkaline liquid is a step in the mix design process, along with the W/C ratio, fine aggregate quality, fine aggregate proportion to total aggregate, and superplasticizer content. Premixed geopolymer concrete's compressive strength, shear strength, flexural strength, and bonding strength will be evaluated after the materials' internal reactivity has been verified.

Development of durable class F fly ash based geopolymer concretes

The production of Portland Cement (PC) has been shown to be responsible for 5-8% of global CO2 emissions. This has led to class F fly ash based geopolymer concrete being developed as a substiture for PC concrete for to reduce these global CO2 emissions. However, research has shown that each fly ash has unique characteristics and requires a specific mix design for each fly ash. This is can occupy a significant amount of time. Furthermore, before the mix can be adopted for commercial application it requires the long-term durability to be established. This gap is one of the primary limitations delaying the adoption of geopolymer concrete. While each fly ash is unique, they do have common characteristics which can be utilized to optimise the mix design process. Geopolymers are also known to have good durability characteristics, in particular for acid and sulphate exposure. This paper reports the mix design optimisation process for six class F fly ashes from Australia, Sri Lanka and Indonesia. The strength properties and durability performance of the optimised mixes is reported including the compressive strength development, chloride and carbonation resistance together with performance when exposed to sulphate and acidic media for the Indonesian fly ash, including to a simulated peat soil designed to replicate the conditions experienced in Indonesia.

Effect of recycled crushed bricks as additive in pavement material

Recycling waste materials has gained its own demand and popularity especially in solving disposal problems, hence creating opportunity for researcher to study the advantage of using recycling materials in their products. This paper studied on the impact of indirect tensile strength(ITS) tests on the characterization of bituminous mixes. It focuses on the Marshall Mix design process to determine the optimum binder content in asphalt mixtures of Hot Mix Asphalt (HMA) with recycled crushed bricks (RCB). Samples were prepared with grading the natural aggregates and RCB according to the AC14 grading limit. At 160°C, the samples were blended with 4-6% bitumen content of grade 60/70 until uniformly mixed. Next, another 18 samples were prepared in accordance with Marshall optimization to analyse the performance of sample by conducting both dry and wet ITS test. 20% and 30% of RCB were added to replace fine aggregates of 1.18 mm to pan sieve. The result shows that the dry method gives a better result of ITS with 20% to 30% of replacement of fine aggregates with RCB. The average maximum load that can be load to the sample was 13.185 kN of load for dry sample whereas the wet sample with 11.23 kN of load.

Procedure for mix designing used for steel fibre reinforced self-compacting concrete

This work focuses on a methodical design process for developing self-compacting concrete (SFRSCC) using steel fibre reinforcements. The objective is to create a self-consolidating concrete that has the double advantage of self-consolidating characteristics and composite toughness. Experimental modelling involved building the mortar phase with manufactured sand (M-sand) as fine aggregate utilising a systematic mix design process based on the particle packing idea. Optimisation of aggregates was determined using the packing density idea and slump cone research. By conducting a paste consistency test, cement and ground granulated blast furnace slag (GGBFS) powder combinations were chosen. Finally, the amount of superplasticizer was determined based on Marsh cone experiments and the percentage by volume of steel fibres determined by slump flow research. The addition of steel fibre dosage impacts the workability of both standard and high strength concrete, and the fresh properties emphasised the necessity to maintain a large paste volume for increased workability. On the basis of the recommended mix design technique using M-sand, self-compacting concrete with a maximum strength of 70MPa was manufactured. On the other hand, experimental studies on the fresh and hardened properties of recommended self-compacting steel fibre concrete mixes were presented. In addition, test findings demonstrated the viability of employing M-sand as a replacement for river sand in its entirety.