Amount Of Cement Research Articles

Effect of Calcium Hydroxide Addition on the Promotion of the Pozzolanic Reaction of Excess Silica Fume in Ultra-High Performance Concrete

In this study, to utilize excess silica fume that cannot react owing to the low water-binder ratio of ultra-high-performance concrete (UHPC), Ca(OH)<sub>2</sub> was substituted and mixed at 1-5%, relative to the weight of the cement, to produce UHPC. Then, the effect of this Ca(OH)<sub>2</sub> substitution on the hydration properties of UHPC and the pullout resistance of embedded steel fibers were investigated. The results of a thermogravimetric test show that when the substitution rate of Ca(OH)<sub>2</sub> increased, the amount of cement decreased slightly. However, Ca(OH)<sub>2</sub> accelerated the hydration reaction to form dense C-A-S-H. In CH-2 in which the DTG peak is maximized, the compressive strength, average adhesion strength, drawing energy, and equivalent adhesion strength were 141 MPa, 22.5 MPa, 680.4 mJ, and 22.6 MPa, respectively. Moreover, when the substitution rate exceeded 2%, all mechanical performance results deteriorated.

Improvement of vibration response of a foundation resting on Salt-Encrusted Flat Soil by cement addition: a numerical investigation

ABSTRACT Excessive vibrations can negatively impact machinery performance by causing misalignment, decreased accuracy, increased wear and tear, and reduced overall operational efficiency. By conducting a numerical investigation on improving vibration response through cement addition to salt-encrusted flat soil, the study aims to enhance the performance and reliability of machine foundations, thereby optimizing machinery operation. The properties of untreated sabkha and cement-sabkha were calibrated to use in the Finite Element Method (FEM) model. The variables investigated have the thickness of cemented sabkha ratio, operation frequency, three types of subsoils (Sabkha and cemented sabkha of 5% and 10%), and vertical amplitude loading. The main findings revealed that the maximum shear modulus (Gmax) increases by 47% with a 5% to 10% increase in cement content. In addition, as the vibration’s frequency increases, the amplitude displacement is observed because the reflected waves become smaller for the foundation resting on cemented sabkha. The natural frequency rises as the thickness ratio and cement amount of cemented sabkha increase. The influence of the thickness ratio on displacement amplitude is considerable when the operation frequency is less than 20 hz. The study implies that engineers can optimize the design of machine foundations to achieve better performance and stability.

Indirect estimation of compressive strength of cement-stabilized mine tailings by using ultrasonic pulse velocity for road construction

This study proposes a correlation formula between the compressive strength, ultrasonic pulse velocity UPV, and dynamic elastic modulus of cement-stabilized mine tailings CSMT. Samples were tested in curing times (1, 7, 14, 28, and 90 days) by cement amounts (2%, 4%, 6%). The findings demonstrated that the strength and wave velocities of CSMT were enhanced due to increased cement content and extended curing periods. A regression equation with a high coefficient of determination (R-square >0.95) was developed to establish a relationship between compressive strength and ultrasonic pulse velocity UPV for tailings containing 6% cement content in both treated types. The study uncovered a strong relationship between pulse velocity and compressive strength in different CSMT mixtures. These findings indicate that UPV tests can accurately, rapidly, and without causing damage, and evaluate the compressive strength of CSMT for road paving. The results improve the advancement of environmentally friendly building materials and showcase the efficacy of non-destructive ultrasonic testing in assessing structural characteristics. The investigation has profound consequences in enhancing the quality and management of road construction industry, and the management of mining waste.

Analisa Perbandingan Biaya dan Mutu Beton Menggunakan Serbuk Cangkang Telur untuk Mereduksi Pemakaian Semen

Concrete is a very important construction material in construction so that the use of concrete is widely used. In the use of conventional concrete, there are many problems that arise, such as the use of so much cement that can affect the environment. In this study, the use of eggshell powder will be used to reduce the amount of cement in the proportion of the concrete constituent mixture as well as in the use of powder and can reduce the cost of using cement which is relatively expensive. In addition, chicken egg shell waste contains calcium carbonate where it is known that one of the constituent ingredients of Portland Cement (SP) is calcium carbonate. This study uses SNI 03-2834-2000, to calculate the proportion of the concrete mixture. Testing the compressive strength of this concrete using cylindrical specimens with a diameter of 150 mm (0.15 m) and a height of 300 mm (0.30 m) as many as 15 samples with the amount of chicken egg shell powder used at 0%, 1%, 2%, 3% and 4%. From the results of testing the average compressive strength at the age of 7 days and then converted to the age of 28 days, the average compressive strength at 0% eggshell percentage is 25.76 MPa, 1% is 22.80 MPa, 2% is 20.43 MPA, 3% is 20.13 MPa, 4% is 19.98 MPa. The greater the percentage of chicken egg shell powder, the compressive strength of the concrete decreases. For the cost of 0% concrete mix is Rp. 11,414.5, 1% is Rp. 11,342.5, 2% is Rp. 11,270.5 3% is, Rp. 11,198.5, 4% is Rp. 11,150.5 the cost of concrete using egg shell powder relatively cheaper.

Effect of water to cement ratio on mechanical properties of FRC subjected to elevated temperatures: Experimental and soft computing approaches

The deterioration of concrete is greatly dependent on the cracks and microcracks development owing to loading or environmental influences. An effective step to avoid the spread of cracks and microcracks is employing of different fibers in concrete and the manufacture of fiber reinforced concrete. On the other hand, fire is one of the cases that always threatens engineering structures. Elevated temperatures cause obvious chemical and physical changes that lead to concrete deterioration. In this study, the effect of adding various fiber with different volume contents in concrete mixture with various cement content was evaluated experimentally. Results indicate that spalling was more dominant in mixture containing steel fiber and with higher amount of cement, while there is not any spalling in mixture containing polypropylene (PP) fibers. Moreover, the reduction in tensile strength of fiber-free concrete specimens is less pronounced in mixtures with higher cement content. In addition, the positive performance of PP fibers compared to steel fibers was proved at higher temperatures and cement contents. Furthermore, by conducting a comprehensive and accurate survey, this research pinpoints gaps in the current literature. Moreover, the gathered dataset serves as input for training a machine learning approach known as Group Method of Data Handling (GMDH). The GMDH network demonstrates a satisfactory accuracy in predicting experimental results, with a MSE of 0.0044.

Effect of crown seating methods on the remnant cement in the subgingival region of a cement-retained implant crown

This study aimed to investigate the effects of crown seating speed, crown seating force, quantity of cement used, and type of implant cement on the amount of remnant cement in the subgingival region (RCS) after cementation. Cement-retained implant crowns were cemented to titanium abutments using the following methods: four types of implant cement (TBN: TEMP BOND NE, NR: NEXUS RMGI, ME: MAXCEM ELITE, and U200: RELYX U200), three quantities of cement (0.02 ml, 0.04 ml, and 0.06 ml), three crown seating speeds (5 mm/s, 10 mm/s, and 15 mm/s), and two crown seating forces (25 N, 50 N). The surface area and length of the RCS were measured using a 3D intraoral scanner. The total RCS weight was measured using an analytical balance. The RCS increased significantly as the seating speed increased, the seating force increased, and the quantity of cement increased (p < 0.05). The RCS values were the highest for TBN, followed by U200, NR, and ME (p < 0.05). The lower seating speed, smaller quantity of cement used, and smaller seating force applied in cement-retained implant restorations minimized the RCS in cement-retained prostheses. The type of cement is a factor that determines the aspects of the RCS.

Evaluation on Preparation and Performance of a Low-Carbon Alkali-Activated Recycled Concrete under Different Cementitious Material Systems.

A green, low-carbon concrete is a top way to recycle waste in construction. This study uses industrial solid waste slag powder (S95) and fly ash (FA) as binders to completely replace cement. This study used recycled coarse aggregate (RCA) instead of natural coarse aggregate (NCA). This is to prepare alkali-activated recycled concrete (AARC) with different cementitious material systems. Alkali-activated concrete (AAC) mixtures are modified for strength and performance based on the mechanical qualities and durability of AARC. Also, the time-varying effects of the environment on AARC properties are explored. The results show that with the performance enhancement of RCA, the mechanical performance of AARC is significantly improved. As RCA's quality improves, so does AARC's compressive strength. At a cementitious material content of 550 kg/m3, AARC's 28d compressive strengths using I-, II-, and III-class RCA were reduced by 2.2%, 12.7%, and 21.8%, respectively. I-class AARC has characteristics similar to natural aggregate concrete (NAC) in terms of shrinkage, resistance to chloride penetration, carbonization, and frost resistance. AARC is a new type of green building material that uses industrial solid waste to prepare alkali-activated cementitious materials. It can effectively reduce the amount of cement and alleviate energy consumption. This is conducive to the reuse of resources, environmental protection, and sustainable development.

Application of Low-Temperature Ice Saline Bone Cement in Percutaneous Vertebroplasty

Percutaneous vertebroplasty uses the traditional method of bone cement filler to inject bone cement, which is easy to solidify, we have found a new method to delay the solidification of bone cement: low temperature ice saline bone cement, which compares the advantages of the new method and the traditional method of injecting bone cement. 82 cases of osteoporotic vertebral compresion fracture(OVCF) were divided into two groups by retrospective study method, 40 patients in group A were treated with the traditional method, 42 patients in group B were treated with the new method.The leakage rate of bone cement, postoperative VAS score, amount of bone cement in each vertebral body, operation time of bone cement, and number of bone cement fillers used were compared between the two groups. There was no significant difference in the bone cement leakage rate, postoperative VAS score, the amount of bone cement in each vertebral body and the number of bone cement fillers used between the two groups; the operation time of bone cement in two groups was statistically significant, and the operation time in group B was significantly longer than that in group A. Low temperature ice saline water bone cement has significant advantages in multiple vertebral fractures, relatively large amount of bone cement injected into each vertebral body and long operation time, which is more suitable for beginners.

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.

Diagenesis Variation in Different Distributary Channels of Shallow Water Lacustrine Delta Deposits and Implication for High-Quality Reservoir Prediction: A Case Study in the Chang 8 Member in Caijiamiao Area, Sw Ordos Basin, China

Tight oil reservoirs are considered important exploration targets in lacustrine basins. High-quality reservoir prediction is difficult as the reservoirs have complex distributions of depositional facies and diagenesis processes. Previous research has found that the diagenesis process of tight oil sandstones varies greatly in different depositional facies. However, diagenesis variation in different depositional facies is still poorly studied, especially in distributary channels of shallow water delta deposits in lacustrine basins. Based on the description of core samples, the observation of rock slices, the interpretation of well logging data, and the analysis of porosity and permeability data, the differences in the lithofacies types, diagenesis processes, and pore structures of different distributary channels have been clarified. Ultimately, a model of diagenesis and reservoir heterogeneity distribution in the shallow-water delta of Chang 8 Member of the Yanchang Formation in the Caijiamiao area of the Ordos Basin has been established. This research indicates that the main distributary channels in the study area are dominated by massive bedding sandstone lithofacies, while the secondary distributary channels are primarily characterized by cross-bedding sandstone lithofacies. There are significant differences in the compaction, dissolution, and cementation of authigenic chlorite and carbonate among different parts of the distributary channels. Plastic mineral components, such as clay and mica, are abundant in sheet sands, and are more influenced by mechanical and chemical compaction. Influenced by the infiltration of meteoric water and hydrocarbon generation, dissolution pores are relatively well-developed in the underwater distributary channel reservoirs. A large amount of carbonate cementation, such as calcite and siderite, is found within the sandstone at the interface between sand and mud. The occurrence of authigenic chlorite exhibits a clear sedimentary microfacies zonation, but there is little difference in the kaolinite and siliceous cementation among different microfacies reservoirs. Finally, a model of diagenetic differences and reservoir quality distribution within dense sand bodies has been established. This model suggests that high-quality reservoirs are primarily developed in the middle of distributary channels, providing a theoretical basis for the further fine exploration and development of oil and gas in the study area.

Effects of C-S-H Seed Prepared by Wet Grinding on the Properties of Cement Containing Large Amounts of Silica Fume

This study aimed to utilize the hydration characteristics of cement through wet grinding techniques to efficiently and conveniently prepare a stable C-S-H seed suspension, providing key parameters and a scientific basis for their large-scale production, which ensures the stability of the C-S-H suspension during production, transportation, and application. This preparation aimed to mitigate the adverse effects of high-volume silica fume on the early mechanical properties of high-performance cement concrete. The properties of C-S-H seed were characterized in detail by SEM, XRD, and TD. In the concrete performance test, silica fume was used to replace part of the cement, and different contents of C-S-H seed were added to test its effect on the compressive strength of concrete, with XRD and SEM used to analyze the performance differences. The results show that the particle size and hydration degree of cement no longer developed after 90 min of wet grinding. Polycarboxylate ether (PCE) superplasticizer can increase the fluidity of the crystal C-S-H seed suspension when the content exceeds 1.5%. When the content of PCE exceeded 2%, the C-S-H seed suspension precipitated. Adding 5% C-S-H seed can increase the compressive strength of cement concrete by 10% under the condition of reducing the amount of cement and increasing the amount of silica fume. And Ca(OH)2 (CH) was produced by cement hydration consumed by silica fumes to generate C-S-H gel, by which the concrete became denser with more strength. However, when the amount of C-S-H seed exceeded 7%, the compressive strength of the concrete decreased.

Comprehensive Utilization of Industry By-Products in Precast Concrete: A Critical Review from the Perspective of Physicochemical Characteristics of Solid Waste and Steam Curing Conditions

Solid wastes have been widely used as a cement substitute in precast concrete. On the one hand, solid waste can effectively ameliorate a series of problems caused by steam curing. On the other hand, the use of solid waste can reduce the amount of cement used in the construction industry and reduce carbon emissions. However, due to the complexity of the steam curing system, the performance of precast concrete prepared under different steam curing conditions varies greatly. Moreover, there are a wide variety of solid wastes, and the differences in the physicochemical properties of different solid wastes are significant. Therefore, it is necessary to systematically determine the mechanism of action of commonly used solid wastes. In this paper, the steam curing system is introduced in detail, and the mechanism of action of solid waste in precast concrete is systematically summarized. It was found that an appropriate increase in the temperature and duration of steam curing facilitates the strength development of precast concrete. In addition, there is a difference in the effect of the addition of solid wastes on the early and late strength of precast concrete, which usually leads to a decrease in the demolding strength of precast concrete, but increases the late strength of precast concrete. This study provides a reference for rationally regulating steam curing systems and realizing the comprehensive utilization of solid wastes in precast concrete.

Use of Various Industrial and Eggshell Wastes for the Sustainable Construction Sector

The alternative composites’ production alleviates the serious problem generated by global warming. Methods to reduce the amount of cement used in concrete production, for example, are being investigated to determine how to reduce carbon dioxide emissions in many applications. Egg shells and various industrial wastes, which are recommended to use in the construction sector at an appropriately high rate, also cause serious environmental damage. Bottom ash (BA) and marble powder (MP) wastes are used today in civil engineering applications. In addition, it is important to increase the use of eggshells due to their rich calcium carbonate content. In this work, BA and MP wastes were blended with eggshells to produce cement paste composites. Two different sets of composites were prepared during this study. The composites were prepared with cement (80%), BA (20%), and MP (20%) wastes by weight with 0.3%, 0.75%, 1.5%, and 2.5% eggshell waste. The fresh (flow table), physical (dry unit mass, apparent specific gravity, and porosity), mechanical (unconfined compressive strength and flexural strength), and durability (water absorption, seawater resistance) tests were conducted. According to the experimental results, the composites can be classified as lightweight construction materials. The test results showed that 0.75% eggshell by weight of cement in bottom ash and marble powder can be used as an optimum value for better performance. The bottom ash mixtures groups are higher water absorption and porosity values when referring to the marble powder mixture groups. The highest compressive strength value was found at 56.03 MPa in the MP mixture group and 52.79 MPa in the BA mixture groups with these optimum eggshell combinations at 56 days. The MP mixture group showed better resistance to seawater when referring to the bottom ash blended mixtures. Laboratory-produced composites are possible candidates for cost-effective and environmentally friendly building materials. The eggshells have a promising alternative binder for concrete in the near future and they are utilized together with industrial wastes such as BA and MP in sustainable concrete construction.

Comparing durability and compressive strength predictions of hyperoptimized random forests and artificial neural networks on a small dataset of concrete containing nano SiO2 and RHA

Through the increasing use of supplementary cementitious materials, the properties of concrete have taken on increased significance in a design code. Using reliable prediction models based on a small data set for the mechanical properties and durability of concrete can reduce the number of trial batches and experiments needed to produce useful design data in the laboratory, reducing time as well as resources. In this study, we investigate how the properties of water penetration, chlorine resistance, and compressive strength can be predicted by polynomial regression (PR), random forest (RF) regression, and artificial neural networks (ANNs) based on the input values of density, workability, and the constituent amount of rice husk ash, cement, and nano SiO 2 . We vary the training data used and test the coefficient of determination ( R 2 score) on the remaining data as a test set to measure predictive capability. We show that RFs and ANNs outperform PR in all settings and have unambiguously extrapolating properties when hyperparameter optimization is designed for this purpose. Remarkably, we obtain R 2 scores on the test data of 0.858 − 0.990 for RFs and 0.825 − 0.985 for ANNs.

Influence of fly ash and granulated blast furnace slag on autogenous shrinkage and drying shrinkage of cement-based materials mixed with alkali accelerator and alkali-free accelerator

Shotcrete shrinkage and cracking are getting more threatening due to its high content of cementitious materials, small aggregate particle size and a large amount of accelerator, which poses a great threat to the construction safety of engineering structures. This paper studies the volume stability of cement mortar with different contents of fly ash (FA) and granulated blast furnace slag (GBFS) (10%/20%/30%) under the condition that the dosage of alkali liquid accelerator is 4% and that of the alkali-free liquid accelerator is 7%. This work is designed to provide a guidance for the basic research and practical application. By comparing the autogenous shrinkage, drying shrinkage, mass loss caused by drying condition, and internal humidity change of drying condition of cement mortar within 180 days of age. Combined with MIP analysis, XRD hydration product analysis, and SEM hydration product morphology analysis, the results show that both fly ash and granulated blast furnace slag can reduce the autogenous shrinkage and drying shrinkage of cement mortar mixed with accelerator to a certain extent, and the shrinkage reduction effect of FA is more prominent. The reduction effect of GBFS is more obvious at high dosages. Adding FA and high content of GBFS can increase the mass loss of cement mortar under drying shrinkage conditions. The high content of mineral admixtures will exacerbate the rate of decrease in the internal humidity of the cement mortar. The reasons for the reduction of autogenous shrinkage and drying shrinkage of cement mortar mixed with accelerators due to the addition of FA and GBFS are: the early activity of the two mineral admixtures is low, and the hydration process is slow. And the reduction of pore size with the range of 5-50nm leads to the reduction of capillary tensile stress and the weakening of the driving force for shrinkage deformation. Among them, the shrinkage reduction effect of fly ash is more pronounced. Therefore, mineral admixture can be used to replace a certain amount of cement in actual shotcrete construction, so as to increase the volume stability of shotcrete.

Hybrid XGB model for predicting unconfined compressive strength of solid waste-cement-stabilized cohesive soil

The utilization of cement has been found to have negative environmental impacts. In order to reduce the quantity of cement used and improve the mechanical properties of solid waste-cement-stabilized cohesive soil, the incorporation of solid waste as additives has been investigated. Unconfined compressive strength is a crucial parameter in geotechnical engineering. However, existing empirical formulas have limited accuracy and applicability when it comes to the unconfined compressive strength of solid waste-cement-stabilized cohesive soil. The machine learning model can be used to provide accurate and comprehensive predictions by considering the nonlinear relationships between independent and dependent variables. This study aims to propose a machine learning model tuned by optimization algorithms with high generalization performance in accurately predicting the unconfined compressive strength. Firstly, a database containing 474 specimens was developed. Secondly, eight machine learning models were established, composed five single models and three hybrid models, to train and test the database. Six performance indicators were employed to evaluate the generalization ability of these models. Finally, the optimal model was selected for analysis of the importance of the feature variables using shapley additive explanations, which were compared with those of the existing empirical model. The research findings indicated that, the extreme gradient boosting model tuned with tree-structured parzen estimators exhibited the highest predictive accuracy and generalization ability. The curing age, cement content, plastic limit, and water content were identified as the most critical factors influencing the unconfined compressive strength. Among the chemical components in solid waste, the aluminum oxide content and silicon dioxide content were found to significantly influence the unconfined compressive strength, while the impact of calcium oxide content was relatively minor. Furthermore, the optimal solid waste content was found to be around 10 %. This study made a significant contribution to the effective utilization of waste resources in the context of sustainable construction practices.

The utilization of pulverized waste tire rubber in a soil–cement composite for sustainable compressed earth brick production

This study examined the suitability of blending waste tires with cement in indigenous soil for producing compressed earth bricks (CEB) to achieve a sustainable environment. CEB was produced with clay soil and a combination of cement at 5 and 10% levels with varying dosages of pulverized waste tire at 0, 2.5, 5, 7.5, and 10% mixture. Classification tests were conducted to determine engineering properties such as the particle size distribution, Atterberg limit, specific gravity, optimum moisture and unit weight of the soil. The physical properties of the pulverized tire were evaluated. Moreover, density, water absorption, and compressive strength were determined for hardened CEB samples produced from mixtures of soil and various proportions of blended cement and pulverized waste tire materials. The classification results showed that the soil was silty clay of low plasticity. The density of the CEB samples was observed to increase slightly with the addition of blended cement-tire residue. Furthermore, a considerable improvement in the compressive strength development of the CEB was observed; however, compared with those of the control, the peak compressive strength of the CEB samples was greater when the soil was stabilized with 7.5% pulverized waste tire material and 10% cement. A decrease in the water absorption capacity of CEB was observed with an increase in the amount of pulverized waste tire and cement in the soil mixture. The response models corroborate the experimental findings indicating that amount of waste tire rubber residue and cement had significant effects on the CEB performance. This study ensure the beneficial recycling of waste tires blended with cement in soil for making medium-strength CEB to achieve sustainable, resilient masonry construction applications.Graphical

Explainable Ensemble Learning and Multilayer Perceptron Modeling for Compressive Strength Prediction of Ultra-High-Performance Concrete.

The performance of ultra-high-performance concrete (UHPC) allows for the design and creation of thinner elements with superior overall durability. The compressive strength of UHPC is a value that can be reached after a certain period of time through a series of tests and cures. However, this value can be estimated by machine-learning methods. In this study, multilayer perceptron (MLP) and Stacking Regressor, an ensemble machine-learning models, is used to predict the compressive strength of high-performance concrete. Then, the ML model's performance is explained with a feature importance analysis and Shapley additive explanations (SHAPs), and the developed models are interpreted. The effect of using different random splits for the training and test sets has been investigated. It was observed that the stacking regressor, which combined the outputs of Extreme Gradient Boosting (XGBoost), Category Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), and Extra Trees regressors using random forest as the final estimator, performed significantly better than the MLP regressor. It was shown that the compressive strength was predicted by the stacking regressor with an average R2 score of 0.971 on the test set. On the other hand, the average R2 score of the MLP model was 0.909. The results of the SHAP analysis showed that the age of concrete and the amounts of silica fume, fiber, superplasticizer, cement, aggregate, and water have the greatest impact on the model predictions.

Optimizing High-Performance Concrete Properties Containing Blast Furnace Slag and Marble Powder

Abstract Building and industrialization-related environmental harm is becoming an increasingly serious concern. In an effort to create an eco-friendly high-performance concrete (HPC), this paper addresses the idea of partially replacing cement with recyclable industrial waste. The current study will experimentally examine the slump, the strengths of compressive (SC) and porosity (P) of fifteen HPC mixtures manufactured from locally available resources. Hence, the effects of utilizing marble powder (MP) as a mineral additive in binary mixes and ternary with cement (PC) and granulated ground blast furnace slag (GGBFS) on the HPCs properties were studied in order to develop statistical models based on mixture design. Highly accurate prediction graphs and models were created for HPC workability, P at 28-day, SC at 7 and 28-day. All responses have satisfactory coefficients of correlation (R2 ≥ 0.76). Replacing cement with GGBFS causes a rise in slump in mixtures. Nevertheless, that only remains relevant when the mixtures have a small MP percentage (≤ 25%). A minor decrease in SC can be attributed by an increase of GGBFS. After 28-day, using GGBFS alone caused a little drop in SC; however, when GGBFS and PC were mixed, SC increased, in comparison with reference composition, and the porosity was reduced. Conversely, SC is superior with lower porosity when a small amount of MP is utilized. The best combination is HPC14, containing 5% GGBFS; it offers an optimal equilibrium among the three qualities; with HPC4’s (15%GGBFS+5%MP) qualities, being almost identical to those of reference HPC15, a lower amount of cement may also be utilized. Findings encourage the use of MP and GGBFS to partially replace cement to produce eco-friendly and cost-effective HPC. An extremely high correlation coefficient indicated a strong relationship between P and SC.

Assessment of Cement Mortar Strength Mixed with Waste Copper Mine Tailings (CT) by Applying Gradient Boosting Regressor and Grid Search Optimization Machine Learning Approach

Growing environmental concerns and resource scarcity, the construction industry must investigate sustainable materials that reduce waste while improving building material properties. This study investigates the viability of using waste copper mine tailings (CT) as a partial replacement for river sand in cement mortar, assessing the impact on mechanical strength and developing a predictive model using a Gradient Boosting Regressor (GBR) and Grid Search Optimization (GSO). Copper tailings mix designs ranging from 0% to 50% replacement by volume (river sand) were developed, with a constant cement quantity while varying the proportions of sand and tailings. The experiments were carried out at Poornima University in Jaipur, using standard protocols to prepare specimens and measure their compressive strength for 0% CT to 50% CT as volume percent in the mixture. The results showed that the addition of copper tailings up to 20% (3CT2) had highly increased the mortar strength, while mix design 6CT3 showed the best strength at 30% CT. Beyond this threshold, strength declined, thus indicating an optimal replacement level. In the final step of the research, the GBR-GSO-based machine learning approach was employed for developing the predictive model for compressive strength in mortar with different contents of copper tailing for mix types 3CT and 6CT. The predictions obtained by the developed model were in very good agreement with empirical data, thus supporting the potential of machine learning to predict material performance for guiding the use of unconventional additives in construction. It could be said that this work not only represents the material properties of copper-tailing-infused mortar but also showcases state-of-the-art machine learning techniques in the building materials science domain while opening new paths for more innovative and sustainable building practices.