Compressive Strength Of Concrete Research Articles

Automated estimation of early-age concrete compressive strength using EMI signature-driven deep learning technique

Concrete is a vital construction material for civil infrastructure, whose early-age compressive strength estimation plays a critical role in determining the proper time for formwork removal and the readiness of the structures for service. Electromechanical impedance (EMI) technique has been proven to be a non-destructive and effective method for monitoring concrete early strength gain. Nonetheless, such a technique heavily relies on effective frequency band selection and statistical parameter-based analysis, which may prevent the technique from a real-time operation. This study proposes an EMI signature-driven deep learning (DL) technique to enable the automated estimation of early-age concrete compressive strength. A novel DL framework called multi-scale learning residual denoising network (MLRD-Net) was developed to autonomously learn sensitive features from raw EMI signatures and ultimately estimate the compressive strength. The EMI signatures were collected during the strength development process of concrete specimens produced from two different mix designs based on strength. Two independent databases were constructed from recorded EMI signatures to test the effectiveness of the MLRD-Net. The results demonstrated the excellent performance of the proposed model with coefficients of determination exceeding 0.99 for both databases. In addition, the MLRD-Net shows superior performances over other methods in terms of prediction accuracy, noise resistance and fitness capacities, indicating its great potential for real-time and accurate estimation of the strength gain of early-age concrete.

Predictive Modeling and Experimental Validation for Assessing the Mechanical Properties of Cementitious Composites Made with Silica Fume and Ground Granulated Blast Furnace Slag

Using sustainable cement-based alternatives, such as secondary cementitious raw materials (SCMs), could be a viable option to decrease CO2 emissions resulting from cement production. Previously conducted studies to determine the optimal mix designs of concrete primarily focused on either experimental approaches or empirical modeling techniques. However, in these experimental approaches, few tests could be performed for optimization due to time restrictions and lack of resources, and empirical modeling methods cannot be relied on without external validation. The machine learning-based approaches are further characterized by certain shortcomings, including a smaller number of data points, a less robust connection among the controlling factors, and a lack of comparative analyses among machine learning models. Furthermore, the literature on predicting the performance of concrete utilizing binary SCMs (silica fume (SF) and ground granulated blast furnace slag (GGBS)) is not available. Therefore, to address these drawbacks, this research aimed to integrate ML-based models with experimental validations for accurate predictions of the compressive strength (CS) and tensile strength (TS) of concrete that includes SF and GGBS as SCMs. Three soft computing techniques, namely the ANN, ANFIS, and GEP methods, were used for prediction purposes. Eight major input parameters, including the W/B ratio, cement, GGBS, SF, coarse aggregates, fine aggregates, superplasticizer, and the age of the specimens, were considered for modeling. The validity of the established models was assessed by using external experimental validation criteria, statistical metrics, and performance measures. In addition, sensitivity and parametric analyses were performed. Based on statistical measures, the ANFIS models outperformed other models with higher correlation and lower statistical error values. However, the GEP models exhibited superior performance compared to ANFIS and ANN with respect to the closeness of the RMSE, MAE, RSE, and R2 values between the training, validation, and testing sets for both the CS and TS models. Experimental validation showed strong evidence for the applicability of the proposed models with an R2 of 0.88 and error percentages of less than 10%. Sensitivity and parametric investigations demonstrated that the input variables exhibited the patterns described in the experimental dataset and the available literature. Hence, the proposed models are accurate, have better prediction performance, and can be used for design purposes.

Mechanical Properties of a Sustainable Low-Carbon Geopolymer Concrete Using a Pumice-Derived Sodium Silicate Solution

A geopolymer is an inorganic amorphous cementitious material, emerging as an alternative sustainable binder for greener concrete production over Ordinary Portland Cement (OPC). Geopolymer concrete production promotes waste reuse since the applicable precursor materials include agricultural and industrial waste that requires disposal, helping to reduce waste in landfills and ensuring sustainable environmental protection. This study investigates the development of an environmentally friendly sodium silicate alternative (SSA) derived from pumice powder (PP) in place of a commercial Na2SiO3 solution at a 10 M concentration. Six concrete batches were produced at alkaline/precursor (A/P) ratios of 0.1, 0.2, 0.3, 0.4, and 0.5. The geopolymer mix AF4, with an A/P ratio of 0.4, became the optimum geopolymer concrete design; however, it recorded lower compressive, tensile splitting, and flexural strengths, respectively, against the control OPC concrete. The geopolymer formulations, however, obtained 28-day-hardened concrete densities comparable to the control concrete. The 28-day compressive strength of the OPC concrete was 29.4 MPa, higher than the 18.8 MPa recorded for AF4. However, the 56-day strength of AF4 improved to 22.4 MPa, an around 19% increase compared to the 30.8 MPa achieved by the control mix on day 56, having experienced only a 5% strength increase. The low mechanical performances of the geopolymer formulation could be attributed to extra water added to the original geopolymer design to improve the workability of the geopolymer mix. Therefore, the SSA alkaline solution using PP showed some potential for developing geopolymer concrete for low-strength construction applications.

Bond shear modulus in reinforced concrete at high temperature: A design‐oriented approach

AbstractModeling bond behavior in either ordinary or high‐temperature conditions requires the knowledge of bond shear modulus—called also slip modulus or simply bond stiffness—that has received so far scanty attention because of the greater interest for bond as a guarantee of equilibrium at the Ultimate Limit State (and in fire conditions) than as a means to guarantee both equilibrium and compatibility at the Serviceability Limit State (and in fire/post‐fire conditions). The limited knowledge of bond shear modulus makes it difficult to numerically model such phenomena as tension stiffening, that controls the structural behavior in both ordinary and fire conditions. The general trends identified by examining eleven experimental campaigns with anchored bars covering 27 different cases and temperatures ranging between 20 and 800°C are the starting point of the design‐oriented laws proposed in this study about bond shear modulus as a function of concrete residual strength and temperature. A simple shear‐lag model is introduced for bond shear modulus at room temperature, as its evaluation from test data is no simple matter due to initial chemical adhesion and different test procedures. Bond shear modulus is shown to be a decreasing function of concrete residual compressive strength and of the maximum temperature reached by the bar‐concrete system. Design charts are proposed to allow the designer to identify the value of the bond stiffness on the basis of the max. temperature, of concrete residual strength and of bar diameter, making it possible to realistically model tension stiffening in fire‐damaged RC structures.

Experimental study to compare the strength of concrete with different amounts of polypropylene fibers at high temperatures

It is widely known that adding fibers to concrete improves the properties of concrete, which has a brittle behavior. Although concrete has high compressive strength but poor tensile strength, this has led researchers to offer a variety of ways to deal with this weakness. The use of fibers is one of the methods used to enhance concrete behavior. Fire is one of the most important cases in structures; if the temperature is high or its duration is long, it will cause serious damage to the structure. The present study is an experimental study in which different concrete samples with different amounts of polypropylene fibers and different cement content are exposed once at a normal temperature of 25 °C and again at high temperatures, including 250 °C and 500 °C. The effect of temperature on the compressive and tensile strengths of concrete containing 0.5%, 1.5%, and 2% polypropylene fibres and with cement contents of 200 kg/m3, 260 kg/m3, and 320 kg/m3. The compressive and tensile strength was evaluated at curing 28 days of strength. The results showed a severe strength loss for all concretes after exposure to 500 °C. The relative compressive strengths of concretes containing PP fibers were higher than those of concretes without PP fibers. The tensile strength of concrete was more sensitive to high temperatures than the compressive strength. Based on the test results, it can be concluded that adding 2% PP fibers can significantly promote the residual mechanical properties of concrete during heating. The compressive strength at 25 °C with 2% PP fibres decreases by 43% with cement contents of 200 kg/m3 and 64% with cement contents of 260 kg/m3, and 37% with cement contents of 320 kg/m3, respectively. Also, the compressive strength at 500 °C with 2% PP fibres decreases by 61% with 200 kg/m3, 59% with 260 kg/m3, and 42% with cement contents of 320 kg/m3, respectively.

Impact of fine lightweight aggregates and coal waste on structural lightweight concrete: Experimental study and gene expression programming

Light Expanded Clay Aggregate (LECA) is one of the materials used to make lightweight concrete. In this study, two types of lightweight aggregates including LECA in both coarse and fine aggregate forms and pumice in fine aggregate form have been used to produce lightweight aggregate concrete. Coal waste is also used as partial replacement for cement. The results show that the usage of pumice as partial replacement for sand and LECA as coarse aggregate is applicable and can used for producing structural lightweight aggregate concrete. Furthermore, by using and increasing in the amount of LECA as fine aggregate, the slump of lightweight concrete in comparison to using sand, is also increased. The mixtures with LECA in both fine and coarse aggregate forms have the best results. Also, the results show that adding the coal waste improves the properties of lightweight concrete, and the optimal ratio is about 15% as partial replacement for cement by weight. In addition, gene expression programming is employed to establish empirical connections derived from experimental outcomes. In this regard, a relationship is proposed to determine the compressive strength of lightweight aggregate concrete based on the experimental data. The outcomes of gene expression programming demonstrate that the suggested formula exhibits good accuracy and efficiency in forecasting the specified parameter.

An Experimental Study of Concrete Mixed with Coconut Shell as Partial Replacement of Course Aggregate

The high cost of conventional construction materials affects the economy of structure. The possibility of utilizing recycled coconut shell aggregates in concrete as coarse aggregate is examined in the present study. An optimum percentage replacement of coarse aggregate with coconut shell aggregate is determined from the study. Coconut shell is a lightweight material thus producing lightweight concrete. The replacement of coarse aggregate of coconut shell by 10%, 20% and 30%. The Design mix used is M20 grade and testing of specimens are conducted after 7 and 28 days of curing. The flexural and compressive strength of concrete are tests. The main objective is to encourage the use of these waste products as construction materials in low-cost housing Aggregate is a major ingredient for making concrete, occupying almost 70-80% part of concrete. Conventionally crushed rocks are used as coarse aggregate and river sand as fine aggregate. Both are naturally available materials. Due to the speedy growth of construction works, conventional aggregate sources are exhaust very fast leading to a significant increase in the cost of construction. For sustainable development, these materials should be used wisely and alternative materials need to be searched to replace regular aggregate. A large number of studies have been done to search for alternative materials for the production of concrete. At the same time due to fast industrialization, production of waste material is increasing day by day. Its disposal has become a real problem. It can be concluded that the ideal amount of coconut shell replacement for coarse aggregate for M20 Concrete is 10% -20%. Key Words: Concrete Mix, Coconut shell, Low cost housing, Flexural strength, Compressive strength, Design mix, Replacement of Course Aggregate.

Response Surface Optimization of Rice and Guinea Corn Husk Ash Blended Concrete

Concrete, a fundamental construction material, consists of aggregates, water, cement, and additives. Unfortunately, the large-scale production of cement is a major contributor to carbon dioxide (CO2) emissions, primarily from the manufacturing process and the consumption of fossil fuels. This not only incurs environmental costs associated with global warming but also depletes vital limestone deposits. To mitigate these issues, this study aims to explore the optimal utilization of Guinea Corn Husk Ash (GCHA) and Rice Husk Ash (RHA) in concrete. This research investigated the chemical properties of GCHA and RHA, and their impact on the compressive and split-tensile strengths of concrete when integrated in various proportions. The study reveals that both GCHA and RHA meet the minimum oxide content requirement of 70% set by ASTM C618, with silicon dioxide (SiO2) as the predominant oxide. Increasing the content of RHA and GCHA from 5% to 10% improves the concrete's compressive and split-tensile strengths after curing for 56 days. Optimization results indicate that the ideal mix consists of 10% GCHA, 8.5% RHA, and 82.5% cement, yielding a compressive and split tensile strength of 31.34 N/mm² and 3.07 N/mm² respectively. This study thus offers a promising solution for sustainable concrete production by reducing the environmental footprint of cement while enhancing material properties and promoting an eco-friendlier approach to construction. Keywords: Concrete, Guinea Corn Husk Ash, Rice Husk Ash, Compressive Strength, Split-Tensile Strength

Performance of Self-Compacting Concrete Containing Recycled Aggregates and Recycled Steel Fibers

Since the inception of self-compacting concrete, there has been a growing interest in integrating waste materials into its composition. This study explores the performance of self-compacting concrete and fibered self-compacting concrete, incorporating recycled aggregates sourced from the demolition and crushing of previously tested specimens. Additionally, it investigates the influence of steel fibers, both of commercial origin and those recycled from waste tires. The analysis spans both the fresh and hardened states, encompassing twelve concrete mixtures to assess workability (through measurements such as slump flow, T500, and L-BOX), segregation resistance, compressive strength, and flexural strength. Furthermore, the durability of these concrete mixtures is evaluated by examining mass loss and compressive strength after 56 days of exposure to acidic environments (HCl and H2SO4). The study is organized into three distinct series of concrete mixtures. The first series explores concrete without any fiber additives, focusing on replacing filler limestone with recycled concrete powder and/or substituting coarse aggregates with recycled concrete aggregates. In the second series, commercial fibers are introduced at a dosage of 30 kg/m³. The third series replaces the commercial fibers with recycled fibers with hooked ends. The assessment of the hardened state reveals enhanced mechanical properties in the case of fibered self-compacting concrete (compressive strength increased by more than 9%, and flexural strength increased by more than 8%). Notably, the results highlight that recycled aggregates exhibit improved resistance to HCl acid attack. Interestingly, the replacement of commercial fibers with recycled fibers does not substantially affect the concrete's resistance to acid exposure.

Optimizing sustainable concrete compressive strength prediction: A new particle swarm optimization‐based metaheuristic approach to neural network modeling for circular economy and disaster resilience

AbstractThis study aims to predict the final‐age compressive strengths of sustainable concrete series produced using different aggregate types with high accuracy using an artificial intelligence model and to reduce environmental degradation and conserve rapidly decreasing natural resources within the scope of sustainable development and circular economy goals. For this purpose, 45 different sustainable concrete series containing all‐natural, recycled, natural, and recycled concrete aggregates were produced and subjected to compressive strength tests at 7 and 28 days. In addition, this study also considers whether deep neural network models, which have gained popularity in recent years but have high time and resource costs, or shallow models are more suitable for determining the compressive strength values with high accuracy. For this purpose, a method is proposed that can automatically generate the optimal neural network model using a metaheuristic approach that eliminates the human factor. For this purpose, the proposed method was extensively compared with different classical machine learning algorithms. The proposed method predicted the 7 and 28‐day compressive strength with a coefficient of determination of 0.999 and presented better compressive strength predictions than all other algorithms. Considering the number of buildings to be constructed after earthquakes, the widespread use of concrete, and the importance of compressive strength, the proposed method will likely provide significant gains within sustainable development, circular economy, and disaster risk reduction.

Biaxial compressive failure criteria and constitutive model of high-strength geopolymer concrete after high temperature

This paper studies the effect of high temperatures (20℃, 200℃, 400℃, and 600℃) and stress ratios (σ2/σ3) (0.15, 0.3, 0.45, and 0.60) on biaxial compressive strength and strain development patterns of high-strength geopolymer concrete (HGPC). Experimental observations indicate that HGPC exhibits laminar damage similar to ordinary Portland concrete (OPC) before and after exposure to high temperatures, under biaxial compression. Unlike OPC, damage in HGPC initially passes through coarse aggregates at room temperature but shifts predominantly to the aggregate-mortar interface at 600℃. The biaxial compressive strength envelope of HGPC expands with rising temperatures. The increased multiple of biaxial compressive strength (σ3/fm) for HGPC at room temperature rises and then falls with increasing stress ratios, peaking at a stress ratio of 0.45 and surpassing the values for OPC and high-strength concrete (HSC). Before high temperature, the modulus of elasticity of HGPC is higher than that of OPC and decreases with increasing temperature, decreasing by about 45–60% for every 200°C increase, and the reduction is greater than that of OPC. The existing failure criterion is not suitable for HGPC after high temperature, this paper develops a new biaxial compression failure criterion for HGPC before and after high temperature based on Kupfer's criterion, taking into account the effect of temperature. The proposed failure criterion calculation results are well in agreement with the experimental data. A damage constitutive model for HGPC considering the effect of temperature and lateral pressure is also proposed, combining the Weibull distribution model for the ascending branch and a modified empirical model for the descending branch. This combined model effectively predicts the stress-strain relationship of HGPC under various temperature conditions.

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

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

Prediction of Torque Capacity in Circular Concrete-Filled Double-Skin Tubular Members under Pure Torsion via Machine Learning and Shapley Additive Explanations Interpretation

The prediction of torque capacity in circular Concrete-Filled Double-Skin Tubular (CFDST) members under pure torsion is considered vital for structural design and analysis. In this study, torque capacity is predicted using machine learning (ML) algorithms, such as Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Random Forest (RF), and Decision Tree (DT), which are employed. The interpretation of the results is conducted using Shapley Additive Explanations (SHAPs). The performance of these ML models is evaluated against two traditional analytical formulas that have been proposed and are available in the literature. Through comprehensive analysis, it is shown that superior predictive capabilities are possessed by the CatBoost and XGBoost models, characterized by high R2 values and minimal mean errors. Additionally, insights into the influence of input features are provided by SHAP interpretation, with an emphasis on key parameters such as concrete compressive strength and steel tube dimensions. The gap between empirical models and ML techniques is bridged by this study, offering engineers a more accurate and efficient tool for CFDST structural design. Significant implications for optimizing CFDST column designs and advancing structural engineering practices are presented by these findings. Directions for future research include the further refinement of ML models and the integration of probabilistic analyses for enhanced structural resilience. Overall, the transformative potential of ML and SHAP interpretation in advancing the field of structural engineering is showcased by this study.

PENGGUNAAN BAHAN ADITIF MASTER GLENIUM ACE 8595 UNTUK MEMINIMALISIR POROSITAS PADA BETON

<p><em>Concrete has now become the most essential material in modern construction. The strength and durability of concrete are vital factors that influence the strength and stability of buildings and infrastructure. Porosity refers to the degree to which concrete has voids or voids within its structure. Concrete porosity has a significant impact on concrete compressive strength. The higher the level of porosity, the lower the compressive strength of the concrete. In this research, we conducted a concrete formulation experiment by adding a type F additive to MasterGlenium Ace 8595. This research investigates the effect of adding the additive Master Glenium Ace 8595 on changes in concrete porosity and increasing concrete compressive strength. The results of the research show that from the aggregate suitability testing, it was stated that all the aggregates tested were suitable for use as a building material for concrete, workability testing obtained results with dilute criteria, porosity testing obtained an average porosity of 8.05%, and compressive strength testing obtained an average compressive strength value. -an average of 23.56 MPa indicates an increase of 8.69% of the planned concrete quality. This research concludes that concrete porosity is one of the factors that must be considered to increase the compressive strength of concrete because it significantly influences its density and compressive strength. Keeping the porosity at a low level and the compressive strength of the concrete at a high level can ensure that the concrete structure has good resistance to loads and the environment and reduces the risk of damage and maintenance.</em></p>

Effect of Silica Fume Addition on Mechanical Properties of Concrete in Peat Swamp Water Environment

Hydraulic Portland cement, water, fine and coarse aggregate, and additional ingredients can be added or left out to create concrete. Making and maintaining concrete requires water as a reagent in the cement mixture so that a chemical reaction occurs when it undergoes the hydration process, which is the process where the cement begins to bind the ingredients that make up the concrete and then hardens and forms a solid mass. The need for water in concrete care is to soak it during the hardening process, however, not all types of water can be used. Peat swamp water is water that collects or flows within the peat swamp ecosystem. Peat swamps are a type of swamp formed from organic material accumulated over thousands of years. Silica fume is a material that contains SiO2 greater than 85% and is a very smooth, round material with a diameter of 1/100 the diameter of cement. The swamp water used in this research was taken from Jl. Rappokalling, Tallo District, Makassar City, South Sulawesi. Mix Design uses the Indonesian National Standard (SNI) 03-2834-2000 method with a planned concrete quality (f'c) of 25 MPa. Compressive strength tests were performed on concrete that was 7 and 28 days old for split tensile strength, and on the 28-day old concrete for modulus of elasticity. The concrete's compressive strength ratings after 28 days in variations of immersion in normal water and peat swamp water are 25,842 MPa and 20,749 MPa. From the test results, it was found that the average split tensile strength value of concrete for variations in normal water immersion and peat swamp water was 2.545 MPa and 1.886 MPa. From the results of the modulus of elasticity test, it was found that the average concrete in normal water immersion and peat swamp water variations was 19893.961 MPa, and 17109.75 MPa.

Enhancing Concrete Mechanical Properties through Basalt Fibers and Calcium Sulfate Whiskers: Optimizing Compressive Strength, Elasticity, and Pore Structure.

To study the effects of basalt fibers (BFs), calcium sulfate whiskers (CSWs), and modified calcium sulfate whiskers (MCSWs) on the compressive strength and dynamic modulus of elasticity of concrete, this paper utilizes Mercury Intrusion Porosimetry (MIP) to measure the microstructure of concrete and calculate the fractal dimension of pore surface area. The results indicate that both CSWs and BFs can increase the compressive strength of concrete. CSWs can enhance the dynamic modulus of elasticity of concrete, while the effect of BFs on the dynamic modulus of elasticity is not significant. The improvement in compressive strength and dynamic modulus of elasticity provided by MCSWs is significantly greater than that provided by CSWs. Both CSWs and BFs can effectively improve the pore structure of concrete and have a significant impact on the surface fractal dimension. CSWs inhibit the formation of ink-bottle pores, while BFs increase the number of ink-bottle pores. Due to the ink-bottle pore effect, the fractal dimension of the capillary pore surface is generally greater than three, lacking fractal characteristics. The compressive strength and dynamic modulus of elasticity of concrete have a good correlation with the fractal dimensions of large pores and transition pores.

Influence of the Nature of Cement on the Physical and Mechanical Properties of Soil Concretes from Sandy Clay and Laterite

Soil concrete is a material produced by mixing the soil at the site with a hydraulic binder. This paper aims to study the influence of the nature of binder on the physical and mechanical properties of soil concrete. For the mixtures, three types of soil were chosen and studied: sandy clay with a granular class of 0/5 (SA5), laterite with a granular class of 0/5 (LA5), and laterite with a granular class of 0/10 (LA10). Three different cements were used: CEM I 52.5, CEM II 42.5, and CEM III 32.5, with cement contents of 150 and 250 kg/m3. The soil concretes were designed for a constant spread of 32–33 cm measured on a mini-slump. The results showed that LA5-based soil concrete has a higher water content of about 8.8% more than SA5 and LA10-based soil concretes. For all the mixtures, the lowest porosity values were obtained with CEM III 32.5, followed by CEM I 52.5, and finally CEM II 42.5. For the three types of cement and the same soil granular size, the compressive strength, static, and dynamic modulus of SA5-based soil concretes are higher than LA5. It was noted that the mechanical properties of soil concretes made with CEM III 32.5 are higher than those made with CEM I 52.5 and CEM II 42.5. Regardless of the type of cement used, the mechanical properties obtained on LA10-based soil concrete are higher than those on LA5-based soil concrete.

Predicting the mechanical properties of plastic concrete: An optimization method by using genetic programming and ensemble learners

This study presents a comparative analysis of individual and ensemble learning algorithms (ELAs) to predict the compressive strength (CS) and flexural strength (FS) of plastic concrete. Multilayer perceptron neuron network (MLPNN), Support vector machine (SVM), random forest (RF), and decision tree (DT) were used as base learners, which were then combined with bagging and Adaboost methods to improve the predictive performance. In addition, gene expression programming (GEP) was used to develop computational equations that can be used to predict the CS and FS of plastic concrete. An extensive database containing 357 and 125 data points was obtained from the literature, and the eight most impactful ingredients were used in the model’s development. The accuracy of all models was assessed using several statistical measures, including an error matrix, Akaike information criterion (AIC), K-fold cross-validation, and other external validation equations. Furthermore, sensitivity and SHAP analysis were performed to evaluate input variables' relative significance and impact on the anticipated CS and FS. Based on statistical measures and other validation criteria, GEP outpaces all other individual models, whereas, in ELAs, the SVR ensemble with Adaboost and RF modified with the Bagging technique demonstrated superior performance. SHapley Additive exPlanations (SHAP) and sensitivity analysis reveal that plastic, cement, water, and the age of the specimens have the highest influence, while superplasticizer has the lowest impact, which is consistent with experimental studies. Moreover, GUI and GEP-based simple mathematical correlation can enhance the practical scope of this study and be an effective tool for the pre-mix design of plastic concrete.

The enhancement of engineering characteristics in recycled aggregates concrete combined effect of fly ash, silica fume and PP fiber

In today's world, enhancing the performance of recycled aggregate concrete is an essential need, as it ensures the effective utilization of demolished construction waste. The advantages of fly ash and polypropylene fiber in conventional strength concrete are well known. However Further research is required to evaluate the impact of using fly ash, silica fume and polypropylene fiber in case of RA and its effect on the properties of recycled aggregate concrete (RAC). This study evaluates the effects of using 50%, 75%, and 100% recycled aggregate (RA) and 1%, 2%, and 3% polypropylene fiber with a fixed amount of fly ash and silica fume 87 kg/m3 and 21.75 kg/m3. The objective is to determine how these combinations affect concrete strength and durability properties (compression, tensile strength, acid resistance and water absorption). This study also investigates the reliability of non-destructive tests, ultrasonic pulse velocity and rebound number, in the presence of polypropylene (PP) fibers and fly ash with recycled aggregate. The experimental investigation showed that the combination of RA based concrete, fly ash (FA), and polypropylene fiber (PPF) improved concrete durability and strength. The concrete compressive strength made with 50% RA was 13% lesser than that of control concrete, but with the incorporation 3% PP fiber the same mix achieved 5% higher strength than control mix. On the other hand, the concrete compressive strength made with 75% RA and 100% RA was experimentally found 19% and 22% lower, respectively than control mix. The combinations containing constant fly ash and PPF, in addition to 50% recycled concrete aggregate (RCA), demonstrate greater resistance when exposed to sulfuric acid (H2SO4) environments, in comparison to the control mixture. However, the worst performance is shown by mix (100% RA).

Experimental Study on Partial Replacement of Cement by PROSOPIS JULIFLORA in Concrete

In recent days, the prosopisjuliflora (seemaikaruvelam) develops in a haphazard way in our lands and acts as a major environmental pollutant. In this project our main objective is to study the influence of partial replacement of cement with prosopisjuliflora ash, and to compare it with the compressive strength and workability of ordinary M20 concrete. We are also trying to replace the various percentages like 10%, 15%, and 20% of prosopisjuliflora ash against cement to study the tendency of the Characteristic strength of concrete.