Strength Of Rice Husk Ash Research Articles

Prediction model for compressive strength of rice husk ash blended sandcrete blocks using a machine learning models

Prediction model for compressive strength of rice husk ash blended sandcrete blocks using a machine learning models

Influence of the initial setting of cement on the shear strength of rice husk ash stabilised soil

Determination of soil shear strength is essential for engineering construction. Using rice husk ash (RHA) with cement in the soil is a potential ground improvement technique that can reduce environmental problems and construction budget. In the present study, ten combinations of soil-RHA-cement were investigated to understand the effects of RHA with cement on the shear strength parameters of the soil. The admixtures were prepared by taking soil with 5%, 10%, 15% RHA, and 2%, 4%, and 6% cement. All specimens of soil-RHA-cement were tested after the initial setting of cement. The direct shear test was set up to compare different soil-RHA-cement mixtures under three normal stresses of 40, 60, and 80 kPa. The test results showed the difference in each mixture’s shear stress-shear displacement relationship, shear displacement- vertical displacement relationship, shear stress-normal stress relationship, shear strength, cohesion, and internal friction angle. According to the findings, shear strength improved as the cement ratio increased for the same amount of RHA in the soil, while dilatancy reduced compared to untreated soil. All soil-RHA-cement combinations had a stronger cohesiveness than untreated soil, but the angle of internal friction was lower. Under normal stress of 80 kPa, the soil with 5% RHA and 6% cement combination had the highest shear strength, corresponding to 62.9 kPa, and can be suggested for ground development.

Prediction of compressive strength of rice husk ash concrete based on stacking ensemble learning model

By replacing cement in concrete production with rice husk ash (RHA), the amount of cement used and its environmental impact can be reduced. The objective of this study is to accurately determine the compressive strength of rice husk ash (RHA) concrete using a machine learning model. Stacking is an excellent fusion strategy. It uses meta-learner to better learn the prediction results of multiple base learners and improve the performance of the mode. In this research, a stacking ensemble learning-based compressive strength prediction model for rice husk ash (RHA) concrete is developed. The ensemble learning model is the first layer of the stacking model; the linear regression model is the second layer. The optimal configuration of base learners was experimentally determined, and the stacking model was contrasted with other mainstream methods. Using the base learner XGBoost model, the importance of the input feature variables was assessed. The findings reveal that the created stacking ensemble learning model can successfully fuse the prediction outputs of base learners and increase the predictive accuracy of the model. The performance evaluation indices of the established stacking model are as follows: RMSE = 2.344, MAE = 1.764, and R2 = 0. 987. The developed models were compared with previous studies and the model accuracy was better than previous studies. The developed model was applied to the new dataset and the model showed good performance. The cement and age are the two most important parameters impacting the compressive strength of rice husk ash (RHA) concrete.

Predictive modeling of compressive strength of sustainable rice husk ash concrete: Ensemble learner optimization and comparison

One of the largest sources of greenhouse gas (GHG) emissions is the construction concrete industry which has alone 50% of the world's emissions. One possible remedy to mitigate the effect of environmental issues is the use of waste and recycled material in concrete. Today, immense agricultural waste is being used as a substitute for cement in the production of sustainable concrete. Therefore, this study is aimed to predict and develop an empirical formula of the compressive strength of rice husk ash (RHA) concrete using machine learning algorithms. Methods employed in this study includes gene expression programming (GEP) and Random Forest Regression (RFR). A reliable database of 192 data points was employed for developing the models. Most influential variables including age, cement, rice husk ash, water, super plasticizer, and aggregate were employed as input parameters in the development of RHA-based concrete models. Evaluation of models was performed using different statistical parameters. These statistical measures include mean absolute error (MAE), coefficient of determination (R2), performance index (ρ), root man square error (RMSE), relative squared error (RSE) and relative root mean square (RRMSE). The GEP model outperforms the RFR ensemble model in terms of robustness, with a greater correlation of R2 = 0.96 compared to RFR's R2 = 0.91. Ensemble modeling showed an enhancement of 1.62 percent for RFR compressive strength model when compared with individual RFR compressive strength model as illustrated by statistical parameters. Moreover, GEP model shows an enhancement of 37.33 percent in average error with an average error 2.35 MPa as compared to RFR model with average error of about 3.75 MPa. Cross validation was used as external check to avoid overfitting issues of the models and confirm the generalized model output. Parametric analysis was performed to determine the impact of the input parameters on the output. Cement and age were shown to have a substantial impact on the compressive strength of RHA concrete using sensitivity analysis.

Rice Husk Ash-Based Geopolymer Binder: Compressive Strength, Optimize Composition, FTIR Spectroscopy, Microstructural, and Potential as Fire-Retardant Material.

Compressive strength is an important property in construction material, particularly for thermal insulation purposes. Although the insulation materials possess high fire-retardant characteristics, their mechanical properties are relatively poor. Moreover, research on the correlation between fire-retardant and compressive strength of rice husk ash (RHA)-based geopolymer binder (GB) is rather limited. In addition, previous studies on RHA-based GB used the less efficient one-factor-at-a-time (OFAT) approach. In understanding the optimum value and significant effect of factors on the compressive strength, it was deemed necessary to employ statistical analysis and a regression coefficient model (mathematical model). The objective of the study is to determine the effect of different material behavior, namely brittle and ductile, on the compressive strength properties and the optimum material formulation that can satisfy both compressive strength and fire-retardant properties. The factors chosen for this study were the rice husk ash/activated alkaline solution (RHA/AA) ratio and the sodium hydroxide (NaOH) concentration. Compressive strength and fire-retardant tests were conducted as part of the experiments, which were designed and analyzed using the response surface methodology (RSM). The microstructure of geopolymer samples was investigated using a scanning electron microscope (SEM). Results showed that RHA/AA ratio was highly significant (p < 0.000) followed by NaOH concentration (p < 0.024). When the RHA/AA ratio was at 0.7 to 0.8 and the NaOH concentration was between 12 and 14 M, high compressive strength above 28 MPa was recorded. Optimum compressive strength of approximately 47 MPa was achieved when the RHA/AA ratio and NaOH concentration were 0.85 and 14 M, respectively. Brittle samples with low Si/Al ratio of 88.95 were high in compressive strength, which is 33.55 MPa, and showed a high degree of geopolymerization. Inversely, ductile samples showed low compressive strength and degree of geopolymerization. Water content within the geopolymer binder had a major effect on its fire-retardant properties. Semi-ductile GB showed the best fire-retardant properties, followed by semi-brittle and brittle GB. Using RHA as an aluminosilicate source has proven to be a promising alternative.

Impact strength of rice husk ash and basalt fibre based sustainable geopolymer concrete in rigid pavements

In this analysis, the alkaline activators prepared by mixing sodium hydroxide and sodium silicate and rice husk ash (RHA) were used as the source material in geopolymer concrete. As fibre reinforcement in geopolymer concrete, basalt fibres (B.F.s) were used. Silica luxurious RHA and alumina luxurious B.F.s proved the best mishmash for rigid pavement abrasion resistance. The ratio of sodium silicate (sdsi) to sodium hydroxide (sdhd), alkaline activator to binder content was maintained as 2.0 and 0.4 respectively, the concentration of sodium hydroxide was maintained as 14 M, the fluidity of geopolymer concrete was improved by superplasticizer, and RHA used was 500 kg/m3 in MRM (control mix which is rice husk ash based geopolymer concrete without basalt fibres). B.F.s were used as 1, 5, 10, 15, 20, and 25 per cent by weight of RHA in the MB1, MB5, MB10, MB15, MB20 and MB25 combinations, and the results of B.F.'s blends were compared with the MRM. The results showed that, relative to the control mix and all other mixes, the impact strength in mix MB10 was high. In contrast to the MRM control combination, the seven, twenty-eight, and ninety-day impact strength of MB10 showed a substantial increase of more than 88, 89, and 92 per cent, respectively.

Comparison of Machine Learning Approaches with Traditional Methods for Predicting the Compressive Strength of Rice Husk Ash Concrete

Efforts are being devoted to reducing the harmful effect of the construction industry around the globe, including the use of rice husk ash as a partial replacement of cement. However, no method is available to date to predict the compressive strength (CS) of rice husk ash blended concrete (RHAC). In this study, advanced machine learning techniques (artificial neural network, artificial neuro-fuzzy inference system) were used to predict the CS of RHAC. Based on the published literature, six inputs, i.e., age of specimen, percentage of rice husk ash, percentage of superplasticizer, aggregates, water, and amount of cement, were selected. Results obtained from machine learning methods were compared with traditional methods such as linear and non-linear regressions. It was observed that the performance of machine learning methods was superior to traditional methods for determining the CS of RHAC. This study will prove beneficial in minimizing the cost and time of executing laboratory experiments for designing the optimum content portions of RHAC.

Optimization of mechanical properties of rice husk ash concrete using Scheffe’s theory

In this research study, Scheffe’s second degree polynomial based model was adapted for the optimization of the mechanical properties of a five component concrete. Here, rice husk ash which is derived from solid waste and contains high quantity of aluminosilicates which makes it a pozzolanic material is used as the fifth component in the concrete mixture. Applying Scheffe’s statistical simplex methodology, the mixture formulations; a total of ninety concrete cubes of 150 × 150 × 150 mm dimension were produced including the control points used for testing the adequacy of the model. The concrete sample were cured for 28 days before crushing test is carried out to obtain its compressive strength. The maximum compressive strength of 33.45 N/mm2 was achieved with a corresponding mix ratio of 0.60:0.65:1.30:1.60:0.35 for fraction of water, cement, fine aggregate, coarse aggregate and rice husk ash respectively. The Scheffe’s model equation developed is $${\widehat{\mathrm{Y}}=24\mathrm{X}}_{1}+{27.35\mathrm{X}}_{2}+ {33.45\mathrm{X}}_{3}+21{\mathrm{X}}_{4}+ {16.95\mathrm{X}}_{5}+{23.3\mathrm{X}}_{1} {\mathrm{X}}_{2}+{1.1\mathrm{X}}_{1}{\mathrm{X}}_{3}+ {21.4\mathrm{X}}_{1}{\mathrm{X}}_{4}+ 22{.1\mathrm{X}}_{1}{\mathrm{X}}_{5}- 15{.4\mathrm{X}}_{2}{\mathrm{X}}_{3}+ {3.3\mathrm{X}}_{2}{\mathrm{X}}_{4}+ {5.4\mathrm{X}}_{2}{\mathrm{X}}_{5}- {16.9\mathrm{X}}_{3}{\mathrm{X}}_{4}-{23.8\mathrm{X}}_{3}{\mathrm{X}}_{5}- {3.9\mathrm{X}}_{4}{\mathrm{X}}_{5}$$ . The formulated mathematical model was validated and a suitable relationship was observed between the experimental and predicted values based on the outcomes of student t test and analysis of variance. The model can predict the compressive strength of rice husk ash blended cement concrete so as to inform early decision making when used as construction material.

Compressive strength of rice husk ash based geopolymer: The effect of alkaline activator

In this paper, the effect of alkali activator to binder (AAB) ratio and molarity of alkaline activator on the rice husk ash based geopolymer were investigated. Compressive strength has been determined on 70.6 × 70.6 × 70.6 mm specimens by varying AAB ratio from 0.5 to 0.7 and molarity of alkali activator solution from 12 M to 16 M. The microstructure was also examined using scanning electron microscope (SEM). Experimental results revealed that the maximum compressive strength is obtained up to 39.95 N/mm2 after 28 days. It has been observed that compressive strength is directly proportional to both AAB ratio and molarity of alkali activator solution. With increasing the molarity, microstructure become quite dense, this may be attributed to high degree of geopolymerisation.

Shell Mould Strength of Rice Husk Ash (RHA) and Bentonite Clays in Investment Casting

Investment casting process (IC) plays a major role in the modern manufacturing process in providing an economical means of mass production components with intricate shape and complex geometry as demand in various crucial applications including aerospace, automotive, military, biomedical and others. This casting technique, develop shell mould fabrication by coating the required pattern with a refractory mixture which offers the complex geometrical shape and sizes parts to be cast. However, the modern IC approach in shell mould production suffers from zircon’s cost and supply instability as it is the main material to be used. Zircon uses as refractory filler for slurry production, and also in the form of sand used as stucco particles, is favoured by the investment casting facilities and industries as it exhibits the most versatile properties such as low thermal expansion and low reactivity to the metal to be cast. During the period of zircon supply shortage, many facilities introduce several alternatives. Currently, the step taken to reduce the cost of primary slurry material is by using some alternate refractory material like, alumina, silica, to be used with zircon for shell mould production. In relation to that, several researches continue to search for alternatives approach for shell mould materials. This research introduces the alternative method in fabricating investment casting shell mould as recognized from investment casting industry located in Sungai Puar of Bukittingi Padang Indonesia. This industry employs several local resources to fabricate the shell mould. These materials consist of rice husk ash (RHA), and two types of bentonite clays. The bentonite clays were obtained nearly from Kota Payakumbuh in the western provinces. However, this industry suffers from weak shell mould strength and need to be investigated and consulted. In this paper, the investigation on shell mould strength made from rice husk ash (RHA), and bentonite clays were conducted. The strength was measured by its modulus of rupture (MOR) performed in 3 points flexural bending test. The green and fired shell mould strength was determined from five type of slurry composition. The results revealed that the highest green and fired strength obtained were 0.157 MPa and 0.361 MPa from shell mould sample C of RHA (46%) and bentonite (54%) of its composition.

Particle size effect on the strength of rice husk ash blended gap-graded Portland cement concrete

Abstract Rice husk ash (RHA) has been used as a highly reactive pozzolanic material to improve the microstructure of the interfacial transition zone (ITZ) between the cement paste and the aggregate in high-performance concrete. Mechanical experiments of RHA blended Portland cement concretes revealed that in addition to the pozzolanic reactivity of RHA (chemical aspect), the particle grading (physical aspect) of cement and RHA mixtures also exerted significant influences on the blending efficiency. The relative strength increase (relative to the concrete made with plain cement, expressed in %) is higher for coarser cement. The gap-grading phenomenon is expected to be the underlying mechanism. This issue is also approached by computer simulation. A stereological spacing parameter (i.e., mean free spacing between mixture particles) is associated with the global strength of the blended model cement concretes. This paper presents results of a combined mechanical and computer simulation study on the effects of particle size ranges involved in RHA-blended Portland cement on compressive strength of gap-graded concrete in the high strength/high performance range. The simulation results demonstrate that the favourable results for coarser cement (i.e., the gap-graded binder) reflect improved particle packing structure accompanied by a decrease in porosity and particularly in particle spacing.