An Experiment and Study of M30 Grade of Concrete by Partially Replacing Fine Aggregate with Marble Dust Powder and Phosphogypsum in Rigid Pavement

The Project is to study about M30 grade of concrete by adding waste materials. Marble dust powder and phosphogypsum which is easily available marble which are standard among the most imperative materials, utilized as a part of the development business. Marble dust is a waste material from the construction site is mixed with concrete as a replacement material. Marble dust powder is acquired from sawing and moulding of marble rock. Phosphogypsum is produced as an outgrowth of the production of fertilizer from phosphate rock. There is a high gypsum content and gypsum is a widely used material in constructions. It is weakly radioactive in nature because it is a by-product of phosphate fertilizers. In the M30 grade of concrete fine aggregate is partially replaced by marble dust powder and phosphosgypsum in some proportions. The fine aggregate is replaced by 10%, 20% and 30% in which marble dust powder and phosphogypsum and are added in an equal proportion. Keywords: Marble dust powder, phosphogypsum, grade of concrete, rigid pavement, green concrete.

Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques

Silica fume (SF) is a frequently used mineral admixture in producing sustainable concrete in the construction sector. Incorporating SF as a partial substitution of cement in concrete has obvious advantages, including reduced CO2 emission, cost-effective concrete, enhanced durability, and mechanical properties. Due to ever-increasing environmental concerns, the development of predictive machine learning (ML) models requires time. Therefore, the present study focuses on developing modeling techniques in predicting the compressive strength of silica fume concrete. The employed techniques include decision tree (DT) and support vector machine (SVM). An extensive and reliable database of 283 compressive strengths was established from the available literature information. The six most influential factors, i.e., cement, fine aggregate, coarse aggregate, water, superplasticizer, and silica fume, were considered as significant input parameters. The evaluation of models was performed by different statistical parameters, such as mean absolute error (MAE), root mean squared error (RMSE), root mean squared log error (RMSLE), and coefficient of determination (R2). Individual and ensemble models of DT and SVM showed satisfactory results with high prediction accuracy. Statistical analyses indicated that DT models bested SVM for predicting compressive strength. Ensemble modeling showed an enhancement of 11 percent and 1.5 percent for DT and SVM compressive strength models, respectively, as depicted by statistical parameters. Moreover, sensitivity analyses showed that cement and water are the governing parameters in developing compressive strength. A cross-validation technique was used to avoid overfitting issues and confirm the generalized modeling output. ML algorithms are used to predict SFC compressive strength to promote the use of green concrete.

Producing Green Concrete with Plastic Waste and Nano Silica Sand

Industrial and urban development has resulted in the spread of plastic waste and the increase in the emissions of carbon dioxide resulting from the cement manufacturing process. The current research aims to produce green (environmentally friendly) concrete by using plastic waste as coarse aggregates in different proportions (10% and 20%) and nano silica sand powder as an alternative to cement in different proportions (5% and 10% by weight). The results showed that compressive strength decreased by 12.10% and 19.23% for 10% and 20% plastic waste replacement and increased by 12.89% and 20.39% for 5% and 10% silica sand replacement respectively at 28 days. Flexural strength decreased by 12.95% and 19.64% for 10% and 20% plastic waste replacement and increased by 11.16% and 19.86% for 5% and 10% silica sand replacement. Splitting tensile strength decreased by 12.74% and 20.22% for 10% and 20% plastic waste replacement and increased by 10.86% and 19.66% for 5% and 10% silica sand replacement. Dry density decreased by 4.51% and 7.83% for 10% and 20% plastic waste replacement and increased by 2.78% and 4.10% for 5% and 10% silica sand replacement respectively at 28 days.

Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP

Silica fume (SF) is a mineral additive that is widely used in the construction industry when producing sustainable concrete. The integration of SF in concrete as a partial replacement for cement has several evident benefits, including reduced CO2 emissions, cost-effective concrete, increased durability, and mechanical qualities. As environmental issues continue to grow, the development of predictive machine learning models is critical. Thus, this study aims to create modelling tools for estimating the compressive and cracking tensile strengths of silica fume concrete. Multilayer perceptron neural networks (MLPNN), adaptive neural fuzzy detection systems (ANFIS), and genetic programming are all used (GEP). From accessible literature data, a broad and accurate database of 283 compressive strengths and 149 split tensile strengths was created. The six most significant input parameters were cement, fine aggregate, coarse aggregate, water, superplasticizer, and silica fume. Different statistical measures were used to evaluate models, including mean absolute error, root mean square error, root mean squared log error and the coefficient of determination. Both machine learning models, MLPNN and ANFIS, produced acceptable results with high prediction accuracy. Statistical analysis revealed that the ANFIS model outperformed the MLPNN model in terms of compressive and tensile strength prediction. The GEP models outperformed all other models. The predicted values for compressive strength and splitting tensile strength for GEP models were consistent with experimental values, with an R2 value of 0.97 for compressive strength and 0.93 for splitting tensile strength. Furthermore, sensitivity tests revealed that cement and water are the determining parameters in the growth of compressive strength but have the least effect on splitting tensile strength. Cross-validation was used to avoid overfitting and to confirm the output of the generalized modelling technique. GEP develops an empirical expression for each outcome to forecast future databases’ features to promote the usage of green concrete.

A Review in Sustainable Plastic Waste in Concrete

Recently times, industrial development has increased, including plastic industries, and since plastic has a very long analytical life, it will cause environmental pollution. Therefore studies have resorted to reusing recycled plastic waste (sustainable plastic) to produce environmentally friendly concrete (green concrete). In this research, some studies were reviewed and then summarized into several things, including the percentage of plastic replacement from the aggregate and the effect of this percentage on the fresh properties of concrete, such as the workability and the effect of plastic waste on the hardening properties of concrete such as dry density, compressive, tensile and flexural strength.