Concrete by Preplaced Aggregate Method Using Silica Fume and Polypropylene Fibres.

Preplaced aggregate concrete (PAC) is prepared in two steps, with the coarse aggregate being initially laid down in the formwork, after which a specialised grout is injected into it. To enhance the properties of concrete and to reduce the emission of CO2 produced during the production of cement, supplementary cementitious materials (SCMs) are used to partially substitute ordinary Portland cement (OPC). In this study, 100 mm × 200 mm (diameter x height) PAC cylinders were cast with 10 per cent of cement being substituted with silica fume; along with that, 1.5% dosage of Macro polypropylene fibres were also introduced into the coarse aggregate matrix. Compressive strength test, splitting tensile strength test, mass loss at 250 °C, and compressive strength at 250 °C were performed on the samples. PAC samples with 10% of cement replaced with Silica Fume (SPAC) were used as control samples. The primary objective of this study was to observe the effect of the addition of Polypropylene fibres to PAC having Silica fume as SCM (FRPAC). The aforementioned tests showed that FRPAC had a lower compressive strength than that of the control mix (SPAC). FRPAC had greater tensile strength than that of NPAC and SPAC. Mass loss at 250 °C was greater in SPAC compared to FRPAC. The compressive strength loss at 250 °C was significantly greater in FRPAC compared to SPAC. FRPAC exhibited a greater strain for the applied stress, and their stress-strain curve showed that FRPAC was more ductile than SPAC.

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.

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.

Design of experiment based statistical approaches to optimize submerged arc welding process parameters.

Joining of metals is very useful concept which is being utilized since Bronze Age and then gradual advancement gave rise to development of modern welding. And now welding is increasingly used in the fields of fabrication, manufacturing and construction. But productivity is the main concern in many manufacturing and industrial welding applications. Therefore selection of a welding process and its variables/parameters without sacrificing weld quality with respect to productivity and its quality is very important because an optimum blend of parameters which inevitably develop minimum or no defect will result in high productivity. For this study Submerge Arc Welding (SAW) process is selected for optimization because this versatile welding process is the first choice whenever good productivity with high quality requires in fabrication and manufacturing of Marine & pressure vessels, pipelines and offshore structures. Here Signal to noise (S/N) ratio analyses are used to find significant effects of key parameters on selected responses and then for optimization design of experiment based both quality loss function (OFM) and desirability function along with variance analyses by ANOVA are utilized. Moreover codes and standards provide a range for weld process parameters but author experienced that still there is a window to further optimize these parameters to produce the quality weld. Therefore this study is also useful to contribute in welding related research work by enhancing the knowledge of welding process and its analysis by utilizing advance statistical optimization techniques to find optimum zone within the acceptable zone from Code & Standard based tolerance Zone.