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.

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.

This study presents a life cycle impact assessment of OPC concrete, recycled aggregate concrete, geopolymer concrete, and recycled aggregate-based geopolymer concrete by using the mid-point approach of the CML 2001 impact-assessment method. The life cycle impact assessment was carried out using OpenLCA software with nine different impact categories, such as global warming potential, acidification potential, eutrophication potential, ozone depletion potential, photochemical oxidant formation, human toxicity, marine aquatic ecotoxicity, and freshwater and terrestrial aquatic ecotoxicity potential. Subsequently, a contribution analysis was conducted for all nine impact categories. The analysis showed that using geopolymer concrete in place of OPC concrete can reduce global warming potential by up to 53.7%. Further, the use of geopolymer concrete represents the reduction of acidification potential and photochemical oxidant formation in the impact categories, along with climate change. However, the potential impacts of marine aquatic ecotoxicity, freshwater aquatic ecotoxicity, human toxicity, eutrophication potential, ozone depletion potential, and terrestrial aquatic ecotoxicity potential were increased using geopolymer concrete. The increase in these impacts was due to the presence of alkaline activators such as sodium hydroxide and sodium silicate. The use of recycled aggregates in both OPC concrete and geopolymer concrete reduces all the environmental impacts.

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.

Due to an increase in the number of image achieves, Content-Based Image Retrieval (CBIR) has gained attention for research community of computer vision. The image visual contents are represented in a feature space in the form of numerical values that is considered as a feature vector of image. Images belonging to different classes may contain the common visuals and shapes that can result in the closeness of computed feature space of two different images belonging to separate classes. Due to this reason, feature extraction and image representation is selected with appropriate features as it directly affects the performance of image retrieval system. The commonly used visual features are image spatial layout, color, texture and shape. Image feature space is combined to achieve the discriminating ability that is not possible to achieve when the features are used separately. Due to this reason, in this paper, we aim to explore the low-level feature combination that are based on color and shape features. We selected color moments and color histogram to represent color while shape is represented by using invariant moments. We selected this combination, as these features are reported intuitive, compact and robust for image representation. We evaluated the performance of our proposed research by using the Corel, Coil and Ground Truth (GT) image datasets. We evaluated the proposed low-level feature fusion by calculating the precision, recall and time required for feature extraction. The precision, recall and feature extraction values obtained from the proposed low-level feature fusion outperforms the existing research of CBIR.