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Utilization of sustainable materials is a global demand in the construction industry. Hence, this study aims to integrate waste management and artificial intelligence by developing an artificial neural network (ANN) model to predict the compressive strength of green concrete. The proposed model allows the use of recycled coarse aggregate (RCA), recycled fine aggregate (RFA) and fly ash (FA) as partial replacements of concrete constituents.

The model is constructed, trained and validated using python through a set of experimental data collected from the literature. The model’s architecture comprises an input layer containing seven neurons representing concrete constituents and two neurons as the output layer to represent the 7- and 28-days compressive strength. The model showed high performance through multiple metrics, including mean squared error (MSE) of 2.41 and 2.00 for training and testing data sets, respectively.

Results showed that cement replacement with 10% FA causes a slight reduction up to 9% in the compressive strength, especially at early ages. Moreover, a decrease of nearly 40% in the 28-days compressive strength was noticed when replacing fine aggregate with 25% RFA.

The research is limited to normal compressive strength of green concrete with a range of 25 to 40 MPa.

The developed model is designed in a flexible and user-friendly manner to be able to contribute to the sustainable development of the construction industry by saving time, effort and cost consumed in the experimental testing of materials.

Green concrete containing wastes can solve several environmental problems, such as waste disposal problems, depletion of natural resources and energy consumption.

This research proposes a machine learning prediction model using the Python programming language to estimate the compressive strength of a green concrete mix that includes construction and demolition waste and FA. The ANN model is used to create three guidance charts through a parametric study to obtain the compressive strength of green concrete using RCA, RFA and FA replacements.

This paper aims to give an overview about the state of the art and novel technologies used in gas sensing. It also discusses the miniaturization potential of some of these technologies in a comparative way.

In this article, the authors state the most of the methods used in gas sensing discuss their advantages and disadvantages and at last the authors discuss the ability of their miniaturization comparing between them in terms of their sensing parameters like sensitivity, selectivity and cost.

In this article, the authors will try to cover most of the important methods used in gas sensing and their recent developments. The authors will also discuss their miniaturization potential trying to find the best candidate among the different types for the aim of miniaturization.

In this article, the authors will review most of the methods used in gas sensing and discuss their miniaturization potential delimiting the research to a certain type of technology or application.