Artificial Intelligence Neural Network Compressive Strength Prediction Of Recycled Aggregate Concrete Samples

Artificial Intelligence Neural Network: Compressive Strength Prediction of Recycled Aggregate Concrete Samples

Old and demolished structures profusely exist in landfills because they are not being recycled frequently nor being employed correctly. This leads to an increase of construction and demolished wastes (C&D). These demolished structures and blocks can be broken down into smaller components to serve as aggregates (which are called recycled aggregates). Recycled aggregates are not being used regularly because they sometimes have detrimental influence on the compressive strength of concrete. Recycled concrete aggregate (RCA) reduces compressive strength of the concrete samples due to absorption issues related to the type, and age of the old concrete. Increase in water absorption levels leads to reduction in the compressive strength. If this issue is resolved, consumption of natural resources would decrease, and the use of recycled aggregate would increase which has beneficial reflection on the economy and the environment. The objective of this research was to develop a model to predict the compressive strength of concrete containing different percentages of RCA. This research studied the physical properties that reduce compressive strength, and even included the parameters that are aligned to the concrete mixture and treated them as input parameters in a prediction model. The model was created using artificial intelligence neural network. The built model included a specific prediction algorithm which was Bayesian Regularization Backpropagation which can deal with many types of data, even those of the random type. Although, the data was considered as non-linear, the Bayesian probability algorithm was able to determine the pattern between the data and reduce the error by using the error function which was Mean Squared Error. The experimental data was collected from previous published research works in literature. The collection of the data and the evaluation of the model were both built upon specific criteria. The training results showed the success of the model. The model can be used as a tool by engineers to calculate compressive strength when recycled aggregates are added by entering the physical properties of the mixture. The work done here can be extended in the future to cover optimization of mechanical properties of concrete containing RCA.

2nd International Conference on Smart Sustainable Materials and Technologies (ICSSMT 2023)

Sustainable materials science and engineering is one of the important characteristics of the existing high-tech revolution. The advances of materials science pave way for technical advancements in materials science and industrial technologies throughout the world. Materials are regarded as critical component in all emerging industries. Exquisite preparation and manufacturing must be carried out before a new material may be used. Nevertheless, electronic materials are undeniably important in many aspects of life. Smart materials and structures is a multi-disciplinary platform dedicated to technical advances in smart materials, systems and structures, including intelligent materials, sensing and actuation, adaptive structures, and active control. Recently, sustainable materials and technologies reshape the electronics industry to build realistic applications. At present, without the impact of sustainability, the electronics industry faces challenges. Researchers are now more focused on understanding the fundamental science of nano, micro, and macro-scale aspects of materials and technologies for sustainable development with a special attention toward reducing the knowledge gap between materials and system designs. The main aim of this international conference is to address the new trends on smart sustainable materials field for industrial and electronics applications. The main purpose of this conference is to assess the recent development in the applied science involving research activity from micro- to macro-scale aspects of materials and technologies for sustainable applications. In such a context, particular emphasis is given to research papers tailored in order to improve electronic and industrial applications and market extension of sustainable materials.

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