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Artificial neural network prediction of window openings and positions in reinforced concrete infilled frames with pneumatic interface

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Abstract

Energy consumption is consecutively increasing nowadays in residential sectors. So as to overcome this issue, window openings are provided in the masonry units. The window openings will diminish the energy consumption in the buildings. The increase in opening size will directly improves the ventilation and enrich in the comfort of the buildings. However, as the size of the window increases, the thermal comfort is improved but, the structural properties of the masonry unit will be diminished. To rectify this current issue, the window opening size has been optimized to improve the structural properties without compromising the thermal aspects. The percentage of window opening sizes are 10%, 20%, 30% up to 100% from the masonry infill area. The window openings are made in single bay single storey reinforced concrete Infilled frame model. The static lateral loading is applied to the RC Infilled frame. A finite element analysis software was used for analysing this issue. By analysing the models, the window opening size of 50% from the masonry infill attains a better performance when comparing all the other models. The soft computing approaches using ANN modelling to predict the Stiffness of the frame. The ANN results indicate R2 values of 0.98466 in 0%, 0.9674 in 20%, 0.9762 in 40%, 0.94987 in 60%, 0.95577 in 80% and 0.99421 in 100% in every instance, ANN model is a highly effective prediction of stiffness of the frame with openings. Therefore, the ANN model is the most effective machine-learning algorithm for the stiffness of the frame.

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Data availability

The datasets generated and analysed during the current study are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Research Scholar, Department of Civil Engineering, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, 603 203, Tamil Nadu, India

    G. Prem Kumar

  2. Associate Director-Campus Life (Adjunct Faculty), Department of Civil Engineering, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, 603 203, Tamil Nadu, India

    V. Thirumurugan

  3. Department of Civil Engineering, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, 603 203, Tamil Nadu, India

    K. S. Satyanarayanan

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  1. G. Prem Kumar
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GPK Conceptualization, Methodology, Data curation, original draft Writing. VT Conceptualization, Methodology, Data curation. KSS Conceptualization, original draft Writing.

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Correspondence to G. Prem Kumar.

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Kumar, G.P., Thirumurugan, V. & Satyanarayanan, K.S. Artificial neural network prediction of window openings and positions in reinforced concrete infilled frames with pneumatic interface. Asian J Civ Eng 24, 1915–1925 (2023). https://doi.org/10.1007/s42107-023-00611-7

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