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S. Czarnecki¹, A.D.R. Troncoso García², K. Nyarko³, M. Hadzima-Nyarko⁴ and F. Martinez Alvarez²

¹Department of Materials Engineering and Construction Processes, Wroclaw University of Science and Technology, Poland
², Universidad Pablo de Olavide, Spain
³Faculty of Electrical Engineering, Computer Science and Information Technology, Josip Juraj Strossmayer, University of Osijek, Croatia
⁴Faculty of Civil Engineering and Architecture, Josip Juraj Strossmayer, University of Osijek, Croatia

S. Czarnecki, A.D.R. Troncoso García, K. Nyarko, M. Hadzima-Nyarko, F. Martinez Alvarez, "A Machine Learning Approach for Predicting the Compressive Strength of Masonry Walls: An Artificial Neural Network Model", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Eighteenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 10, Paper 5.4, 2025,

Keywords: masonry walls, artificial neural network, compressive strength, mortar, masonry unit, machine learning.

Abstract

Masonry walls have long been a critical element in construction due to their durability, fire resistance and aesthetic value. With the increasing integration of advanced technologies like artificial intelligence (AI) and machine learning (ML) in the construction industry, this study aims to propose a simple yet reliable model for evaluating the compressive strength of masonry walls using ML techniques. A larger data set was constructed for numerical analysis, expanding on previous data availability. The results indicate that the artificial neural network model achieved very satisfactory values of the accuracy parameters, demonstrating the reliability of the model. These findings suggest that ANN, can provide accurate predictions for the compressive strength of masonry walls, offering a promising tool for improving design and construction practices. The adoption of such models can significantly enhance the efficiency, safety, and cost-effectiveness of masonry wall assessments in the future.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 10 PROCEEDINGS OF THE EIGHTEENTH INTERNATIONAL CONFERENCE ON CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING COMPUTING Edited by: P. Iványi, J. Kruis and B.H.V. Topping Paper 5.4 A Machine Learning Approach for Predicting the Compressive Strength of Masonry Walls: An Artificial Neural Network Model S. Czarnecki¹, A.D.R. Troncoso García², K. Nyarko³, M. Hadzima-Nyarko⁴ and F. Martinez Alvarez² ¹Department of Materials Engineering and Construction Processes, Wroclaw University of Science and Technology, Poland ², Universidad Pablo de Olavide, Spain ³Faculty of Electrical Engineering, Computer Science and Information Technology, Josip Juraj Strossmayer, University of Osijek, Croatia ⁴Faculty of Civil Engineering and Architecture, Josip Juraj Strossmayer, University of Osijek, Croatia Full Bibliographic Reference for this paper S. Czarnecki, A.D.R. Troncoso García, K. Nyarko, M. Hadzima-Nyarko, F. Martinez Alvarez, "A Machine Learning Approach for Predicting the Compressive Strength of Masonry Walls: An Artificial Neural Network Model", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Eighteenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 10, Paper 5.4, 2025, Keywords: masonry walls, artificial neural network, compressive strength, mortar, masonry unit, machine learning. Abstract Masonry walls have long been a critical element in construction due to their durability, fire resistance and aesthetic value. With the increasing integration of advanced technologies like artificial intelligence (AI) and machine learning (ML) in the construction industry, this study aims to propose a simple yet reliable model for evaluating the compressive strength of masonry walls using ML techniques. A larger data set was constructed for numerical analysis, expanding on previous data availability. The results indicate that the artificial neural network model achieved very satisfactory values of the accuracy parameters, demonstrating the reliability of the model. These findings suggest that ANN, can provide accurate predictions for the compressive strength of masonry walls, offering a promising tool for improving design and construction practices. The adoption of such models can significantly enhance the efficiency, safety, and cost-effectiveness of masonry wall assessments in the future. download the full-text of this paper (PDF, 12 pages, 726 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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