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Faculty of Mechanical Engineering and Civil Engineering in Kraljevo, University of Kragujevac , Kraljevo , Serbia
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School of Applied Studies for Information and Communication Technologies, Academy of technical and art applied studies Belgrade, Academy of technical and art applied studies Belgrade , Beograd , Serbia
Faculty of civil engineering and architecture, University of Nis , Nis , Serbia
The application of artificial neural networks (ANNs) has significantly advanced the resolution of complex engineering problems in civil engineering, particularly in data analysis, design, and structural performance prediction. They play a crucial role in materials science by enabling the assessment of mechanical properties of construction materials and structural systems. This paper focuses on the use of ANNs for predicting the compressive strength of masonry walls, a key parameter for reliable structural design. The objective is to provide a systematic and critical review of relevant studies, highlighting the employed neural network models and their predictive accuracy compared to conventional approaches. The findings indicate that ANNs offer more reliable and accurate predictions of compressive strength, especially when experimental data are limited or heterogeneous, and support the development of auxiliary engineering tools and software applications.
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artificial intelligence, neural networks, compressive strength of masonry, masonry structures
This research was supported by the Ministry of Science, Technological Development and Innovation of the Republic of Serbia, under the Agreement on Financing the Scientific Research Work of Teaching Staff at the Faculty of Mechan-ical Engineering and Civil Engineering in Kraljevo, University of Kragujevac - Registration number: 451-03-137/2025-03/200108 and Faculty of Civil Engineering and Architecture, University of Nis - Registration number: 451-03-137/2025-03/200095 dated 04/02/2025. The conducted research is related to the goal Industry, innovation and infra-structure in accordance with the universal strategy of the United Nations (Agenda 2030).
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