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Fuzzy Logic Methods for Determining the Mechanical Behavior of Masonry Walls

Year 2023, Volume: 03 Issue: 02, 86 - 96, 31.12.2023

Abstract

The mechanical behaviour of the wall, in-situ wall tests and numerical analysis is required along with the material properties. With the support of smart learning techniques, the state of the walls was estimated. This makes it possible to obtain healthy data to support the experiment and modelling. The utilization of mathematical tools like Fuzzy Logic has been demonstrated to be beneficial in resolving intricate engineering issues, without the need to replicate the studied phenomenon, given that the only available information consists of the problem's parameters and desired outcomes. To analyse the wall's behaviour more accurately and quickly, analyses were made using the fuzzy method, one of the smart learning techniques, and compared with the data in the studies in which experimental analysis was applied. The behaviour of the wall, the flexibility and energy capacity were tried to be estimated. In the fuzzy, material parameters and wall load capacities that will affect the properties of the wall are used as inputs. Thirty-five (35) data sets, experiments and modelling data from different studies were taken. Estimation results were compared with empirical results.

References

  • [1] A. Furtado, T. Ramos, H. Rodrigues, A. Arêde, H..Varum, and P. Tavares, “In-plane Response of masonry infill walls: experimental study using digital image correlation”, Procedia Engineering, 114, 870–876. 2015.
  • [2] D. Gautam, and H. Chaulagain, “Structural performance and associated lessons to be learned from world earthquakes in Nepal after 25 April 2015 (MW 7.8) Gorkha earthquake”, Engineering Failure Analysis, 68, 222–243, 2016.
  • [3] P.Usta, N. Morova., Evci, A., and Ergün, S. “Assessment of seismic damage on the exist buildings using Fuzzy Logic”, IOP Conference Series: Materials Science and Engineering, 300(1), 012062. 2018.
  • [4] D. Parisi, A., Calabrese, and G. Serino, “Seismic performance of a Low-Cost base isolation system for unreinforced brick masonry buildings in developing countries”, Soil Dynamics and Earthquake Engineering, 141, 106501, 2021.
  • [5] B. Zengin, B., Toydemir, S., Ulukaya, D., Oktay N. Yüzer and A. Kocak, “Determination of mechanical properties of blend bricks used in historical structures”, 3rd International Conference on Protection of Historical Constructions, Portugal, 2017.
  • [6] G. Vasconcelos and P.B. Lourenço, “Experimental characterization of stone masonry in shear and compression”, Construct Build Material; 23(11):3337–45, 2009.
  • [7] V. Sarhosis and Y. Sheng , “Identification of material parameters for low-bond-strength masonry. Engineering Structures”, 60,100–11.2014.
  • [8] H.R. Eslami Ronagh S.S. Mahini and R. Morshed, “Experimental investigation and nonlinear analysis of historical masonry buildings – A case study”, Construction and Building Materials, 35, 251–260, 2012.
  • [9] S. Aktan and B. Doran., “Constitutive modeling of masonry walls under in-plane loadings. Sigma J-Eng & Nat Sci 7 (2), 165-171, 2016.
  • [10] B. Doran, H. O. Köksal, S. Aktan, S. Ulukaya, D. Oktay and N. Yüzer, “In-Plane shear behavior of traditional masonry walls. International Journal of Architectural Heritage Conservation”, Analysis, and Restoration ISSN: 1558-1558-3066, 2016.
  • [11] M., Tomazevic, M. Lutman, and V. Bosıljkov , “Robustness of hollow clay masonry units and seismic behavior of masonry walls”, Construction and Building Materials, 20 (10): 1028-1039. 2009.
  • [12] O.Onat, P.B. Lourenco and A. Koçak, “Nonlinear analysis of RC structure with massive infill wall exposed to shake table”,Earthquakes and Structures, 10, 811-828, 2016.
  • [13] A. Koçak and M.K. Yildirim, “Effects of infill wall ratio on the period of reinforced concrete framed buildings. Advances in Structural Engineering, 14, 731-743, 2011.
  • [14] F.J. Crisafulli, A.J. Carr, and R. Park, “Analytical modeling of infilled frames structures, A general review. Bulletin of the New Zealand Society for Earthquake Engineering, 33, (1), 30-47, 2000.
  • [15] A. Koçak and M.K. Yildirim Effects of infill wall ratio on the period of reinforced concrete framed buildings”, Advances in Structural Engineering, 14, 731-743,2011.
  • [16] F.J., Crisafulli, A.J. Carr, and R. Park, “Analytical modeling of infilled frames structures, A general review”, Bulletin of the New Zealand Society for Earthquake Engineering, 33, (1). 30-47, 2000.
  • [17] P.G., Asteris, I.P. Giannopoulos, and C.Z. Chrysostomou, “Modeling of infilled frames with openings”, Open Construction and Building Technology Journal, 6, 81-91, 2012.
  • [18] P.G. Asteris and D.M. Cotsovos, “Numerical investigation of the effect of infill walls on the structural response of RC frames”, Open Construction and Building Technology Journal, Bentham Science Publishers, 6, 164-181, 2012.
  • [19] P.G., Asteris, S.T., Antoniou, D.S. Sophianopoulos, and C.Z. Chrysostomou, , “Mathematical macro-modeling of infilled frames: State of the art”, Journal of Structural Engineering, 137 (12), 1508-1517, 2011.
  • [20] C.Z. Chrysostomou, and P.G. Asteris, “On-the in-plane properties and capacities of infilled frames”, Engineering Structures, 41, 385-402, 2012.
  • [21] P.G., Asteris, C.Z., Chrysostomou, Giannopoulos,. and E. Smyrou, “Document masonry infilled reinforced concrete frames with openings”, In: III ECCOMAS thematic conference on computational methods in structural dynamics and earthquake engineering, Corfu, Greece, 2011.
  • [22] M. Tomaževi, “Earthquake -Resistant design of masonry buildings, series on innovation in structures and construction”, Vol 1, Imperial College Press, London, 1999.
  • [23] A. Sayed, A. Essa T., M. R. K Badr and A., H. El-Zanaty, “Effect of infill wall on the ductility and behavior of high-strength reinforced concrete frames”, cHBRC Journal, 10, 258–264, 2014.
  • [24] R.M., Bennett, K.A. Boyd, and R.D. Flanagan, “Compressive properties of structural clay tile prisms”, Journal of Structural Engineering, 123(7): 920-926, 1997.
  • [25] P, Lu, S, Chen and Y., Zheng, “Artificial intelligence in civil engineering”, Math Probl Eng 2012.
  • [26] MA. Shahin “State-of-the-art review of some artificial intelligence applications in pile foundations”, Geosci Front;7(1):33–44. 2016.
  • [27] G. Tayfur, T.K. Erdem and Ö. Kirca , “Strength prediction of high-strength concrete by fuzzy logic and artificial neural networks”, J. Mater. Civil. Eng. 26 (11) 04014079, 2014.
  • [28] Madandoust, R. J.H. Bungey, R. Ghavidel, , “Prediction of the concrete compressive strength by means of core testing using GMDH-type neural network and ANFIS models. Comput. Mater. Sci. 51 (1) 261–272, 2012.
  • [29] A.C. Aydin, A. Tortum, M. Yavuz, , “Prediction of concrete elastic modulus using adaptive neuro-fuzzy inference system. Civ. Eng. Environ. Syst. 23 (4) 295–309, 2006.
  • [30] Z.H. Duan, S.C. Kou, C.S. Poon, , “Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete”, Constr. Build. Mater. 44 524–532, 2013.
  • [31] Ahmadi-Nedushan, B. , “Prediction of elastic modulus of normal and high-strength concrete using ANFIS and optimal nonlinear regression models”, Constr. Build. Mater. 36 665–673, 2012
  • [32] Yuan, Z. Wang, L.N. and Ji, X. , “Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS”, Adv. Eng. Softw, 67 156–163, 2014.
  • [33] Z.H. Duan, S.C. Kou, and C.S. Poon, , “Prediction of compressive strength of recycled aggregate concrete using artificial neural networks”, Constr. Build. Mater,. 40 1200–1206, 2013.
  • [34] A. Öztas, M. Pala, E. Özbay, E. Kanca, N. Çag˘lar, and M.A. Bhatti, , “Predicting the compressive strength and slump of high strength concrete using neural network”, Constr. Build. Mater, 20 (9) 769–775, 2006.
  • [35] L. Bal, F. Buyle-Bodin, , “Artificial neural network for predicting drying shrinkage of concrete”, Constr. Build. Mater, 38 248–254, 2013.
  • [36] R. Parichatprecha and P. Nimityongskul, , “Analysis of durability of high-performance concrete using artificial neural networks”,Constr. Build. Mater, 23 (2) 910–917, 2009.
  • [37] Y. Zhang,. Zhou, G. Xiong, Y. and M. Rafiq, , “Techniques for predicting cracking pattern of masonry wallet using artificial neural networks and cellular automata”, J. Comput. Civ. Eng,24 (2) 161–172, 2010.
  • [38] J. Garzón-Roca, C.O. Marco and Adam, J.M. , “Compressive strength of masonry made of clay bricks and cement mortar: Estimation based on neural networks and fuzzy logic”, Eng. Struct. 48 21–27, 2013.
  • [39] J. Garzón-Roca, J.M. Adam, C. Sandoval, and P. Roca, , “Estimation of the axial behaviour of masonry walls based on Artificial Neural Networks, Comput. Struct. 125 145–152, 2013.
  • [40] V. Plevris, and P.G. Asteris, , “Modeling of masonry failure surface under biaxial compressive stress using neural networks”, Constr. Build. Mater. 55 447–461, 2014.
  • [41] P G Asteris and V. Plevris, “Anisotropic masonry failure criterion using artificial neural networks”, Neural Comput Appl; 28:2207–2229, 2016.
  • [42] Masonry Standards Joint Committee (MSJC) of the Masonry Society. Building code requirements and specification for masonry structures: containing building code requirements for masonry structures (TMS 402–11/ACI 530–11/ASCE 5–11), specification for masonry structures (TMS 602–11/ACI 530.1- 11/ASCE 6–11), and companion commentaries. Reston (VA): American Society of Civil Engineers, p. 236. 2011.
  • [43] Canadian Standards Association (CSA), CSA S304.1-04 (R2010) – Design of Masonry Structures, Canadian Standards Association, Toronto (Ontario), p. 64, 2004.
  • [44] British Standards Institution (BSI), BS EN 1996 (Eurocode 6): Design of Masonry Structures, British Standards Institution, p. 128, 2005.
  • [45] S.R. Sarhat, and E.G. Sherwood, “The prediction of compressive strength of ungrouped hollow concrete block masonry”, Constr. Build. Mater, 58 111–121, 2014.
  • [46] V. Singhal and D. Rai, “Behaviour of Confined Masonry Walls with Openings under In-Plane and Out-of-Plane Loads”, Earthquake Spectra, 34, 2, 817–841, 2018.
  • [47] T-T. Bui , A. Limam and V. Sarhosis, “Failure analysis of masonry wall panels subjected to in-plane and out-of-plane loading using the discrete element method”, European Journal of Environmental and Civil Engineering, 2019.
  • [48] Abdulla K. F. Cunningham L S, Gillie M., , “Simulating masonry wall behaviour using a simplified micro-model approach”,Engineering Structures 151 349–365, 2017.
  • [49] B. Zengin, “Investigating elastic-plastic behaviour of masonry walls by experimental and numerical methods”, Department of Civil Engineering, Ph.D. Thesis, Turkey,2018.
  • [50] LA. Zadeh Fuzzy sets. Inform Control 1965; 8:338–53, 1965.
  • [51] Matlab 7.0. User’s Guide. The MathWorks, Inc., Natick, MA, 2004.
  • [52] T., Takagi, and M. Sugeno “Fuzzy identification of systems and its application to modeling and control. IEEE Transactions on Systems”, Man and Cybernetics. Pp.116-132,1985.

Fuzzy Logic Methods for Determining the Mechanical Behavior of Masonry Walls

Year 2023, Volume: 03 Issue: 02, 86 - 96, 31.12.2023

Abstract

The mechanical behaviour of the wall, in-situ wall tests and numerical analysis is required along with the material properties. With the support of smart learning techniques, the state of the walls was estimated. This makes it possible to obtain healthy data to support the experiment and modelling. The utilization of mathematical tools like Fuzzy Logic has been demonstrated to be beneficial in resolving intricate engineering issues, without the need to replicate the studied phenomenon, given that the only available information consists of the problem's parameters and desired outcomes. To analyse the wall's behaviour more accurately and quickly, analyses were made using the fuzzy method, one of the smart learning techniques, and compared with the data in the studies in which experimental analysis was applied. The behaviour of the wall, the flexibility and energy capacity were tried to be estimated. In the fuzzy, material parameters and wall load capacities that will affect the properties of the wall are used as inputs. Thirty-five (35) data sets, experiments and modelling data from different studies were taken. Estimation results were compared with empirical results.

References

  • [1] A. Furtado, T. Ramos, H. Rodrigues, A. Arêde, H..Varum, and P. Tavares, “In-plane Response of masonry infill walls: experimental study using digital image correlation”, Procedia Engineering, 114, 870–876. 2015.
  • [2] D. Gautam, and H. Chaulagain, “Structural performance and associated lessons to be learned from world earthquakes in Nepal after 25 April 2015 (MW 7.8) Gorkha earthquake”, Engineering Failure Analysis, 68, 222–243, 2016.
  • [3] P.Usta, N. Morova., Evci, A., and Ergün, S. “Assessment of seismic damage on the exist buildings using Fuzzy Logic”, IOP Conference Series: Materials Science and Engineering, 300(1), 012062. 2018.
  • [4] D. Parisi, A., Calabrese, and G. Serino, “Seismic performance of a Low-Cost base isolation system for unreinforced brick masonry buildings in developing countries”, Soil Dynamics and Earthquake Engineering, 141, 106501, 2021.
  • [5] B. Zengin, B., Toydemir, S., Ulukaya, D., Oktay N. Yüzer and A. Kocak, “Determination of mechanical properties of blend bricks used in historical structures”, 3rd International Conference on Protection of Historical Constructions, Portugal, 2017.
  • [6] G. Vasconcelos and P.B. Lourenço, “Experimental characterization of stone masonry in shear and compression”, Construct Build Material; 23(11):3337–45, 2009.
  • [7] V. Sarhosis and Y. Sheng , “Identification of material parameters for low-bond-strength masonry. Engineering Structures”, 60,100–11.2014.
  • [8] H.R. Eslami Ronagh S.S. Mahini and R. Morshed, “Experimental investigation and nonlinear analysis of historical masonry buildings – A case study”, Construction and Building Materials, 35, 251–260, 2012.
  • [9] S. Aktan and B. Doran., “Constitutive modeling of masonry walls under in-plane loadings. Sigma J-Eng & Nat Sci 7 (2), 165-171, 2016.
  • [10] B. Doran, H. O. Köksal, S. Aktan, S. Ulukaya, D. Oktay and N. Yüzer, “In-Plane shear behavior of traditional masonry walls. International Journal of Architectural Heritage Conservation”, Analysis, and Restoration ISSN: 1558-1558-3066, 2016.
  • [11] M., Tomazevic, M. Lutman, and V. Bosıljkov , “Robustness of hollow clay masonry units and seismic behavior of masonry walls”, Construction and Building Materials, 20 (10): 1028-1039. 2009.
  • [12] O.Onat, P.B. Lourenco and A. Koçak, “Nonlinear analysis of RC structure with massive infill wall exposed to shake table”,Earthquakes and Structures, 10, 811-828, 2016.
  • [13] A. Koçak and M.K. Yildirim, “Effects of infill wall ratio on the period of reinforced concrete framed buildings. Advances in Structural Engineering, 14, 731-743, 2011.
  • [14] F.J. Crisafulli, A.J. Carr, and R. Park, “Analytical modeling of infilled frames structures, A general review. Bulletin of the New Zealand Society for Earthquake Engineering, 33, (1), 30-47, 2000.
  • [15] A. Koçak and M.K. Yildirim Effects of infill wall ratio on the period of reinforced concrete framed buildings”, Advances in Structural Engineering, 14, 731-743,2011.
  • [16] F.J., Crisafulli, A.J. Carr, and R. Park, “Analytical modeling of infilled frames structures, A general review”, Bulletin of the New Zealand Society for Earthquake Engineering, 33, (1). 30-47, 2000.
  • [17] P.G., Asteris, I.P. Giannopoulos, and C.Z. Chrysostomou, “Modeling of infilled frames with openings”, Open Construction and Building Technology Journal, 6, 81-91, 2012.
  • [18] P.G. Asteris and D.M. Cotsovos, “Numerical investigation of the effect of infill walls on the structural response of RC frames”, Open Construction and Building Technology Journal, Bentham Science Publishers, 6, 164-181, 2012.
  • [19] P.G., Asteris, S.T., Antoniou, D.S. Sophianopoulos, and C.Z. Chrysostomou, , “Mathematical macro-modeling of infilled frames: State of the art”, Journal of Structural Engineering, 137 (12), 1508-1517, 2011.
  • [20] C.Z. Chrysostomou, and P.G. Asteris, “On-the in-plane properties and capacities of infilled frames”, Engineering Structures, 41, 385-402, 2012.
  • [21] P.G., Asteris, C.Z., Chrysostomou, Giannopoulos,. and E. Smyrou, “Document masonry infilled reinforced concrete frames with openings”, In: III ECCOMAS thematic conference on computational methods in structural dynamics and earthquake engineering, Corfu, Greece, 2011.
  • [22] M. Tomaževi, “Earthquake -Resistant design of masonry buildings, series on innovation in structures and construction”, Vol 1, Imperial College Press, London, 1999.
  • [23] A. Sayed, A. Essa T., M. R. K Badr and A., H. El-Zanaty, “Effect of infill wall on the ductility and behavior of high-strength reinforced concrete frames”, cHBRC Journal, 10, 258–264, 2014.
  • [24] R.M., Bennett, K.A. Boyd, and R.D. Flanagan, “Compressive properties of structural clay tile prisms”, Journal of Structural Engineering, 123(7): 920-926, 1997.
  • [25] P, Lu, S, Chen and Y., Zheng, “Artificial intelligence in civil engineering”, Math Probl Eng 2012.
  • [26] MA. Shahin “State-of-the-art review of some artificial intelligence applications in pile foundations”, Geosci Front;7(1):33–44. 2016.
  • [27] G. Tayfur, T.K. Erdem and Ö. Kirca , “Strength prediction of high-strength concrete by fuzzy logic and artificial neural networks”, J. Mater. Civil. Eng. 26 (11) 04014079, 2014.
  • [28] Madandoust, R. J.H. Bungey, R. Ghavidel, , “Prediction of the concrete compressive strength by means of core testing using GMDH-type neural network and ANFIS models. Comput. Mater. Sci. 51 (1) 261–272, 2012.
  • [29] A.C. Aydin, A. Tortum, M. Yavuz, , “Prediction of concrete elastic modulus using adaptive neuro-fuzzy inference system. Civ. Eng. Environ. Syst. 23 (4) 295–309, 2006.
  • [30] Z.H. Duan, S.C. Kou, C.S. Poon, , “Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete”, Constr. Build. Mater. 44 524–532, 2013.
  • [31] Ahmadi-Nedushan, B. , “Prediction of elastic modulus of normal and high-strength concrete using ANFIS and optimal nonlinear regression models”, Constr. Build. Mater. 36 665–673, 2012
  • [32] Yuan, Z. Wang, L.N. and Ji, X. , “Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS”, Adv. Eng. Softw, 67 156–163, 2014.
  • [33] Z.H. Duan, S.C. Kou, and C.S. Poon, , “Prediction of compressive strength of recycled aggregate concrete using artificial neural networks”, Constr. Build. Mater,. 40 1200–1206, 2013.
  • [34] A. Öztas, M. Pala, E. Özbay, E. Kanca, N. Çag˘lar, and M.A. Bhatti, , “Predicting the compressive strength and slump of high strength concrete using neural network”, Constr. Build. Mater, 20 (9) 769–775, 2006.
  • [35] L. Bal, F. Buyle-Bodin, , “Artificial neural network for predicting drying shrinkage of concrete”, Constr. Build. Mater, 38 248–254, 2013.
  • [36] R. Parichatprecha and P. Nimityongskul, , “Analysis of durability of high-performance concrete using artificial neural networks”,Constr. Build. Mater, 23 (2) 910–917, 2009.
  • [37] Y. Zhang,. Zhou, G. Xiong, Y. and M. Rafiq, , “Techniques for predicting cracking pattern of masonry wallet using artificial neural networks and cellular automata”, J. Comput. Civ. Eng,24 (2) 161–172, 2010.
  • [38] J. Garzón-Roca, C.O. Marco and Adam, J.M. , “Compressive strength of masonry made of clay bricks and cement mortar: Estimation based on neural networks and fuzzy logic”, Eng. Struct. 48 21–27, 2013.
  • [39] J. Garzón-Roca, J.M. Adam, C. Sandoval, and P. Roca, , “Estimation of the axial behaviour of masonry walls based on Artificial Neural Networks, Comput. Struct. 125 145–152, 2013.
  • [40] V. Plevris, and P.G. Asteris, , “Modeling of masonry failure surface under biaxial compressive stress using neural networks”, Constr. Build. Mater. 55 447–461, 2014.
  • [41] P G Asteris and V. Plevris, “Anisotropic masonry failure criterion using artificial neural networks”, Neural Comput Appl; 28:2207–2229, 2016.
  • [42] Masonry Standards Joint Committee (MSJC) of the Masonry Society. Building code requirements and specification for masonry structures: containing building code requirements for masonry structures (TMS 402–11/ACI 530–11/ASCE 5–11), specification for masonry structures (TMS 602–11/ACI 530.1- 11/ASCE 6–11), and companion commentaries. Reston (VA): American Society of Civil Engineers, p. 236. 2011.
  • [43] Canadian Standards Association (CSA), CSA S304.1-04 (R2010) – Design of Masonry Structures, Canadian Standards Association, Toronto (Ontario), p. 64, 2004.
  • [44] British Standards Institution (BSI), BS EN 1996 (Eurocode 6): Design of Masonry Structures, British Standards Institution, p. 128, 2005.
  • [45] S.R. Sarhat, and E.G. Sherwood, “The prediction of compressive strength of ungrouped hollow concrete block masonry”, Constr. Build. Mater, 58 111–121, 2014.
  • [46] V. Singhal and D. Rai, “Behaviour of Confined Masonry Walls with Openings under In-Plane and Out-of-Plane Loads”, Earthquake Spectra, 34, 2, 817–841, 2018.
  • [47] T-T. Bui , A. Limam and V. Sarhosis, “Failure analysis of masonry wall panels subjected to in-plane and out-of-plane loading using the discrete element method”, European Journal of Environmental and Civil Engineering, 2019.
  • [48] Abdulla K. F. Cunningham L S, Gillie M., , “Simulating masonry wall behaviour using a simplified micro-model approach”,Engineering Structures 151 349–365, 2017.
  • [49] B. Zengin, “Investigating elastic-plastic behaviour of masonry walls by experimental and numerical methods”, Department of Civil Engineering, Ph.D. Thesis, Turkey,2018.
  • [50] LA. Zadeh Fuzzy sets. Inform Control 1965; 8:338–53, 1965.
  • [51] Matlab 7.0. User’s Guide. The MathWorks, Inc., Natick, MA, 2004.
  • [52] T., Takagi, and M. Sugeno “Fuzzy identification of systems and its application to modeling and control. IEEE Transactions on Systems”, Man and Cybernetics. Pp.116-132,1985.
There are 52 citations in total.

Details

Primary Language English
Subjects Environmentally Sustainable Engineering, Electrical Engineering (Other)
Journal Section Research Article
Authors

Başak Zengin 0000-0003-3719-9423

Pınar Usta 0000-0001-9809-3855

Özge Onat 0000-0002-4336-0212

Publication Date December 31, 2023
Submission Date December 20, 2023
Acceptance Date December 31, 2023
Published in Issue Year 2023 Volume: 03 Issue: 02

Cite

IEEE B. Zengin, P. Usta, and Ö. Onat, “Fuzzy Logic Methods for Determining the Mechanical Behavior of Masonry Walls”, Researcher, vol. 03, no. 02, pp. 86–96, 2023.

The journal "Researcher: Social Sciences Studies" (RSSS), which started its publication life in 2013, continues its activities under the name of "Researcher" as of August 2020, under Ankara Bilim University.
It is an internationally indexed, nationally refereed, scientific and electronic journal that publishes original research articles aiming to contribute to the fields of Engineering and Science in 2021 and beyond.
The journal is published twice a year, except for special issues.
Candidate articles submitted for publication in the journal can be written in Turkish and English. Articles submitted to the journal must not have been previously published in another journal or sent to another journal for publication.