Strength assessment of structural masonry walls: analysis based on machine learning approaches

References

• Hendry EAW. Masonry walls: materials and construction. Constr Build Mater. 2001;15(8):323–330. doi: 10.1016/S0950-0618(01)00019-8
   Web of Science ®Google Scholar
• Murty CVR, Jain SK, Beneficial influence of masonry infill walls on seismic performance of RC frame buildings, in: 12th World Conference on Earthquake Engineering, Auckland, New Zealand, 2000: pp. 1–6. [cited 2022 Apr 30]. https://www.iitk.ac.in/nicee/wcee/article/1790.pdf
   Google Scholar
• Sarangapani G, Venkatarama Reddy BV, Jagadish KS. Brick-mortar bond and masonry compressive strength. J Mater Civ Eng. 2005;17(2):229–237. doi: 10.1061/(ASCE)0899-1561(2005)17:2(229)
   Web of Science ®Google Scholar
• Franzoni E, Gentilini C, Graziani G, et al. Compressive behaviour of brick masonry triplets in wet and dry conditions. Constr Build Mater. 2015;82:45–52. doi: 10.1016/j.conbuildmat.2015.02.052
   Web of Science ®Google Scholar
• Celano T, Argiento LU, Ceroni F, et al. In-plane behaviour of masonry walls: numerical analysis and design formulations. Materials. 2021;14(19):5780. doi: 10.3390/ma14195780
   PubMed Web of Science ®Google Scholar
• Abdulla KF, Cunningham LS, Gillie M. Simulating masonry wall behaviour using a simplified micro-model approach. Eng Struct. 2017;151:349–365. doi: 10.1016/j.engstruct.2017.08.021
   Web of Science ®Google Scholar
• Mohammed A, Hughes TG, Mustapha A. The effect of scale on the structural behaviour of masonry under compression. Constr Build Mater. 2011;25(1):303–307. doi: 10.1016/j.conbuildmat.2010.06.025
   Web of Science ®Google Scholar
• ACI 530/530.1-13. Building code requirements and specification for masonry structures. American Concrete Institute; 2013. p. 319.
   Google Scholar
• Keshava M, Raghunath SR. Experimental investigations on axially and eccentrically loaded masonry walls. J Inst Eng India Ser A. 2017;98(4):449–459. doi: 10.1007/s40030-017-0222-2
   Google Scholar
• Perez Gavilan JJ, Flores LE, Alcocer SM. An experimental study of confined masonry walls with varying aspect ratios. Earthq Spectra. 2015;31(2):945–968. doi: 10.1193/090712EQS284M
   Web of Science ®Google Scholar
• Kaushik HB, Rai DC, Jain SK. Stress-strain characteristics of clay brick masonry under uniaxial compression. J Mater Civ Eng. 2007;19(9):728–739. doi: 10.1061/(ASCE)0899-1561(2007)19:9(728)
   Web of Science ®Google Scholar
• Corradi M, Borri A, Vignoli A. Experimental study on the determination of strength of masonry walls. Constr Build Mater. 2003;17(5):325–337. doi: 10.1016/S0950-0618(03)00007-2
   Web of Science ®Google Scholar
• Doddamani D, Keshava M. AAC block masonry with ready mix mortar—an experimental and numerical analysis. In: Rao A Ramanjaneyulu K, editors. Recent advances in structural engineering. Lecture Notes in Civil Engineering, Volume 1, Vol. 11. Singapore: Springer; 2019. p. 681–692. doi: 10.1007/978-981-13-0362-3_55
   Google Scholar
• Reboul N, Mesticou Z, Si Larbi A, et al. Experimental study of the in-plane cyclic behaviour of masonry walls strengthened by composite materials. Constr Build Mater. 2018;164:70–83. doi: 10.1016/j.conbuildmat.2017.12.215
   Web of Science ®Google Scholar
• Belghiat C, Messabhia A, Plassiard J-P, et al. Experimental study of double-panel confined masonry walls under lateral loading. J Buil Eng. 2018;20:531–543. doi: 10.1016/j.jobe.2018.09.001
   Web of Science ®Google Scholar
• James J, Pandian PK, Deepika K, et al. Cement stabilized soil blocks admixed with Sugarcane Bagasse Ash. JF Eng. 2016;2016:1–9. doi: 10.1155/2016/7940239
   Google Scholar
• Sajanthan K, Balagasan B, Sathiparan N. Prediction of compressive strength of stabilized earth block masonry. Adv Civil Eng. 2019;2019:1–13. doi: 10.1155/2019/2072430
   Web of Science ®Google Scholar
• Lulić L, Lukačević I, Skejić D, et al. Assessment of existing masonry resistance using partial factors approaches and field measurements. Buildings. 2023;13(11):2790. doi: 10.3390/buildings13112790
   Web of Science ®Google Scholar
• Drougkas A, Roca P, Molins C. Numerical prediction of the behavior, strength and elasticity of masonry in compression. Eng Struct. 2015;90:15–28. doi: 10.1016/j.engstruct.2015.02.011
   Web of Science ®Google Scholar
• Prakash PR, Azenha M, Pereira JM, et al. Finite element based micro modelling of masonry walls subjected to fire exposure: framework validation and structural implications. Eng Struct. 2020;213:110545. doi: 10.1016/j.engstruct.2020.110545
   Web of Science ®Google Scholar
• Srinivas V, Sasmal S. Experimental and numerical studies on ultimate load behaviour of brick masonry. J Inst Eng India Ser A. 2016;97(2):93–104. doi: 10.1007/s40030-016-0152-4
   Google Scholar
• D’Altri AM, Sarhosis V, Milani G, et al. Modeling strategies for the computational analysis of unreinforced masonry structures: review and classification. Arch Computat Methods Eng. 2020;27(4):1153–1185. doi: 10.1007/s11831-019-09351-x
   Web of Science ®Google Scholar
• Keshava M, Behaviour of masonry under axial, eccentric and lateral loading [ Ph.D. Thesis]. VTU, Belagavi; 2012.
   Google Scholar
• Pekmezci BY, Polat Pekmezci I. Development of shear strength index test probe: its application on historic structures. Int J Build Pathol Adapt. 2022;40(5):693–711. doi: 10.1108/IJBPA-10-2020-0089
   Web of Science ®Google Scholar
• Friedman JH. Stochastic gradient boosting. Comput Stat Data Anal. 2002;38(4):367–378. doi: 10.1016/S0167-9473(01)00065-2
   Web of Science ®Google Scholar
• Natekin A, Knoll A. Gradient boosting machines, a tutorial. Front Neurorobot. 2013;7:21. doi: 10.3389/fnbot.2013.00021
   PubMed Web of Science ®Google Scholar
• Elman JL. Finding structure in time. Cognit Sci. 1990;14(2):179–211. doi: 10.1207/s15516709cog1402_1
   Web of Science ®Google Scholar
• Guanghua R, Cao Y, Wen S, et al. A modified Elman neural network with a new learning rate scheme. Neurocomputing. 2018;286:11–18. doi: 10.1016/j.neucom.2018.01.046
   Web of Science ®Google Scholar
• Friedman JH. Multivariate adaptive regression splines. Ann Stat. 1991;19(1):1–67. doi: 10.1214/aos/1176347963
   Web of Science ®Google Scholar
• Hastie T, Tibshirani R, Friedman J. 2009. The elements of statistical learning, Springer series in statistics. second ed. NY: Springer. doi: 10.1007/978-0-387-84858-7
   Google Scholar