Elimination of a measurement problem: A robust prediction model for missing eigenvector value to assess earthquake induced out-of-plane failure of infill wall

Ranković, 2014, Development of support vector regression identification model for prediction of dam structural behaviour, Struct. Saf., 48, 33, 10.1016/j.strusafe.2014.02.004 Salazar, 2015, An empirical comparison of machine learning techniques for dam behaviour modelling, Struct. Saf., 56, 9, 10.1016/j.strusafe.2015.05.001 Onat, 2019, Fundamental vibration frequency prediction of historical masonry bridges, Struct. Eng. Mech., 69, 155 Yön, 2019, Earthquake damages on non-structural elements of reinforced concrete buildings during 2011 Van seismic sequence, J. Perform. Constr. Facil., 10.1061/(ASCE)CF.1943-5509.0001341 Onat, 2018, Application of Artificial Neural Networks to the prediction of out-of-plane response of infill walls subjected to shake table, Smart Struct. Syst., 21, 521 Onat, 2015, Experimental and numerical analysis of RC structure with two leaf cavity wall subjected to shake table, Struct. Eng. Mech., 55, 1037, 10.12989/sem.2015.55.5.1037 Onat, 2016, Nonlinear analysis of RC structure with massive infill wall exposed to shake table, Earthquake Struct., 10, 811, 10.12989/eas.2016.10.4.811 Lourenço, 2016, Shaking table testing for masonry infill walls: unreinforced versus reinforced solutions, Earthquake Eng. Struct. Dyn., 45, 2241, 10.1002/eqe.2756 Onat, 2017, Structural model calibration of RC structure with two-leaf cavity brick infill wall by deterministic approach, Građevinar, 69, 171 Varela-Rivera, 2011, Out-of-plane behaviour of confined masonry walls, Eng. Struct., 33, 1734, 10.1016/j.engstruct.2011.02.012 Varela-Rivera, 2012, Confined masonry walls subjected to combined axial loads and out-of-plane uniform pressures, Can. J. Civ. Eng., 39, 439, 10.1139/l2012-021 Furtado, 2016, Experimental evaluation of out-of-plane capacity of masonry infill walls, Eng. Struct., 111, 48, 10.1016/j.engstruct.2015.12.013 Onat, 2018, Assessment of the combined in-plane and out-of-plane behavior of brick infill walls within reinforced concrete frames under seismic loading, Earthquake Eng. Struct. Dyn., 47, 2821, 10.1002/eqe.3111 Chiauzzi, 2012, Estimate of fundamental period of reinforced concrete buildings: code provisions vs. experimental measures in Victoria and Vancouver (BC, Canada) Onat, 2019, Experimental damage evaluation of prototype infill wall based on forced vibration test, Adv. Concr. Constr. Masi, 2010, Experimental and numerical evaluation of the fundamental period of undamaged and damaged RC framed buildings, Bull. Earthquake Eng., 8, 643, 10.1007/s10518-009-9136-3 Eleftheriadou, 2012, Fundamental period versus seismic damage for reinforced concrete buildings Ditommaso, 2013, Evaluation and considerations about fundamental periods of damaged reinforced concrete buildings, Nat. Hazards Earth Syst. Sci., 13, 10.5194/nhess-13-1903-2013 Vidal, 2014, Changes in dynamic characteristics of Lorca RC buildings from pre-and post-earthquake ambient vibration data, Bull. Earthquake Eng., 12, 2095, 10.1007/s10518-013-9489-5 Chang, 2018, Applications of neural network models for structural health monitoring based on derived modal properties, Measurement, 129, 457, 10.1016/j.measurement.2018.07.051 Eurocode 8, “Eurocode 8: Design of structures for earthquake resistance-part 1: general rules, seismic actions and rules for buildings”, Brussel. 2008. Leite, 2014 Mendes, 2013 Mendes, 2008 Foti, 2014, Output-only identification and model updating by dynamic testing in unfavorable conditions of a seismically damaged building, Comput.-Aided Civ. Infrastruct. Eng., 29, 659, 10.1111/mice.12071 Cabalar, 2012, Some applications of adaptive neuro-fuzzy inference system (ANFIS) in geotechnical engineering, Comput. Geotech., 40, 14, 10.1016/j.compgeo.2011.09.008 Edincliler, 2013, Modelling dynamic behaviour of sand–waste tires mixtures using Neural Networks and Neuro-Fuzzy, Eur. J. Environ. Civ. Eng., 17, 720, 10.1080/19648189.2013.814552 Mirrashid, 2014, Earthquake magnitude prediction by adaptive neuro-fuzzy inference system (ANFIS) based on fuzzy C-means algorithm, Nat. Hazards, 74, 1577, 10.1007/s11069-014-1264-7 Gul, 2017, ANFIS kullanılarak Tunceli ili için global güneş radyasyonu tahmini, DÜMF Mühendislik Dergisi, 8, 891 Işık, 2018, Artificial neural networks and adaptive neuro-fuzzy inference systems approaches to forecast the meteorological data for HVAC: the case of cities for Turkey, Energy, 154, 7, 10.1016/j.energy.2018.04.069 Jang, 1993, ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans. Syst., Man, Cybern., 23, 665, 10.1109/21.256541