Dogan, G.Arslan, M.H.Ilki, A.2023-12-092023-12-0920232623-3347https://hdl.handle.net/20.500.13091/48789th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, COMPDYN 2023 -- 12 June 2023 through 14 June 2023 -- 193215In Turkey, which is located on an active seismic zone, the existing old reinforced concrete (RC) buildings have been extensively and severely damaged in the past earthquakes. In this context, in the event of a possible earthquake such as the 6 February 2023 Kahramanmaras earthquakes (Mw=7.7 and 7.6), it is extremely important to conduct the damage assessment promptly and in an objective/homogenous way. Although there are advanced methods developed for the assessment of post-earthquake damages to buildings in Turkey, there may often be differences/subjectiveness in the damage decisions based on the experience of the assessment staff and the psychological factors in the field. In this respect, introduction of intelligent decision support systems that will accelerate and harmonize the decision-making process of the engineers/technical staff involved in damage assessment activities after earthquakes may be very beneficial. In this study, firstly, information about the preliminary studies on the use of smart systems in structural engineering problems was given. Afterwards, smart software developed to be used in post-earthquake damage assessment of reinforced concrete buildings were compared according to their success/accuracy rates. In the evaluation, it has been seen that pre-trained deep learning models have a very high success in predicting post-earthquake damages in reinforced concrete structures. © 2023 COMPDYN Proceedings. All rights reservedeninfo:eu-repo/semantics/closedAccessConvolutional Neural NetworksDamageDeep learningEarthquakeReinforced ConcreteComputational methodsConcrete buildingsConvolutional neural networksDamage detectionDecision makingDecision support systemsDeep learningEarthquake engineeringEarthquakesEngineering geologyLearning systemsActive seismicConvolutional neural networkDamageDamage assessmentsDecision-basedDeep learningEarthquake damagesPsychological factorsReinforced concrete buildingsSeismic zonesReinforced concreteConference Object2-s2.0-85175870245