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- MakaleYalnız künye2026Scopus SJRQ2Frequency-Based Damage Detection with ANN and Seismic Behaviour Assessment of a Historical Masonry Tower
Demirtaş, Betül; Türker, Temel; Yanik, Yusuf; Dede, Tayfun
Nowadays, Artificial Neural Networks (ANNs) are widely used in damage detection because of their powerful computational and exceptional ability to recognize patterns in historical structures. This paper presents the seismic behaviour assessment and structural damage detection performance of the ANN trained with natural frequencies to identify the damage on the Hagia Sophia Bell Tower in Trabzon, Türkiye. Reduction in the stiffness of the structures causes changes in the structural dynamics. Natural frequencies, being key indicators in damage detection, were utilized as input parameters to train the ANN in this study. The study’s scope included updating of the initial finite element (FE) model constituted with SAP2000 software according to the experimental dynamic characteristics obtained from the Civil Engineering Vibration System developed by our work team. The seismic behaviour of the tower was also examined through time history analyses under the effect of strong earthquakes. The required data for the ANN analyses was obtained from the FE modal analysis of the tower. The ANNs dataset was generated separately by simulating both single and double damage scenarios. Possible twelve damage zones were determined according to the places where the highest stresses occurred in the earthquake analysis of the tower. The reduction in the elasticity modulus of each region defined on the tower was considered as damaging effect and used as ANN output parameter. It was shown that ANN trained with natural frequencies can effectively identify the location and severity of damage.
AtıfScopus6OpenAlex7
İncele - MakaleYalnız künye2015DAMAGE DETECTION IN SIMULATED SPACE FRAMES USING GENETIC ALGORITHMS
Artar, Musa; Daloglu, Ayse T.
Genetic algorithms (GA) based finite element model updating are applied to predict damage location and severity in space frames. The changes in natural frequencies are used as dynamic indicators to describe damaged members. Objective functions including dynamic data provide minimization of dynamic differences between numerical model and simulated damaged model. The presence of damages in structural elements is identified by stiffness reduction as a reduction in modulus of elasticity. Reproduction, double-point crossover and mutation operators are used in GA optimization procedures. In this paper, different simulated examples having various damage scenarios are modelled in SAP2000 software to obtain the experimental dynamic data. In the last example, noise effect is taken into account in simulated damaged data. A program is developed in MATLAB software for numerical model updating based on all genetic algorithm procedures. Thus, the size and extent of simulated damages are determined by updated numerical model. Results obtained from examples show that GA optimization is a convenient method for damage identification.
AtıfWoS2
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