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- MakaleYalnız künye2019Scopus SJRQ2Experimental damage evaluation of prototype infill wall based on forced vibration test
Onat, Onur
This paper aims to investigate vibration frequency decrease (vibration period elongation) of reinforced concrete (RC) structure with unreinforced infill wall and reinforced infill wall exposed to progressively increased artificial earthquake load on shaking table. For this purpose, two shaking table experiments were selected as a case study. Shaking table experiments were carried on 1:1 scaled prototype one bay one storey RC structure with infill walls. The purpose of this shaking table experiment sequence is to assess local behavior and progressive collapse mechanism. Frequency decrease and eigen-vector evolution are directly related to in-plane and out-of-plane bearing capacities of infill wall enclosure with reinforced concrete frame. Firstly, frequency decrease-damage relationship was evaluated on the base of experiment results. Then, frequency decrease and stiffness degradation were evaluated with applied Peak Ground Acceleration (PGA) by considering strength deterioration. Lastly, eigenvector evolution-local damage and eigenvector evolution-frequency decrease relationship was investigated. Five modes were considered while evaluating damage and frequency decrease of the tested specimens. The relationship between frequency decrease, stiffness degradation and damage level were presented while comparing with Unreinforced Brick Infill (URB) and Reinforced Infill wall with Bed Joint Reinforcement (BJR) on the base of natural vibration frequency.
AtıfScopus14WoS12OpenAlex9
İncele - MakaleYalnız künye2019Scopus SJRQ1Elimination of a measurement problem: A robust prediction model for missing eigenvector value to assess earthquake induced out-of-plane failure of infill wall
Onat, Onur; Yön, Burak
One of the effective methods to assess local or global behavior of structural system is to perform shake table experiment. The biggest challenge of all experiments, either shake table or others, are missing data during the experiments due to removed instruments. This problem is arose especially during the out-of-plane tests. This problem is named as “Measurement” problem. This paper is focused on detection the measurement problem and solving this problem by using Adaptive-Network Based Neuro Inference System (ANFIS). Definition of the problem is briefly missing out-of-plane eigenvector value during the implementation of a shake table experiments on a full scale one bay one storey reinforced concrete structural system. For this aim, two shake table experiments were performed with reinforced concrete frame (RCF)and infill wall at the National Civil Engineering Laboratory (LNEC)Portugal. The out-of-plane failure of infill wall was assessed under combined bidirectional seismic load. Shake table experiments were conducted on two types of specimens. One of them is Unreinforced Brick Infill Wall (URBIF)composed of single layer 22 cm thick brick. The other is single layer 22 cm thick Infill Wall with Bed Joint Reinforcement solution (IwBJR). Both specimens were made of a single-layer brick infill wall enclosure with the RCF. Bed joint reinforcement term refers horizontal bed joint reinforcement as a strengthening technique. Shake table experiments were performed on each specimen at four stages. After third level of earthquake load, the accelerometers were removed on the wall to prevent damage to them. The removal of these instruments results in missing data. The missing eigenvector values were predicted with a robust Adaptive-Network Based Neuro Inference System (ANFIS)model to present failure mode of infill wall. The estimation quality was tested with R2, Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE)and Root Mean Square Error (RMSE)among with available data. The results of the quality indicator of R2 are 0.999 for URBIF model and 0.997 for IwBJR model, respectively. Moreover, rest of the indicator results are less than %1. The proposed approach is reliable on the base of quality indicator. Moreover, this study can be used for seismic engineering to predict eigenvector values to see local behavior of infill wall and mode shape evolution to determine which mod prone for partial and total collapse of infill wall.
AtıfScopus12WoS11OpenAlex10İncele - MakaleYalnız künye2018Scopus SJRQ1Application of Artificial Neural Networks to the prediction of out-of-plane response of infill walls subjected to shake table
Onat, Onur; Gul, Muhammet
The main purpose of this paper is to predict missing absolute out-of-plane displacements and failure limits of infill walls by artificial neural network (ANN) models. For this purpose, two shake table experiments are performed. These experiments are conducted on a 1:1 scale one-bay one-story reinforced concrete frame (RCF) with an infill wall. One of the experimental models is composed of unreinforced brick model (URB) enclosures with an RCF and other is composed of an infill wall with bed joint reinforcement (BJR) enclosures with an RCF. An artificial earthquake load is applied with four acceleration levels to the URB model and with five acceleration levels to the BJR model. After a certain acceleration level, the accelerometers are detached from the wall to prevent damage to them. The removal of these instruments results in missing data. The missing absolute maximum out-of-plane displacements are predicted with ANN models. Failure of the infill wall in the out-of-plane direction is also predicted at the 0.79g acceleration level. An accuracy of 99% is obtained for the available data. In addition, a benchmark analysis with multiple regressionis performed. This study validates that the ANN-based procedure estimates missing experimental data more accurately than multiple regression models.
AtıfScopus33WoS29OpenAlex14İncele - MakaleYalnız künye2015Scopus SJRQ2Experimental and numerical analysis of RC structure with two leaf cavity wall subjected to shake table
Onat, Onur; Lourenço, Paulo B.; Koçak, Ali
This paper presents finite element (FE) based pushover analysis of a reinforced concrete structure with a two-leaf cavity wall (TLCW) to estimate the performance level of this structure. In addition to this, an unreinforced masonry (URM) model was selected for comparison. Simulations and analyses of these structures were performed using the DIANA FE program. The mentioned structures were selected as two storeys and two bays. The dimensions of the structures were scaled 1: 1.5 according to the Cauchy Froude similitude law. A shake table experiment was implemented on the reinforced concrete structure with the two-leaf cavity wall (TLCW) at the National Civil Engineering Laboratory (LNEC) in Lisbon, Portugal. The model that simulates URM was not experimentally studied. This structure was modelled in the same manner as the TLCW. The purpose of this virtual model is to compare the respective performances. Two nonlinear analyses were performed and compared with the experimental test results. These analyses were carried out in two phases. The research addresses first the analysis of a structure with only reinforced concrete elements, and secondly the analysis of the same structure with reinforced concrete elements and infill walls. Both researches consider static loading and pushover analysis. The experimental pushover curve was plotted by the envelope of the experimental curve obtained on the basis of the shake table records. Crack patterns, failure modes and performance curves were plotted for both models. Finally, results were evaluated on the basis of the current regulation ASCE/SEI 41-06.
AtıfWoS15İncele