Civil Engineering / İnşaat Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/13

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Subject: search.filters.subject.Artificial neural network

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  • Book Part
    Citation - Scopus: 3
    Real-Time Flood Hydrograph Predictions Using Rating Curve and Soft Computing Methods (ga, Ann)
    (Elsevier, 2022) Tayfur, Gökmen
    This chapter introduces hydraulic and hydrologic flood routing methods in natural channels. It details hydrological flood routing methods of the Rating Curve and Muskingum. Based on the rating curve method (RCM), it presents real-time flood hydrograph predictions using the genetic algorithm (GA-based RCM) model. In addition, it presents how to make real-time flood hydrograph predictions using the artificial neural network (ANN). The chapter briefly introduces the basics of GA and details how to calibrate and validate the GA-based RCM model using measured real-time flood hydrographs. Similarly, after giving the basics of ANN, it shows how to train and test the ANN model using measured hydrographs. Real hydrograph simulations by the RCM, GA-based RCM, and ANN are presented, and merits of each model are discussed. © 2023 Elsevier Inc. All rights reserved.
  • Article
    Citation - Scopus: 1
    Relationship Between Abrasion, Fragmentation and Thermal Weathering Resistance of Aggregates: Regression and Artificial Neural Network Analyses
    (Springer, 2023) Gökalp, İslam; Kaya, Orhan; Uz, Volkan Emre
    For being used in pavement construction, properties of aggregates must satisfy the minimum requirements specified by highway agencies or institutions. The properties of the aggregates are determined by many tests lasting anywhere between a couple of hours to a few weeks depending on the type of the test. If good correlations can be established between the tests taking longer time and the ones taking comparably shorter time, there might be no need to conduct these longer time-taking tests for the sake of time. The aim of this study is to investigate the relationships between abrasion, fragmentation, and thermal weathering resistances of different aggregate types. To accomplish this aim, aggregates with different origins (natural and slags) were tested and correlative analyses utilizing regression analysis and artificial neural network (ANN) models were performed to establish relationships between the results of these test methods. It was found that good correlations can be established especially with ANN models and significant amount of time and effort can be saved with these developed models. © 2023, The Author(s), under exclusive licence to Chinese Society of Pavement Engineering.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 1
    Significance of Rent Attributes in Prediction of Earthquake Damage in Adapazari, Turkey
    (Czech Technical University in Prague, 2014) Tayfur, Gökmen; Bektaş, Birkan; Duvarcı, Yavuz
    This paper analyses rent-based determinants of earthquake damage from an urban planning perspective with the data gathered from Adapazari, Turkey, after the disaster in 1999 Eastern Marmara Earthquake (EME). The study employs linear regression, log-linear regression, and artificial neural networks (ANN) methods for cross-verification of results and for finding out the significant urban rent attribute(s) responsible for the damage. All models used are equally capable of predicting the earthquake damage and converge to similar results even if the data are limited. Of the rent variables, the physical density is proved to be especially significant in predicting earthquake damage, while the land value contributes to building resistance. Thus, urban rent can be the primary tool for planners to help reduce the fatalities in preventive planning studies.
  • Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Developing Cation Exchange Capacity and Soil Index Properties Relationships Using a Neuro-Fuzzy Approach
    (Springer Verlag, 2014) Pulat, Hasan Fırat; Tayfur, Gökmen; Yükselen Aksoy, Yeliz
    Artificial intelligence methods are employed to predict cation exchange capacity (CEC) from five different soil index properties, namely specific surface area (SSA), liquid limit, plasticity index, activity (ACT), and clay fraction (CF). Artificial neural networks (ANNs) analyses were first employed to determine the most related index parameters with cation exchange capacity. For this purpose, 40 datasets were employed to train the network and 10 datasets were used to test it. The ANN analyses were conducted with 15 different input vector combinations using same datasets. As a result of this investigation, the ANN analyses revealed that SSA and ACT are the most effective parameters on the CEC. Next, based upon these most effective input parameters, the fuzzy logic (FL) model was developed for the CEC. In the developed FL model, triangular membership functions were employed for both the input (SSA and ACT) variables and the output variable (CEC). A total of nine Mamdani fuzzy rules were deduced from the datasets, used for the training of the ANN model. Minimization (min) inferencing, maximum (max) composition, and centroid defuzzification methods are employed for the constructed FL model. The developed FL model was then tested against the remaining datasets, which were also used for testing the ANN model. The prediction results are satisfactory with a determination coefficient, R2 = 0.94 and mean absolute error, (MAE) = 7.1.
  • Article
    Citation - WoS: 28
    Citation - Scopus: 30
    Principle Component Analysis in Conjuction With Data Driven Methods for Sediment Load Prediction
    (Springer Verlag, 2013) Tayfur, Gökmen; Karimi, Yashar; Singh, Vijay P.
    This study investigates sediment load prediction and generalization from laboratory scale to field scale using principle component analysis (PCA) in conjunction with data driven methods of artificial neural networks (ANNs) and genetic algorithms (GAs). Five main dimensionless parameters for total load are identified by using PCA. These parameters are used in the input vector of ANN for predicting total sediment loads. In addition, nonlinear equations are constructed, based upon the same identified dimensionless parameters. The optimal values of exponents and constants of the equations are obtained by the GA method. The performance of the so-developed ANN and GA based methods is evaluated using laboratory and field data. Results show that the expert methods (ANN and GA), calibrated with laboratory data, are capable of predicting total sediment load in field, thus showing their transferability. In addition, this study shows that the expert methods are not transferable for suspended load, perhaps due to insufficient laboratory data. Yet, these methods are able to predict suspended load in field, when trained with respective field data.