Marijana Hadzima-Nyarko

A neural network based modelling and sensitivity analysis of damage ratio coefficient

The level of structural damage after an earthquake can often be expressed using the damage ratio (DR) coefficient. This coefficient can be calculated using different formulas. A previously valorised new original formula for damage ratio derived for regular structures is implemented. This formula uses the structure response parameters of a single degree of freedom (SDOF) model. The structure response parameters of the SDOF model are obtained by analyzing a large number of non-linear numeric structure responses using earthquakes of different intensities as load input. In this paper, a multilayer perceptron (MLP) neural network is used to model the relationship between the structure parameters (natural period, elastic base shear capacity, post-elastic stiffness and damping) of an SDOF model and the damage ratio (DR) coefficient. The influence of the individual structure parameters on the damage level of a structure is then determined by performing a sensitivity analysis procedure on the trained MLP neural network.

Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava

Water temperature directly affects the physical, biological and chemical characteristics of the river and determines the fitness and life of all aquatic organisms. It has direct and indirect effects on nearly all aspects of stream ecology. Accurately estimating water temperature is a complex problem. The purpose of this article is to analyze the relationship between the air and water temperature of the River Drava by constructing an artificial neural network (ANN) model and choosing appropriate network architectures for the River Drava’s daily river water temperature as well as demonstrating its application in improving the interpretation of the results. A linear regression model, as well as a stochastic model are also constructed and compared to ANN models consisting of a multilayer perceptron neural network and a radial basis function network. The results indicate that the ANN models are much better models and that ANNs are powerful tools that can be used for the estimation of daily mean river temperature.

Modelling river temperature from air temperature: case of the River Drava (Croatia)

Abstract Measurements made in the past few decades undeniably indicate change in the climate. The most visible sign of global climate change is air temperature, while less visible indicators include changes in river water temperatures. Changes in river temperature can significantly affect the environment, primarily the biosphere. The physical, biological and chemical characteristics of the river are directly affected by water temperature, although estimation of this relationship presents a complex problem. Although river temperature is influenced by hydrological and meteorological factors, the purpose of this study is to model daily water temperature using only one known parameter, mean air temperature. The relationship between the daily mean air and daily water temperature of the River Drava in Croatia is analysed using linear regression, stochastic modelling or nonlinear regression and multilayer perceptron (MLP) feed-forward neural networks. The results indicate that the MLP models are much better models which can be used for the estimation and prediction of daily mean river temperature. Editor D. Koutsoyiannis; Associate editor M. Acreman

Seismic Risk of Croatian Cities Based on Building’s Vulnerability

Seismic risk is fundamental for the establishment of priorities in long-term prevention policy since urbanization and concentration of population in e

Spectral Functions of Damage Index (DI) for Masonry Buildings with Flexible Floors

Most of the buildings in old city cores of Croatia, built between 1860 and 1920 with wooden floors, are mainly designed to bear vertical loads. In thi

Modelling daily water temperature from air temperature for the Missouri River

The bio-chemical and physical characteristics of a river are directly affected by water temperature, which thereby affects the overall health of aquatic ecosystems. It is a complex problem to accurately estimate water temperature. Modelling of river water temperature is usually based on a suitable mathematical model and field measurements of various atmospheric factors. In this article, the air–water temperature relationship of the Missouri River is investigated by developing three different machine learning models (Artificial Neural Network (ANN), Gaussian Process Regression (GPR), and Bootstrap Aggregated Decision Trees (BA-DT)). Standard models (linear regression, non-linear regression, and stochastic models) are also developed and compared to machine learning models. Analyzing the three standard models, the stochastic model clearly outperforms the standard linear model and nonlinear model. All the three machine learning models have comparable results and outperform the stochastic model, with GPR having slightly better results for stations No. 2 and 3, while BA-DT has slightly better results for station No. 1. The machine learning models are very effective tools which can be used for the prediction of daily river temperature.

Determining the Natural Frequency of Cantilever Beams Using ANN and Heuristic Search

ABSTRACT An artificial neural network (ANN) is used to model the frequency of the first mode, using the beam length, the moment of inertia, and the load applied on the beam as input parameters on a database of 100 samples. Three different heuristic optimization methods are used to train the ANN: genetic algorithm (GA), particle swarm optimization algorithm and imperialist competitive algorithm. The suitability of these algorithms in training ANN is determined based on accuracy and runtime performance. Results show that, in determining the natural frequency of cantilever beams, the ANN model trained using GA outperforms the other models in terms of accuracy.

ASSESSING SEISMIC RISK IN RETFALA NOVA, OSIJEK

The Croatian territory, as part of the Mediterranean-trans-Asiatic belt, experiences pronounced earthquake activity. Seismic risk is the expected damage caused by earthquakes to buildings, measured both in social and economic losses, which can be described through seismic hazard, seismic vulnerability, and exposure. The city of Osijek is located in the eastern part of Croatia, and Retfala Nova is a residential settlement in the western part of the city. An important step in assessing earthquake loss is defining the exposure, so we created a form used to collect information on buildings and make a building database. In this paper, we estimated seismic vulnerability based on the capacity spectrum method, which involves constructing fragility curves and converting them to damage probability matrices, as well as constructing capacity curves.

Usporedba osnovnih perioda modela zgrada s armiranobetonskim zidovima s empirijskim izrazima

Usporedba osnovnih perioda modela zgrada s armiranobetonskim zidovima s empirijskim izrazima Izvorni znanstveni clanka Empirijski izrazi za procjenu osnovnih perioda su sastavni dio propisa za proracun seizmickih djelovanja te uglavnom ovise o visini, materijalu (celik, armirani beton) i nosivom sustavu(okvir, posmicni zidovi, itd.) građevine. Ovi izrazi su obicno nastali iz empirijskih podataka kroz analizu mjerenih osnovnih perioda postojecih građevina pod djelovanjem potresa. Provedena je parametarska studija na 480 modela armiranobetonskih zgrada s posmicnim zidovima s razlicitim ulaznim podacima: visina zgrade, broj raspona i omjer povrsine posmicnih zidova i tlocrtne povrsine zgrade. Cilj ovog istraživanja je provjeriti empirijske izraze razlicitih autora i seizmickih propisa u svrhu provjere tocnosti izraza kao pocetnih pretpostavki kod projektiranja potresno otpornih građevina. Kljucne rijeci: EN1998-1; armiranobetonski posmicni zidovi; modeli zgrada; osnovni period; parametarska studija

OJAČANJE POVIJESNIH GRAĐEVINA KOMPOZITNIM POLIMERIMA

Vecina građevina kulturne ili graditeljske bastine su zidane zgrade koje su izgrađene najcesce od kamenih ili opecnih zidnih elemenata povezan