• Coupled systems mechanics
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• 제11권2호
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• pp.121-132
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• 2022

Wastewater treatment plants (WWTPs) are designed and built to remove contaminants from wastewater. WWTPs are composed of various facilities equipped with hydro-mechanical and electrical equipment. This paper presents a comparison of two different approaches for WWTPs modelling. Static modelling is suitable for determining the dimensions of facilities and equipment capacity. The special significance of this approach is for the design of new plants, i.e., when a very small number of input data on the quantities and composition of the influent wastewater is available. Dynamic modelling is expensive, time consuming and requires great expertise in the use of simulators, models and very good understanding of the treatment processes. Also, dynamic modelling is very important to use for optimization, consideration of future scenarios and also possible scenarios on the plant. The comparison of two approaches was made on the input data from the biggest and most important plant in Bosnia and Herzegovina (B&H)-WWTP Butila (Sarajevo). The main idea is to show the differences between two demanding accesses. It is important to know how to apply an adequate approach to research and solve the set task. The II phase of the plant Butila, which includes the removal of nutrients, is planned in several years and therefore the importance of research has increased.

• Steel and Composite Structures
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• 제17권4호
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• pp.387-403
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• 2014

This paper presents a high accuracy Finite Element approach for delamination modelling in laminated composite structures. This approach uses multi-layered shell element and cohesive zone modelling to handle the mechanical properties and damages characteristics of a laminated composite plate under low velocity impact. Both intralaminar and interlaminar failure modes, which are usually observed in laminated composite materials under impact loading, were addressed. The detail of modelling, energy absorption mechanisms, and comparison of simulation results with experimental test data were discussed in detail. The presented approach was applied for various models and simulation time was found remarkably inexpensive. In addition, the results were found to be in good agreement with the corresponding results of experimental data. Considering simulation time and results accuracy, this approach addresses an efficient technique for delamination modelling, and it could be followed by other researchers for damage analysis of laminated composite material structures subjected to dynamic impact loading.

• Structural Engineering and Mechanics
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• 제33권5호
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• pp.589-604
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• 2009

In the field of predicting structural safety and reliability the operating conditions play an essential role. Since the time and cost limitations are a significant factors in engineering it is important to predict the future operating conditions as close to the actual state as possible from small amount of available data. Because of the randomness of the environment the shape of measured load spectra can vary considerably and therefore simple distribution functions are frequently not sufficient for their modelling. Thus mixed distribution functions have to be used. In general their major weakness is the complicated calculation of unknown parameters. The scope of the paper is to investigate the load spectra growth for actual operating conditions and to investigate the modelling and extrapolation of load spectra with algorithm for mixed distribution estimation, REBMIX. The data obtained from the measurements of wheel forces and the braking moment on proving ground is used to generate load spectra.

• 대한의용생체공학회:의공학회지
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• 제9권1호
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• pp.17-24
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• 1988

This paper describes a neural network modelling of a vertebrate retina using a discrete-time and discrete-space approach based on neuro-anatomical data, and the computer simulations of the model which approximate the frog/amphibian negro-physiological data. It then compares them and describes how such a model can be beneficially used for confirming the hypothesis of a given neural system and further predict yet unknown experimental data.

• 한국해양공학회지
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• 제10권1호
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• pp.65-74
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• 1996

Up to now, the existing crack growth modelling has used a mathematical approximation but an assumed function have a great influence on this method. Especially, crack growth behavior that shows very strong nonlinearity needed complicated function which has difficulty in setting parameter of it. The main characteristics of neural network modelling to engineering field are simple calculations and absence of assumed function. In this paper, after discussing learning and generalization of neural networks, we performed crack growth modelling on the basis of above learning algorithms. J'-da/dt relation predicted by neural networks shows that test condition with unlearned data is simulated well within estimated mean error(5%).

• Smart Structures and Systems
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• 제20권2호
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• pp.127-137
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• 2017

Identification of damping characteristics is of significant importance for dynamic response analysis and condition assessment of structural systems. Damping is associated with the behavior of the energy dissipation mechanism. Identification of damping ratios based on the sensitivity of dynamic responses and the model updating technique is investigated with numerical and experimental investigations. The effectiveness and performance of using the sensitivity-based model updating method and vibration monitoring data for damping ratios identification are investigated. Numerical studies on a three-dimensional truss bridge model are conducted to verify the effectiveness of the proposed approach. Measurement noise effect and the initial finite element modelling errors are considered. The results demonstrate that the damping ratio identification with the proposed approach is not sensitive to the noise effect but could be affected significantly by the modelling errors. Experimental studies on a steel planar frame structure are conducted. The robustness and performance of the proposed damping identification approach are investigated with real measured vibration data. The results demonstrate that the proposed approach has a decent and reliable performance to identify the damping ratios.

• Smart Structures and Systems
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• 제32권4호
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• pp.267-279
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• 2023

Data modelling and interpretation for structural health monitoring (SHM) field data are critical for evaluating structural performance and quantifying the vulnerability of infrastructure systems. In order to improve the data modelling accuracy, and extend the application range from data regression analysis to out-of-sample forecasting analysis, an improved most likely heteroscedastic Gaussian process (iMLHGP) methodology is proposed in this study by the incorporation of the outof-sample forecasting algorithm. The proposed iMLHGP method overcomes this limitation of constant variance of Gaussian process (GP), and can be used for estimating non-stationary typhoon-induced response statistics with high volatility. The first attempt at performing data regression and forecasting analysis on structural responses using the proposed iMLHGP method has been presented by applying it to real-world filed SHM data from an instrumented cable-stay bridge during typhoon events. Uncertainty quantification and correlation analysis were also carried out to investigate the influence of typhoons on bridge strain data. Results show that the iMLHGP method has high accuracy in both regression and out-of-sample forecasting. The iMLHGP framework takes both data heteroscedasticity and accurate analytical processing of noise variance (replace with a point estimation on the most likely value) into account to avoid the intensive computational effort. According to uncertainty quantification and correlation analysis results, the uncertainties of strain measurements are affected by both traffic and wind speed. The overall change of bridge strain is affected by temperature, and the local fluctuation is greatly affected by wind speed in typhoon conditions.

• 한국정밀공학회지
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• 제9권1호
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• pp.96-105
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• 1992

A study on the construction of a CAD/CAM system operated by 16 Bit PC basic language, for machining sculptured surface of die, was carried out. The system consists of 2 steps i.e., process for geometric modelling by wire frame and process for machining data generation. Geometric modelling for sculptured surface is made by the point data fitting, parallel sweeping, normal sweeping and linear connection of cross section curve. Machining data are gained by cutter off-set of geometric model data and machining carried out by DNC. This system is to be proved enough for rough cutting by actual machining experiment. But, for becoming a high level system, another method of cutter off-set has to be regarded and system must be reconstructed by another program language.

• 설비공학논문집
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• 제16권6호
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• pp.556-565
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• 2004

We have experimented an actual solar water heating system acquiring real data for one year period. On the basis of the operation data, it is necessary to predict the system performance such as collector efficiency and solar fraction, and to analyze the economical efficiency for system optimal design. To estimate the performance of actual systems through simulation, valid modelling for components consisting of the system should be accompanied. The present study is focused on the modelling for load patterns and operating control conditions. We proposed two load models: concentration model which gathers real loads as a meaningful group and distribution model which disperses real loads with time. If grouping of the load distribution is suitable, the predicted values by the concentration model approaches to those by the distribution model close to actual load pattern apparently. As a result, both of them are in good agreement with those by experiment.

• Wind and Structures
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• 제5권1호
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• pp.1-14
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• 2002

Artificial Neural Networks (ANN) have the capability to develop functional relationships between input-output patterns obtained from any source. Thus ANN can be conveniently used to develop a generalised relationship from limited and sometimes inconsistent data, and can therefore also be applied to tackle the data obtained from wind tunnel tests on building models with large number of variables. In this paper ANN model has been developed for predicting wind induced pressures in various zones of a Gable Building from limited test data. The procedure is also extended to a case wherein interference effects on a gable roof building by a similar building are studied. It is found that the Artificial Neural Network modelling is seen to predict successfully, the pressure coefficients for any roof slope that has not been covered by the experimental study. It is seen that ANN modelling can lead to a reduction of the wind tunnel testing effort for interference studies to almost half.