• Wind and Structures
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• v.36 no.6
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• pp.423-434
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• 2023

Aerodynamic force coefficients are generally obtained from traditional wind tunnel tests or computational fluid dynamics (CFD). Unfortunately, the techniques mentioned above can sometimes be cumbersome because of the cost involved, such as the computational cost and the use of heavy equipment, to name only two examples. This study proposed to build a deep neural network model to predict the aerodynamic force coefficients based on data collected from CFD simulations to overcome these drawbacks. Therefore, a series of CFD simulations were conducted using different geometric parameters to obtain the aerodynamic force coefficients, validated with wind tunnel tests. The results obtained from CFD simulations were used to create a dataset to train a multilayer perceptron artificial neural network (ANN) model. The models were obtained using three optimization algorithms: scaled conjugate gradient (SCG), Bayesian regularization (BR), and Levenberg-Marquardt algorithms (LM). Furthermore, the performance of each neural network was verified using two performance metrics, including the mean square error and the R-squared coefficient of determination. Finally, the ANN model proved to be highly accurate in predicting the force coefficients of similar bridge sections, thus circumventing the computational burden associated with CFD simulation and the cost of traditional wind tunnel tests.

• Geomechanics and Engineering
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• v.33 no.6
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• pp.567-581
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• 2023

The prediction and achievement of a proper rock fragmentation size is the main challenge of blasting operations in surface mines. This is because an optimum size distribution can optimize the overall mine/plant economics. To this end, this study attempts to develop four improved artificial intelligence models to predict rock fragmentation through cascaded forward neural network (CFNN) and radial basis function neural network (RBFNN) models. In this regards, the CFNN was trained by the Levenberg-Marquardt algorithm (LMA) and Conjugate gradient backpropagation (CGP). Further, the RBFNN was optimized by the Dragonfly Algorithm (DA) and teaching-learning-based optimization (TLBO). For developing the models, the database required was collected from the Midouk copper mine, Iran. After modeling, the statistical functions were computed to check the accuracy of the models, and the root mean square errors (RMSEs) of CFNN-LMA, CFNN-CGP, RBFNN-DA, and RBFNN-TLBO were obtained as 1.0656, 1.9698, 2.2235, and 1.6216, respectively. Accordingly, CFNN-LMA, with the lowest RMSE, was determined as the model with the best prediction results among the four examined in this study.

• Smart Structures and Systems
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• v.32 no.1
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• pp.37-47
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• 2023

Indirect determination of pan evaporation (PE) has been highly regarded, due to the advantages of intelligent models employed for this objective. This work pursues improving the reliability of a popular intelligent model, namely multi-layer perceptron (MLP) through surmounting its computational knots. Available climatic data of Fresno weather station (California, USA) is used for this study. In the first step, testing several most common trainers of the MLP revealed the superiority of the Levenberg-Marquardt (LM) algorithm. It, therefore, is considered as the classical training approach. Next, the optimum configurations of two metaheuristic algorithms, namely cuttlefish optimization algorithm (CFOA) and teaching-learning-based optimization (TLBO) are incorporated to optimally train the MLP. In these two models, the LM is replaced with metaheuristic strategies. Overall, the results demonstrated the high competency of the MLP (correlations above 0.997) in the presence of all three strategies. It was also observed that the TLBO enhances the learning and prediction accuracy of the classical MLP (by nearly 7.7% and 9.2%, respectively), while the CFOA performed weaker than LM. Moreover, a comparison between the efficiency of the used metaheuristic optimizers showed that the TLBO is a more time-effective technique for predicting the PE. Hence, it can serve as a promising approach for indirect PE analysis.

• Transactions of Materials Processing
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• v.33 no.2
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• pp.96-102
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• 2024

This study addresses evaluation of performance of hardening model for a titanium alloy (Ti6Al4V) based on the artificial neural network (ANN) regarding the strain rate and the temperature. Uniaxial compression tests were carried out at different strain rates from 0.001 /s to 10 /s and temperatures from 575 ℃ To 975 ℃. Using the experimental data, ANN models were trained and tested with different hyperparameters, such as size of hidden layer and optimizer. The input features were determined with the equivalent plastic strain, strain rate, and temperature while the output value was set to the equivalent stress. When the number of data is sufficient with a smooth tendency, both the Bayesian regulation (BR) and the Levenberg-Marquardt (LM) show good performance to predict the flow behavior. However, only BR algorithm shows a predictability when the number of data is insufficient. Furthermore, a proper size of the hidden layer must be confirmed to describe the behavior with the limited number of the data.

• Journal of Biomedical Engineering Research
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• v.16 no.2
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• pp.223-230
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• 1995

This study investigates a combined mathematical model for the quantitative estimation of regional myocardial blood flow in experimental canine coronary artery occlusion and in patients with ischemic myocardial diseases using Rb-82 dynamic myocardial positron emission tomography. The coronary thrombosis was induced using the new catheter technique by narrowing the lumen of coronary vessel gradually, which finally led to partial obstruction of coronary artery. Thirty four Rb-82 dynamic myocardial PET scans were performed sequentially for each experiment using our 5, 10 and 20 second acquisition protocol, respectively, and six to seven regions of interest were drawn on each transaxial slices, one on left ventricular chamber for input function and the others on normal and decreased perfusion myocardial segments for the flow estimation in those regions. Two compartment model and graphical analysis method have been applied to the measured sets of regional PET data, and the rate constants of influx to myocardial tissue were calculated for regional myocardial flow estimates with the two parameter fits of raw data by the Levenberg-Marquardt method. The results showed that, (I) two compartment model suggested by Kety-Schmidt, with proper modification of the measured data and volume of distribution, could be used for the simple estimation of regional myocardial blood flow, (2) the calculated regional myocardial blood flow estimates were dependent on the selection of input function, which reflected partial volume effect and left ventricular wall motion in previously used graphical analysis, and (3) mathematically fitted input and tissue time activity curves were more suitable than the direct application of the measured data in terms of convergence.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.20 no.3
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• pp.247-254
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• 2008

The double-peak normal distribution function (DPDF) suggested by Cho et al.(2004) has the problems that the extremely high and low tidal elevations are frequently generated in the Monte-Carlo simulation processes because the upper and lower limits of the DPDF are unbounded in spite of the excellent goodness-offit results. In this study, the modified DPDF is suggested by introducing the upper and lower value parameters and re-scale parameters in order to remove these problems. These new parameters of the DPDF are optimally estimated by the non-linear optimization problem solver using the Levenberg-Marquardt scheme. This modified DPDF can remove completely the unrealistically generated tidal levations and give a slightly better fit than the existing DRDF. Based on the DPDF's characteristic power, the over- and under estimation problems of the design factors are also automatically intercepted, too.

• International Journal of Concrete Structures and Materials
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• v.7 no.3
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• pp.225-238
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• 2013

In this study, a two-layer feed-forward neural network was constructed and applied to determine a mapping associating mix design and testing factors of cement-nano silica (NS)-rice husk ash ternary blended concrete samples with their performance in conductance to the water absorption properties. To generate data for the neural network model (NNM), a total of 174 field cores from 58 different mixes at three ages were tested in the laboratory for each of percentage, velocity and coefficient of water absorption and mix volumetric properties. The significant factors (six items) that affect the permeability properties of ternary blended concrete were identified by experimental studies which were: (1) percentage of cement; (2) content of rice husk ash; (3) percentage of 15 nm of $SiO_2$ particles; (4) content of NS particles with average size of 80 nm; (5) effect of curing medium and (6) curing time. The mentioned significant factors were then used to define the domain of a neural network which was trained based on the Levenberg-Marquardt back propagation algorithm using Matlab software. Excellent agreement was observed between simulation and laboratory data. It is believed that the novel developed NNM with three outputs will be a useful tool in the study of the permeability properties of ternary blended concrete and its maintenance.

• Atmosphere
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• v.27 no.1
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• pp.67-77
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• 2017

In this study, we analyzed the characteristics of flow around the Daeyeon automatic weather station (AWS 942) and established formulas estimating inflow wind speeds at a computational fluid dynamics (CFD) model domain for the area around Pukyong national university using a computational fluid dynamics (CFD) model. Simulated wind directions at the AWS 942 were quite similar to those of inflows, but, simulated wind speeds at the AWS 942 decreased compared to inflow wind speeds except for the northerly case. The decrease in simulated wind speed at the AWS 942 resulted from the buildings around the AWS 942. In most cases, the AWS 942 was included within the wake region behind the buildings. Wind speeds at the inflow boundaries of the CFD model domain were estimated by comparing simulated wind speeds at the AWS 942 and inflow boundaries and systematically increasing inflow wind speeds from $1m\;s^{-1}$ to $17m\;s^{-1}$ with an increment of $2m\;s^{-1}$ at the reference height for 16 inflow directions. For each inflow direction, calculated wind speeds at the AWS 942 were fitted as the third order functions of the inflow wind speed by using the Marquardt-Levenberg least square method. Estimated inflow wind speeds by the established formulas were compared to wind speeds observed at 12 coastal AWSs near the AWS 942. The results showed that the estimated wind speeds fell within the inter quartile range of wind speeds observed at 12 coastal AWSs during the nighttime and were in close proximity to the upper whiskers during the daytime (12~15 h).

• Economic and Environmental Geology
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• v.32 no.2
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• pp.177-187
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• 1999

Gravity, magnetic and VLF surveys were conducted to investigat the structural stability and hazards associated with the Ubong landfill in Pohang City, which has been built to dump industrial wastes. In 1994, the collapse of a bank happened in the 6th landfill site due to sudden heavy rain, and a large quantity of waste materials flowed out to the nearby landfill sites, factories and roads. We used $10{\times}10m$ resolution DEM data for gravity reductions. The maximum variation of the terrain effect in the survey area is about 0.5 mgal and the terrain effect is large in the vicinity of bank boundary. The Bouguer gravity anomaly map shows the effect due to the variatino of thickness and type of waste materials. The small negative gravity anomaly increases from the 9th site to the 6th site. The small negative gravity anomaly of the 9th site reflects the relatively shallow dumping depth of average 14.5 m in this site and increased density of waste materials by the repeated stabilization process of soil overlaying. The 6th site is located at the center of the former valley and rainfall and groundwater are expected to flow from south-east to north-west. Therefore, considering the previous accident of mixing waste and bank materials at the north-west boundary of the landfill, there may be some environmental problems of leakage of contaminated water and bank stability. The complex inversion technique using Simulated annealing and Marquardt-Levenberg methods was applied to calculate three-dimensional density distribution from gravity data. In the case of 6th site, it is apparent that the landfill had been dumped in four sectors. However, most part of the 9th site and showed that high magnetic industrial wastes were concentrated in the 6th site. The result of magnetic survey showing low magnetic anomalies along the boundaries of two sites is similar to that of gravity data. The VLF data also reveals four divided sectors in the 6th site, and overall anomaly trend indicates the directio of former valley.

• Journal of the Society of Naval Architects of Korea
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• v.52 no.1
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• pp.88-95
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• 2015

For the frequency-domain spectral fatigue analysis, the probability density function of stress range needs to be estimated based on the stress spectrum only, which is a frequency domain representation of the response. The probability distribution of the stress range of the narrow-band spectrum is known to follow the Rayleigh distribution, however the PDF of wide-band spectrum is difficult to define with clarity due to the complicated fluctuation pattern of spectrum. In this paper, efforts have been made to figure out the links between the probability density function of stress range to the structural response of wide-band Gaussian random process. An artificial neural network scheme, known as one of the most powerful system identification methods, was used to identify the multivariate functional relationship between the idealized wide-band spectrums and resulting probability density functions. To achieve this, the spectrums were idealized as a superposition of two triangles with arbitrary location, height and width, targeting to comprise wide-band spectrum, and the probability density functions were represented by the linear combination of equally spaced Gaussian basis functions. To train the network under supervision, varieties of different wide-band spectrums were assumed and the converged probability density function of the stress range was derived using the rainflow counting method and all these data sets were fed into the three layer perceptron model. This nonlinear least square problem was solved using Levenberg-Marquardt algorithm with regularization term included. It was proven that the network trained using the given data set could reproduce the probability density function of arbitrary wide-band spectrum of two triangles with great success.