• Steel and Composite Structures
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• v.48 no.2
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• pp.179-190
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• 2023

Many recent attempts have sought accurate prediction of pile pullout resistance (P_ul) using classical machine learning models. This study offers an improved methodology for this objective. Adaptive neuro-fuzzy inference system (ANFIS), as a popular predictor, is trained by a capable metaheuristic strategy, namely equilibrium optimizer (EO) to predict the P_ul. The used data is collected from laboratory investigations in previous literature. First, two optimal configurations of EO-ANFIS are selected after sensitivity analysis. They are next evaluated and compared with classical ANFIS and two neural-based models using well-accepted accuracy indicators. The results of all five models were in good agreement with laboratory P_uls (all correlations > 0.99). However, it was shown that both EO-ANFISs not only outperform neural benchmarks but also enjoy a higher accuracy compared to the classical version. Therefore, utilizing the EO is recommended for optimizing this predictive tool. Furthermore, a comparison between the selected EO-ANFISs, where one employs a larger population, revealed that the model with the population size of 75 is more efficient than 300. In this relation, root mean square error and the optimization time for the EO-ANFIS (75) were 19.6272 and 1715.8 seconds, respectively, while these values were 23.4038 and 9298.7 seconds for EO-ANFIS (300).

• Computers and Concrete
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• v.32 no.2
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• pp.217-232
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• 2023

Numerous studies have been performed on the behavior of pile foundations in cold regions. This study first attempted to employ artificial neural networks (ANN) to predict pile-bearing capacity focusing on pile data recorded primarily on cold regions. As the ANN technique has disadvantages such as finding global minima or slower convergence rates, this study in the second phase deals with the development of an ANN-based predictive model improved with an Elephant herding optimizer (EHO), Dragonfly Algorithm (DA), Genetic Algorithm (GA), and Evolution Strategy (ES) methods for predicting the piles' bearing capacity. The network inputs included the pile geometrical features, pile area (m²), pile length (m), internal friction angle along the pile body and pile tip (Ø°), and effective vertical stress. The MLP model pile's output was the ultimate bearing capacity. A sensitivity analysis was performed to determine the optimum parameters to select the best predictive model. A trial-and-error technique was also used to find the optimum network architecture and the number of hidden nodes. According to the results, there is a good consistency between the pile-bearing DA-MLP-predicted capacities and the measured bearing capacities. Based on the R2 and determination coefficient as 0.90364 and 0.8643 for testing and training datasets, respectively, it is suggested that the DA-MLP model can be effectively implemented with higher reliability, efficiency, and practicability to predict the bearing capacity of piles.

• Geomechanics and Engineering
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• v.31 no.2
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• Atmosphere
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• v.33 no.5
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• pp.549-560
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• 2023

This study evaluates three distinct observation methods, CRDS, OA-ICOS, and OF-CEAS, in greenhouse gas monitoring equipment for atmospheric CO₂ and CH₄ concentrations. The assessment encompasses fundamental performance, high-concentration measurement accuracy, calibration methods, and the impact of atmospheric humidity on measurement accuracy. Results indicate that within a range of approximately 500 ppm, all three devices demonstrate high accuracy and linearity. However, beyond 1000 ppm, CO₂ accuracy sharply declines (84%), emphasizing the need for caution when interpreting high-concentration CO₂ data. An analysis of calibration methods reveals that both CO₂ and CH₄ measurements achieve high accuracy and linearity through 1-point calibration, suggesting that multi-point calibration is not imperative for precision. In dynamic atmospheric conditions with significant CO₂ and CH₄ concentration variations, a 1-point calibration suffices for reliable data (99% accuracy). The evaluation of humidity impact demonstrates that humidity removal devices significantly reduce air moisture levels, yet this has a negligible effect on dry CO₂ concentrations (less than 0.5% relative error). All three observation method instruments, which have integrated humidity correction to calculate dry CO₂ concentrations, exhibit minor sensitivity to humidity removal devices, implying that additional removal devices may not be essential. Consequently, this study offers valuable insights for comparing data from different measurement devices and provides crucial information to consider in the operation of monitoring sites.

• Earthquakes and Structures
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• v.26 no.5
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• pp.349-361
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• 2024

Excessive torsional behaviour is one of the major reasons for failure of buildings, as inferred from past earthquakes. Numerous seismic codes across the world specify a displacement-based or drift-based criterion for classifying buildings as torsionally irregular. In recent years, quite a few researchers have pointed out some of the inherent deficiencies associated with the current codal guidelines on torsional irregularity. This short communication paper aims to envisage the need for a revision of the displacement-based guidelines on torsional irregularity, and further highlight the appropriateness of a rotation-based criterion. A set of 6 reinforced concrete building models with asymmetric shear walls are analysed using ETABS v18.0.2, by varying the number of stories from 1 to 9, and the torsional irregularity coefficient of various stories is calculated using the displacement-based formula. Since rotation about the vertical axis is a direct indication of the twist experienced by a building, the calculated torsional irregularity coefficients of all stories are compared with the corresponding floor rotations. The conflicting results obtained for the torsional irregularity coefficients are projected through five categories, namely mismatch with floor rotations, inconsistency in trend, lack of clarity in incorporation of negative values, sensitivity to low values of displacement and error conceived in the mathematical formulation. The findings indicate that the irregularity coefficient does not accurately represent the torsional behaviour of buildings in a realistic sense. The Indian seismic code-based values of 1.2 and 1.4, which are used to characterize buildings as torsionally irregular are observed to be highly sensitive to the numerical values of displacements, rather than the actual degree of rotation. The study thus emphasizes the revision of current guidelines based on a more relevant rotation-based or eccentricity-based approach.

• The Korean Journal of Nuclear Medicine
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• v.39 no.6
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• pp.445-455
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• 2005

Purpose: We developed an animal SPECT system using clinical Philips ARGUS scintillation camera and pinhole collimator with specially manufactured small apertures. In this study, we evaluated the physical characteristics of this system and biological feasibility for animal experiments. Materials and Methods: Rotating station for small animals using a step motor and operating software were developed. Pinhole inserts with small apertures (diameter of 0.5, 1.0, and 2.0 mm) were manufactured and physical parameters including planar spatial resolution and sensitivity and reconstructed resolution were measured for some apertures. In order to measure the size of the usable field of view according to the distance from the focal point, manufactured multiple line sources separated with the same distance were scanned and numbers of lines within the field of view were counted. Using a Tc-99m line source with 0.5 mm diameter and 12 mm length placed in the exact center of field of view, planar spatial resolution according to the distance was measured. Calibration factor to obtain FWHM values in 'mm' unit was calculated from the planar image of two separated line sources. Te-99m point source with i mm diameter was used for the measurement of system sensitivity. In addition, SPECT data of micro phantom with cold and hot line inserts and rat brain after intravenous injection of [I-123]FP-CIT were acquired and reconstructed using filtered back protection reconstruction algorithm for pinhole collimator. Results: Size of usable field of view was proportional to the distance from the focal point and their relationship could be fitted into a linear equation (y=1.4x+0.5, x: distance). System sensitivity and planar spatial resolution at 3 cm measured using 1.0 mm aperture was 71 cps/MBq and 1.24 mm, respectively. In the SPECT image of rat brain with [I-123]FP-CIT acquired using 1.0 mm aperture, the distribution of dopamine transporter in the striatum was well identified in each hemisphere. Conclusion: We verified that this new animal SPECT system with the Phlilps ARGUS scanner and small apertures had sufficient performance for small animal imaging.

• Analytical Science and Technology
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• v.31 no.6
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• pp.240-246
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• 2018

The present study analyzed standard samples of three types of aluminum matrix certified reference materials (CRM) using GD-MS. Calibration curves were constructed for 13 elements (Mg, Si, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Ga, Sn, and Pb), with the slope representing the relative sensitivity factor (RSF). The x- and y-axes of the calibration curve represented ion beam ratio (IBR) and the authenticated value of the standard sample, respectively. In order to evaluate precision and linearity of the calibration curve, RSD and the coefficient of determination were calculated. Curve RSD for every element reflected high precision (within 10 %). For most elements, the coefficient of determination was ${\geq}0.99$, indicating excellent linearity. However, vanadium, nickel, and gallium curves exhibited relatively low linearity (0.90~0.95), likely due to their narrow concentration ranges. Standard RSF was calculated using the slope of the curve generated for three types of CRM. Despite vanadium, nickel, and gallium exhibiting low coefficients of determination, their standard RSF resembled that of the three types of CRM. Therefore, the RSF method may be used for element quantitation. Standard iron matrix samples were analyzed to verify the applicability of the aluminum matrix standard RSF, as well as to calculate the RSD-estimated error of the measured value relative to the actual standard value. Six elements (Al, Si, V, Cr, Mn, and Ni) exhibited an RSD of approximately 30 %, while the RSD of Cu was 77 %. In general, Cu isotopes are subject to interference: $^{63}Cu$ to $^{54}Fe^{2+}-^{36}Ar$ and $^{65}Cu$ to $^{56}Fe-Al^{3+}$ interference. Thus, the influence of these impurities may have contributed to the high RSD value observed for Cu. To reliably identify copper, the resolution should be set at ${\geq}8000$. However, high resolutions are inappropriate for analyzing trace elements, as it lowers ion permeability. In conclusion, quantitation of even relatively low amounts of six elements (Al, Si, V, Cr, Mn, and Ni) is possible using this method.

• Journal of the Korean earth science society
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• v.41 no.5
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• pp.469-477
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• 2020

Salinity is not only an important variable that determines the density of the ocean but also one of the main parameters representing the global water cycle. Ocean salinity observations have been mainly conducted using ships, Argo floats, and buoys. Since the first satellite salinity was launched in 2009, it is also possible to observe sea surface salinity in the global ocean using satellite salinity data. However, the satellite salinity data contain various errors, it is necessary to validate its accuracy before applying it as research data. In this study, the salinity accuracy between the Soil Moisture Active Passive (SMAP) satellite salinity data and the in-situ salinity data provided by the Ieodo ocean research station was evaluated, and the error characteristics were analyzed from April 2015 to August 2020. As a result, a total of 314 match-up points were produced, and the root mean square error (RMSE) and mean bias of salinity were 1.79 and 0.91 psu, respectively. Overall, the satellite salinity was overestimated compare to the in-situ salinity. Satellite salinity is dependent on various marine environmental factors such as season, sea surface temperature (SST), and wind speed. In summer, the difference between the satellite salinity and the in-situ salinity was less than 0.18 psu. This means that the accuracy of satellite salinity increases at high SST rather than at low SST. This accuracy was affected by the sensitivity of the sensor. Likewise, the error was reduced at wind speeds greater than 5 m s^-1. This study suggests that satellite-derived salinity data should be used in coastal areas for limited use by checking if they are suitable for specific research purposes.

• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.841-848
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• 2020

In the field of radiation therapy using photon beams and electron beams, since each patient has a different sensitivity to radiation, skin side effects may occur even at the same dose. Therefore, if there is a risk of excessive dose to the skin, a dosimeter is attached to verify whether the correct dose is being investigated. However, since the skin dosimeter checks the attachment site visually by measuring a point dose, it is difficult to confirm an accurate dose distribution. As a result, the measurement and simulation errors of the material HgI₂ in the 6 MV photon beam were 3.73% and 5.24%, respectively, at the minimum thickness of 25 ㎛, and the material PbI₂ was 4.73% and 5.65%, respectively. On the other hand, as a result of the 6 MeV electron beam, the measurement and simulation errors of the material HgI₂ were 1.35% and 1.12%, respectively, at a minimum thickness of 25 ㎛, and the material PbI₂ showed relatively low attenuation error, 1.67% and 1.20%, respectively. Therefore, it was evaluated that the thickness of the photon beam within 25 ㎛ and the electron beam within 100 ㎛ is suitable to have a reduction rate error within 5%. This study presents a new research direction for a flexible dosimeter attached to the human body that is required in clinical practice and the construction conditions of a future skin dosimeter.

• Journal of the Society of Disaster Information
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• v.16 no.4
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• pp.813-823
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• 2020

Purpose: In order to maximize the stability and productivity of the work through simulation prior to high-risk facilities and high-cost work such as dismantling the facilities inside the reactor, we intend to use digital twin technology that can be closely controlled by simulating the specifications of the actual control equipment. Motion control errors, which can be caused by the time gap between precision control equipment and simulation in applying digital twin technology, can cause hazards such as collisions between hazardous facilities and control equipment. In order to eliminate and control these situations, prior research is needed. Method: Unity 3D is currently the most popular engine used to develop simulations. However, there are control errors that can be caused by time correction within Unity 3D engines. The error is expected in many environments and may vary depending on the development environment, such as system specifications. To demonstrate this, we develop crash simulations using Unity 3D engines, which conduct collision experiments under various conditions, organize and analyze the resulting results, and derive tolerances for precision control equipment based on them. Result: In experiments with collision experiment simulation, the time correction in 1/1000 seconds of an engine internal function call results in a unit-hour distance error in the movement control of the collision objects and the distance error is proportional to the velocity of the collision. Conclusion: Remote decomposition simulators using digital twin technology are considered to require limitations of the speed of movement according to the required precision of the precision control devices in the hardware and software environment and manual control. In addition, the size of modeling data such as system development environment, hardware specifications and simulations imitated control equipment and facilities must also be taken into account, available and acceptable errors of operational control equipment and the speed required of work.