• Journal of the Earthquake Engineering Society of Korea
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• v.8 no.6 s.40
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• pp.13-22
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• 2004

Ductiluty factor has played an important role in seismic design as it is key component of response modification factor(R). In this stuty, ductility factors() are calculated by multiplying ductility factor for SDOF systems() and MDOF modification factors(). Ductility factors() for SDOF systems are computed from nonlinear dynamic analysis undergoing different level of displacement ductiluty demands and period when subjected to a large number of recorded earthquake ground motions. The MDOF modification factors() are proposed to account for the MDOF systems, based on previous studies. A total of 108 prototype steel frames are designed to investigate the ductility factors considering the number of stories(4, 8 and 16-stories), framing system(Perimeter Frames, PF and Distributed Frames, DF), failure mechanism(Strong-Column Weak-Beam, SCWB and Weak-Column Strong-Beam, WCSB), soil profiles(SA, SC and SE in UBC 1997) and seismic zone factors(Z=0.075, 0.2 and 0.4 in UBC 1997). It is shown that the number of stories, failure mechanisms (SCWB, WCSB), and soil profiles have great influence on the ductility factors, however, the structural system(Perimeter frames, Distributed frames), and seismic zones have no influence on the ductility factors.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.6
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• pp.264-273
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• 2006

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) by means of consecutive optimization and also discuss its comprehensive design methodology involving mechanisms of genetic optimization. The network is based on a structurally as well as parametrically optimized fuzzy polynomial neurons (FPNs) conducted with the aid of information granulation and genetic algorithms. In structurally identification of FPN, the design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). For the parametric identification, we obtained the effective model that the axes of MFs are identified by GA to reflect characteristic of given data. Especially, the genetically dynamic search method is introduced in the identification of parameter. It helps lead to rapidly optimal convergence over a limited region or a boundary condition. To evaluate the performance of the proposed model, the model is experimented with using two time series data(gas furnace process, nonlinear system data, and NOx process data).

• Smart Structures and Systems
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• v.23 no.5
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• pp.449-466
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• 2019

Cable-stayed bridges are attractive due to their beauty, reducing material consumption, less harm to the environment and so on, in comparison with other kinds of bridges. As a massive structure with long period and low damping (0.3 to 2%) under many dynamic loads, these bridges are susceptible to fatigue, serviceability disorder, damage or even collapse. Tuned Mass Damper (TMD) is a suitable controlling system to reduce the vibrations and prevent the threats in such bridges. In this paper, Multi Tuned Mass Damper (MTMD) system is added to the Ahvaz cable stayed Bridge in Iran, to reduce its seismic vibrations. First, the bridge is modeled in SAP2000 followed with result verification. Dead and live loads and the moving loads have been assigned to the bridge. Then the finite element model is developed in OpenSees, with the goal of running a nonlinear time-history analysis. Three far-field and three near-field earthquake records are imposed to the model after scaling to the PGA of 0.25 g, 0.4 g, 0.55 g and 0.7 g. Two MTMD systems, passive and active, with the number of TMDs from 1 to 8, are placed in specific points of the main span of bridge, adding a total mass ratio of 1 to 10% to the bridge. The parameters of the TMDs are optimized using Genetic Algorithm (GA). Also, the optimum force for active control is achieved by Fuzzy Logic Control (FLC). The results showed that the maximum displacement of the center of the bridge main span reduced 33% and 48% respectively by adding passive and active MTMD systems. The RMS of displacement reduced 37% and 47%, the velocity 36% and 42% and also the base shear in pylons, 27% and 47%, respectively by adding passive and active systems, in the best cases.

• Advances in Computational Design
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• v.7 no.3
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• pp.229-251
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• 2022

In 2017, an intraplate earthquake of Mw 7.1 occurred 120 km from Mexico City (CDMX). Most collapsed structural buildings stroked by the earthquake were flat slab systems joined to reinforced concrete (RC) columns, unreinforced masonry, confined masonry, and dual systems. This article presents the simulated response of an actual six-story RC frame building with masonry infill walls that did not collapse during the 2017 earthquake. It has a structural system similar to that of many of the collapsed buildings and is located in a high seismic amplification zone. Five 3D numerical models were used in the study to model the seismic response of the building. The building dynamic properties were identified using an ambient vibration test (AVT), enabling validation of the building's finite element models. Several assumptions were made to calibrate the numerical model to the properties identified from the AVT, such as the presence of adjacent buildings, variations in masonry properties, soil-foundation-structure interaction, and the contribution of non-structural elements. The results showed that the infill masonry wall would act as a compression strut and crack along the transverse direction because the shear stresses in the original model (0.85 MPa) exceeded the shear strength (0.38 MPa). In compression, the strut presents lower stresses (3.42 MPa) well below its capacity (6.8 MPa). Although the non-structural elements were not considered to be part of the lateral resistant system, the results showed that these elements could contribute by resisting part of the base shear force, reaching a force of 82 kN.

• Journal of Astronomy and Space Sciences
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• v.26 no.1
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• pp.75-88
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• 2009

The main purpose of the current research is to introduce the alternative algorithm of the non-recursive batch filter based on the unscented transformation in which the linearization process is unnecessary. The presented algorithm is applied to the orbit determination of a low earth orbiting satellite and compared its results with those of the well-known Bayesian batch least squares estimation and the iterative UKF smoother (IUKS). The system dynamic equations consist of the Earth's geo-potential, the atmospheric drag, solar radiation pressure and the lunar/solar gravitational perturbations. The range, azimuth and elevation angles of the satellite measured from ground stations are used for orbit determination. The characteristics of the non recursive unscented batch filter are analyzed for various aspects, including accuracy of the determined orbit, sensitivity to the initial uncertainty, measurement noise and stability performance in a realistic dynamic system and measurement model. As a result, under large non-linear conditions, the presented non-recursive batch filter yields more accurate results than the other batch filters about 5% for initial uncertainty test and 12% for measurement noise test. Moreover, the presented filter exhibits better convergence reliability than the Bayesian least squares. Hence, it is concluded that the non-recursive batch filter based on the unscented transformation is effectively applicable for highly nonlinear batch estimation problems.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.2
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• pp.377-387
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• 2014

This study mainly treats a new type of the bracing friction damper system, which is able to minimize structural damage under earthquake loads. The slotted bolt holes are placed on the shear faying surfaces with an intention to dissipate considerable amount of friction energy. The superelastic shape memory alloy (SMA) wire strands are installed crossly between two plates for the purpose of enhancing recentering force that are able to reduce permanent deformation occurring at the friction damper system. The smart recentering friction damper system proposed in this study can be expected to reduce repair cost as compared to the conventional damper system because the proposed system mitigates the inter-story drift of the entire frame structure. The response mechanism of the proposed damper system is firstly investigated in this study, and then numerical analyses are performed on the component spring models calibrated to the experimental results. Based on the numerical analysis results, the seismic performance of the recentering friction damper system with respect to recentering capability and energy dissipation are investigated before suggesting optimal design methodology. Finally, nonlinear dynamic analyses are conducted by using the frame models designed with the proposed damper systems so as to verify superior performance to the existing damper systems.

• Journal of Appropriate Technology
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• v.6 no.2
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• pp.136-145
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• 2020

Agriculture is the backbone of the economy of most developing countries. In these countries, agriculture or farming is mostly done manually with little integration of machinery, intelligent systems and data monitoring. Irrigation is an essential process that directly influences crop production. The fluctuating amount of rainfall per year has led to the adoption of irrigation systems in most farms. The absence of smart sensors, monitoring methods and control, has led to low harvests and draining water sources. In this research paper, we introduce a 433 MHz Radio Frequency and 2G based Smart Irrigation Meter System and a water prepayment system for rural areas of Tanzania with no reliable internet coverage. Specifically, Ngurudoto area in Arusha region where it will be used as a case study for data collection. The proposed system is hybrid, comprising of both weather data (evapotranspiration) and soil moisture data. The architecture of the system has on-site weather measurement controllers, soil moisture sensors buried on the ground, water flow sensors, a solenoid valve, and a prepayment system. To achieve high precision in linear and nonlinear regression and to improve classification and prediction, this work cascades a Dynamic Regression Algorithm and Naïve Bayes algorithm.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.18-31
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• 2002

In this study, we introduce a concept of advanced neurofuzzy polynomial networks(ANFPN), a hybrid modeling architecture combining neurofuzzy networks(NFN) and polynomial neural networks(PNN). These networks are highly nonlinear rule-based models. The development of the ANFPN dwells on the technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks and genetic algorithms. NFN contributes to the formation of the premise part of the rule-based structure of the ANFPN. The consequence part of the ANFPN is designed using PNN. At the premise part of the ANFPN, NFN uses both the simplified fuzzy inference and error back-propagation learning rule. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. As the consequence structure of ANFPN, PNN is a flexible network architecture whose structure(topology) is developed through learning. In particular, the number of layers and nodes of the PNN are not fixed in advance but is generated in a dynamic way. In this study, we introduce two kinds of ANFPN architectures, namely the basic and the modified one. Here the basic and the modified architecture depend on the number of input variables and the order of polynomial in each layer of PNN structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the ANFPN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed ANFPN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Korean Chemical Engineering Research
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• v.49 no.5
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• pp.560-564
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• 2011

The objective of this study is to propose a mathematical model for a pervaporation process for concentrating hydrogen peroxide. The process was developed by NASA, which consists of a shell and membrane tubes, where a liquid hydrogen peroxide solution flows in the shell, and a sweep gas flows in the tubes countercurrent to each other. The liquid retentate is concentrated as more water molecules permeate and evaporate through the membrane than hydrogen peroxide. For this process, a mathematical model has been developed in the form of a system of nonlinear partial differential algebraic equations based on a sorption-diffusion mechanism for permeation, an Arrhenius relationship for the temperature dependency of the permeate flux, and mass and momentum balances for the liquid concentrations and flows in the membrane module. The dynamic behavior of the concentration of hydrogen peroxide in the retentate side has been simulated by solving a simplified version of the proposed model, and the result is compared with the experimental data reported in the NASA patent.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.37-47
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• 1998

This paper proposes a controller design method using the evolutionary computation algorithm and the fuzzy logic to control the wafer temperature in rapid thermal processing. First, we design the feedforward static controller to provide the control powers of the lamps for the given steady state temperature. Second, the feedforward dynamic controller is designed for the additional control powers to achieve a given transient response. These feedforward controllers are implemented by using the fuzzy logic to act as a global nonlinear controller over a wide range of operating points. The parameters of these controllers are optimized by using the evolutionary computation algorithm so that it can be used when the mathematical model is not available. In addition, the feedback error controller is introduced to compensate the feedforward controllers when there exist disturbances and modeling errors. The gain of feedback error controller is also obtained by the evolutionary computation algorithm. Through simulations, we verify the proposed control system can give a satisfactory performance.