• Structural Engineering and Mechanics
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• 제82권5호
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• pp.667-678
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• 2022

Moment-resisting frames (MRFs) are among the most conventional steel structures for mid-rise buildings in many earthquake-prone cities. Here, a simplified performance-based methodology is proposed for the seismic collapse capacity assessment of these buildings. This method employs a novel multi-mode pushover analysis to determine the engineering demand parameters (EDPs) of the regular steel MRFs up to the collapse prevention (CP) performance level. The modal combination coefficients used in the proposed pushover analysis, are obtained from two metaheuristic optimization algorithms and a fitting procedure. The design variables for the optimization process are the inter-story drift ratio profiles resulting from the multi-mode pushover analyses, and the objective values are the outcomes of the incremental dynamic analysis (IDA). Here, the collapse capacity of the structures is assessed in three to five steps, using a modified IDA procedure. A series of regular mid-rise steel MRFs are selected and analyzed to calculate the modal combination coefficients and to validate the proposed approach. The new methodology is verified against the current existing approaches. This comparison shows that the suggested method more accurately evaluates the EDPs and the collapse capacity of the regular MRFs in a robust and easy to implement way.

• Structural Engineering and Mechanics
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• 제85권3호
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• pp.369-379
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• 2023

Reliability-Based Design Optimization (RBDO) is an appropriate framework for obtaining optimal designs by taking uncertainties into account. Large-scale problems with implicit limit state functions and problems with discrete design variables are two significant challenges to traditional RBDO methods. To overcome these challenges, this paper proposes a hybrid method to perform RBDO of structures that links Firefly Algorithm (FA) as an optimization tool to advanced (finite element) reliability methods. Furthermore, the Genetic Algorithm (GA) and the FA are compared based on the design cost (objective function) they achieve. In the proposed method, Weighted Simulation Method (WSM) is utilized to assess reliability constraints in the RBDO problems with explicit limit state functions. WSM is selected to reduce computational costs. To performing RBDO of structures with finite element modeling and implicit limit state functions, a First-Order Reliability Method (FORM) based on the Direct Differentiation Method (DDM) is utilized. Four numerical examples are considered to assess the effectiveness of the proposed method. The findings illustrate that the proposed RBDO method is applicable and efficient for RBDO problems with discrete and continuous design variables and finite element modeling.

• Smart Structures and Systems
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• 제29권4호
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• pp.523-533
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• 2022

A novel metaheuristic search method, namely black widow optimization (BWO) is employed to increase the accuracy of slope stability analysis. The BWO is a recently-developed optimizer that supervises the training of an artificial neural network (ANN) for predicting the factor of safety (FOS) of a single-layer cohesive soil slope. The designed slope bears a loaded foundation in different distances from the crest. A sensitivity analysis is conducted based on the number of active individuals in the BWO algorithm, and it was shown that the best performance is acquired for the population size of 40. Evaluation of the results revealed that the capability of the ANN was significantly enhanced by applying the BWO. In this sense, the learning root mean square error fell down by 23.34%. Also, the correlation between the testing data rose from 0.9573 to 0.9737. Therefore, the postposed BWO-ANN can be promisingly used for the early prediction of FOS in real-world projects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권4호
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• pp.1276-1295
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• 2023

Sensor networks are now an essential aspect of wireless communication, especially with the introduction of new gadgets and protocols. Their ability to be deployed anywhere, especially where human presence is undesirable, makes them perfect choices for remote observation and control. Despite their vast range of applications from home to hostile territory monitoring, limited battery power remains a limiting factor in their efficacy. To analyze and transmit data, it requires intelligent use of available battery power. Several studies have established effective routing algorithms based on clustering. However, choosing optimal cluster heads and similarity measures for clustering significantly increases computing time and cost. This work proposes and implements a simple two-phase technique of route creation and maintenance to ensure route reliability by employing nature-inspired ant colony optimization followed by the fuzzy decision engine (FDE). Benchmark methods such as PSO, ACO and GWO are compared with the proposed HRCM's performance. The objective has been focused towards establishing the superiority of proposed work amongst existing optimization methods in a standalone configuration. An average of 15% improvement in energy consumption followed by 12% improvement in latency reduction is observed in proposed hybrid model over standalone optimization methods.

• Smart Structures and Systems
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• 제29권5호
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• pp.677-691
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• 2022

Water cycle algorithm (WCA) has been a very effective optimization technique for complex engineering problems. This study employs the WCA for simultaneous prediction of heating load (L_H) and cooling load (L_C) in residential buildings. This algorithm is responsible for optimally tuning a neural network (NN). Utilizing 614 records, the behavior of the L_H and L_C is explored and the captured knowledge is then used to predict for 154 unanalyzed building conditions. Since the WCA is a population-based algorithm, different numbers of the searching agents were tested to find the most optimum configuration. It was observed that the best solution is discovered by 500 agents. A comparison with five newly-developed benchmark optimizers, namely equilibrium optimizer (EO), multi-tracker optimization algorithm (MTOA), slime mould algorithm (SMA), multi-verse optimizer (MVO), and electromagnetic field optimization (EFO) revealed that the WCANN predicts the desired parameters with considerably larger accuracy. Obtained root mean square errors (1.4866, 2.1296, 2.8279, 2.5727, 2.5337, and 2.3029 for the L_H and 2.1767, 2.6459, 3.1821, 2.9732, 2.9616, and 2.6890 for the L_C) indicated that the most reliable prediction was presented by the proposed model. The EFONN, however, provided a more time-effective solution. Lastly, an explicit predictive formula was elicited from the WCANN.

• Advances in materials Research
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• 제13권1호
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• pp.1-18
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• 2024

The present experimental investigation focuses on finding optimal parametric data-set of laser micro-drilling operation with minimum taper and Heat-affected zone during laser micro-drilling of Carbon Nanotube/Epoxy-based composite materials. Experiments have been conducted as per Box-Behnken design (BBD) techniques considering cutting speed, lamp current, pulse frequency and air pressure as input process parameters. Then, the relationship between control parameters and output responses is developed using second-order nonlinear regression models. The analysis of variance test has also been performed to check the adequacy of the developed mathematical model. Using the Response Surface Methodology (RSM) and an Accelerated particle swarm optimization (APSO) technique, optimum process parameters are evaluated and compared. Moreover, confirmation tests are conducted with the optimal parameter settings obtained from RSM and APSO and improvement in performance parameter is noticed in each case. The optimal process parameter setting obtained from predictive RSM based APSO techniques are speed=150 (m/s), current=22 (amp), pulse frequency (3 kHz), Air pressure (1 kg/cm²) for Taper and speed=150 (m/s), current=22 (amp), pulse frequency (3 kHz), air pressure (3 kg/cm²) for HAZ. From the confirmatory experimental result, it is observed that the APSO metaheuristic algorithm performs efficiently for optimizing the responses during laser micro-drilling process of nanocomposites both in individual and multi-objective optimization.

• 한국산학기술학회논문지
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• 제19권1호
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• pp.1-11
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• 2018

다양한 공학분야의 최적화 문제를 해결하기 위해 적절한 매개변수를 설정하기란 번거로운 작업이며, 매개변수 민감도 분석을 통해 적절한 매개변수를 설정하더라도 설정된 매개변수가 모든 문제에 적절한지 판단하기에는 한계가 있다. 이러한 이유로 매개변수를 문제에 따라 적절하게 설정하는 매개변수 자동검보정 (Self-adaptive) 화음탐색 알고리즘이 개발되고 발전하고 있다. 본 연구에서는 지금까지 개발된 자가적응형 하모니서치를 조사하고 그의 특성을 해탐색, 설정 매개변수, 적용성 등으로 구분하였으며, 이 중 매개변수 설정의 번거로움을 없애고, 적절한 매개변수 설정을 통해 해의 성능 향상을 위해 개발 된 6 가지 자가적응형 화음탐색 알고리즘을 선택하여 비교 분석을 수행하였다. 최적화 결과의 객관적인 비교를 위해 대표적인 수학적, 공학적 최적화 문제를 모두 적용 하였고, 다양한 성능 지수 (Performance index)를 사용하여 각 알고리즘의 성능을 정량적으로 비교하였다. 이것은 향후 신규 최적화 알고리즘을 개발하거나 해 탐색의 성능을 향상시키는 연구에 도움이 될 것으로 기대된다.

• 한국시뮬레이션학회논문지
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• 제28권4호
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• pp.21-32
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• 2019

군집에 대한 사회적 행동 모델에 영감을 받은 군집 최적화 알고리즘은 복잡한 최적화 문제 해결에서부터 인공 신경망의 학습에까지 활용되는 대표적인 메타휴리스틱 최적화 알고리즘 중의 하나이다. 하지만 이 알고리즘은 기본적으로 확률적 노이즈가 존재하지 않는 결정적인 환경에서 개발되었기 때문에, 많은 경우 확률적 노이즈가 존재하는 실제 문제에 적용하기에 어려움이 있었다. 본 논문에서는 이를 개선하기 위하여 불확실 평가 기법이라고 정의되는 통계적 가설 검정 기반의 리샘플링 기법을 적용한다. 이 기법을 통하여 입자 군집 최적화 알고리즘의 성능에 가장 큰 영향을 미치는 입자들의 전역 최적을 정확하게 찾으므로 노이즈 환경에서 입자들이 최적해로 보다 정확하고 빠르게 수렴하도록 한다. 다양한 벤치마크 문제들에 대한 기존 알고리즘들과의 비교 실험 결과는 제안하는 알고리즘의 개선된 성능을 입증하고, 사례 연구의 결과는 본 연구의 필요성을 강조한다. 본 연구 결과가 4차 산업혁명 시대에 디지털 트윈 등을 통한 시뮬레이션 기반 시스템 최적화에 효과적으로 적용될 수 있을 것이라 기대한다.

• Smart Structures and Systems
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• 제22권5호
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• pp.561-574
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• 2018

In this paper, for efficiently reducing the computational cost of the model updating during the optimization process of damage detection, the structural response is evaluated using properly trained surrogate model. Furthermore, in practice uncertainties in the FE model parameters and modelling errors are inevitable. Hence, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The current work builds a framework for Probability Based Damage Detection (PBDD) of structures based on the best combination of metaheuristic optimization algorithm and surrogate models. To reach this goal, three popular metamodeling techniques including Cascade Feed Forward Neural Network (CFNN), Least Square Support Vector Machines (LS-SVMs) and Kriging are constructed, trained and tested in order to inspect features and faults of each algorithm. Furthermore, three wellknown optimization algorithms including Ideal Gas Molecular Movement (IGMM), Particle Swarm Optimization (PSO) and Bat Algorithm (BA) are utilized and the comparative results are presented accordingly. Furthermore, efficient schemes are implemented on these algorithms to improve their performance in handling problems with a large number of variables. By considering various indices for measuring the accuracy and computational time of PBDD process, the results indicate that combination of LS-SVM surrogate model by IGMM optimization algorithm have better performance in predicting the of damage compared with other methods.

• Steel and Composite Structures
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• 제34권6호
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• pp.853-862
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• 2020

In recent years, due to the many advantages cable-stayed bridges have often constructed in medium and long span. These advantages can be listed as an aesthetically pleasing appearance, economic and easy construction, etc. The main structural elements of cable-stayed bridges are listed as deck, pylon, cables and foundation. Perhaps one of the most vital and expensive of these structural elements is stay-cables. Stay-cables ensure the allowable displacement and distribution of bending moments along the bridge deck with prestressing force. Therefore the optimum design of the stay-cables and prestressing force are very important in achieving the performance expected from the cable-stayed bridges. This paper aims to obtain the stay-cables size and prestressing force optimization of the cable-stayed bridge. For this purpose, single pylon and fan type cable configuration Manavgat Cable-Stayed Bridge was selected as an example. The three dimensional (3D) finite element model (FEM) of the bridge was created with SAP2000. Analysis of the 3D FEM of the bridge was conducted under the different combined effects of the self-weight of the structural element, prestressing force of stay-cable and live load. Stay-cable stress and deck displacement were taken into account as constraints for the optimization problem. To optimize this existing bridge a metaheuristic algorithm named Jaya was used in the optimization process. 3D FEM of the selected bridge was repeatedly analyzed by using Open Applicable Programming Interface (OAPI) properties of SAP2000. To carry out the optimization process the developed program which integrates the Jaya algorithm and the required codes for calling SAP2000 is coded in MATLAB. At the end of the study, the total weight of the stay-cables was reduced more than 40% according to existing stay cables under loads taken into account.