• 제목/요약/키워드: Soft-Computing

검색결과 209건 처리시간 0.026초

얀센 키네틱스를 기반으로 한 보행 로봇 개발 (Development of a Legged Walking Robot Based on Jansen Kinetics)

  • 김선욱;김연균;정하민;이세한;황승국;김동헌
    • 한국지능시스템학회논문지
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    • 제20권4호
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    • pp.509-515
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    • 2010
  • 부산과 경남의 경우 해안가뿐 만 아니라 습지 지역과 지역을 통과하는 낙동강의 넓은 남해안 벨트 등 다양한 형태의 환경으로 둘러싸여있다. 이러한 다양한 형태의 부정형 지역에서 활동할 수 있는 로봇의 이동 플랫폼이 요구되고 있다. 하지만 이러한 지형에 바퀴가 달린 감시로봇을 사용한다면 모래나 습지표면에 바퀴가 빠져 움직일 수 없다. 또한, 관절 로봇은 속도가 느리고 몸체가 움직일 때마다 기울어져 넘어질 수 있다. 따라서 본 연구에서는 모래 위, 습지에서 효과적으로 보행할 수 있는 메커니즘을 개발하는 것이다. 개발한 생물체 로봇에 카메라, 적외선 센서 등을 장착하고, 이 센서들을 이용하여 소프트 컴퓨팅 알고리즘을 이용하여 주위 환경에 반응하도록 한다. 또한 블루투스 통신 모듈을 장착하여 외부와 통신하며 외부의 명령에 순응하는 행동을 할 수 있게 한다. 본 연구의 최종 목표는 습지, 모래, 물 위에서 가장 적합하게 작동할 수 있는 생물체를 로봇으로 구현하고, 게 로봇에 부착된 카메라 정보를 통해 필요부분을 감시할 수 있으며, 컴퓨터에 전송된 화면을 보고 사용자가 로봇을 제어 할 수 있는 지능형 로봇을 제작하는 것이다.

Application of Extreme Learning Machine (ELM) and Genetic Programming (GP) to design steel-concrete composite floor systems at elevated temperatures

  • Shariati, Mahdi;Mafipour, Mohammad Saeed;Mehrabi, Peyman;Zandi, Yousef;Dehghani, Davoud;Bahadori, Alireza;Shariati, Ali;Trung, Nguyen Thoi;Salih, Musab N.A.;Poi-Ngian, Shek
    • Steel and Composite Structures
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    • 제33권3호
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    • pp.319-332
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    • 2019
  • This study is aimed to predict the behaviour of channel shear connectors in composite floor systems at different temperatures. For this purpose, a soft computing approach is adopted. Two novel intelligence methods, including an Extreme Learning Machine (ELM) and a Genetic Programming (GP), are developed. In order to generate the required data for the intelligence methods, several push-out tests were conducted on various channel connectors at different temperatures. The dimension of the channel connectors, temperature, and slip are considered as the inputs of the models, and the strength of the connector is predicted as the output. Next, the performance of the ELM and GP is evaluated by developing an Artificial Neural Network (ANN). Finally, the performance of the ELM, GP, and ANN is compared with each other. Results show that ELM is capable of achieving superior performance indices in comparison with GP and ANN in the case of load prediction. Also, it is found that ELM is not only a very fast algorithm but also a more reliable model.

Seismic evaluation of vertically irregular building frames with stiffness, strength, combined-stiffness-and-strength and mass irregularities

  • Nezhad, Moosa Ebrahimi;Poursha, Mehdi
    • Earthquakes and Structures
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    • 제9권2호
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    • pp.353-373
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    • 2015
  • In this paper, the effects of different types of irregularity along the height on the seismic responses of moment resisting frames are investigated using nonlinear dynamic analysis. Furthermore, the applicability of consecutive modal pushover (CMP) procedure for computing the seismic demands of vertically irregular frames is studied and the advantages and limitations of the procedure are elaborated. For this purpose, a special moment resisting steel frame of 10-storey height was selected as reference regular frame for which the effect of higher modes is important. Forty vertically irregular frames with stiffness, strength, combined-stiffness-and-strength and mass irregularities are created by applying two modification factors (MF=2 and 4) in four different locations along the height of the reference frame. Seismic demands of irregular frames are computed by using the nonlinear response history analysis (NL-RHA) and CMP procedure. Modal pushover analysis (MPA) method is also carried out for the sake of comparison. The effect of different types of irregularity along the height on the seismic demands of vertically irregular frames is investigated by studying the results obtained from the NL-RHA. To demonstrate the accuracy of the enhanced pushover analysis methods, the results derived from the CMP and MPA are compared with those obtained by benchmark solution, i.e., NL-RHA. The results show that the CMP and MPA methods can accurately compute the seismic demands of vertically irregular buildings. The methods may be, however, less accurate especially in estimating plastic hinge rotations for weak or weak-and-soft top and middle storeys of vertically irregular frames.

Predictive modeling of the compressive strength of bacteria-incorporated geopolymer concrete using a gene expression programming approach

  • Mansouri, Iman;Ostovari, Mobin;Awoyera, Paul O.;Hu, Jong Wan
    • Computers and Concrete
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    • 제27권4호
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    • pp.319-332
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    • 2021
  • The performance of gene expression programming (GEP) in predicting the compressive strength of bacteria-incorporated geopolymer concrete (GPC) was examined in this study. Ground-granulated blast-furnace slag (GGBS), new bacterial strains, fly ash (FA), silica fume (SF), metakaolin (MK), and manufactured sand were used as ingredients in the concrete mixture. For the geopolymer preparation, an 8 M sodium hydroxide (NaOH) solution was used, and the ambient curing temperature (28℃) was maintained for all mixtures. The ratio of sodium silicate (Na2SiO3) to NaOH was 2.33, and the ratio of alkaline liquid to binder was 0.35. Based on experimental data collected from the literature, an evolutionary-based algorithm (GEP) was proposed to develop new predictive models for estimating the compressive strength of GPC containing bacteria. Data were classified into training and testing sets to obtain a closed-form solution using GEP. Independent variables for the model were the constituent materials of GPC, such as FA, MK, SF, and Bacillus bacteria. A total of six GEP formulations were developed for predicting the compressive strength of bacteria-incorporated GPC obtained at 1, 3, 7, 28, 56, and 90 days of curing. 80% and 20% of the data were used for training and testing the models, respectively. R2 values in the range of 0.9747 and 0.9950 (including train and test dataset) were obtained for the concrete samples, which showed that GEP can be used to predict the compressive strength of GPC containing bacteria with minimal error. Moreover, the GEP models were in good agreement with the experimental datasets and were robust and reliable. The models developed could serve as a tool for concrete constructors using geopolymers within the framework of this research.

Indian Research on Artificial Neural Networks: A Bibliometric Assessment of Publications Output during 1999-2018

  • Gupta, B.M.;Dhawan, S.M.
    • International Journal of Knowledge Content Development & Technology
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    • 제10권4호
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    • pp.29-46
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    • 2020
  • The paper describes the quantitative and qualitative dimensions of artificial neural networks (ANN) in India in the global context. The study is based on research publications data (8260) as covered in the Scopus database during 1999-2018. ANN research in India registered 24.52% growth, averaged 11.95 citations per paper, and contributed 9.77% share to the global ANN research. ANN research is skewed as the top 10 countries account for 75.15% of global output. India ranks as the third most productive country in the world. The distribution of research by type of ANN networks reveals that Feed Forward Neural Network type accounted for the highest share (10.18% share), followed by Adaptive Weight Neural Network (5.38% share), Feed Backward Neural Network (2.54% share), etc. ANN research applications across subjects were the largest in medical science and environmental science (11.82% and 10.84% share respectively), followed by materials science, energy, chemical engineering and water resources (from 6.36% to 9.12%), etc. The Indian Institute of Technology, Kharagpur and the Indian Institute of Technology, Roorkee lead the country as the most productive organizations (with 289 and 264 papers). Besides, the Indian Institute of Technology, Kanpur (33.04 and 2.76) and Indian Institute of Technology, Madras (24.26 and 2.03) lead the country as the most impactful organizations in terms of citation per paper and relative citation index. P. Samui and T.N. Singh have been the most productive authors and G.P.S.Raghava (86.21 and 7.21) and K.P. Sudheer (84.88 and 7.1) have been the most impactful authors. Neurocomputing, International Journal of Applied Engineering Research and Applied Soft Computing topped the list of most productive journals.

Calculating the collapse margin ratio of RC frames using soft computing models

  • Sadeghpour, Ali;Ozay, Giray
    • Structural Engineering and Mechanics
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    • 제83권3호
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    • pp.327-340
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    • 2022
  • The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.

Three dimensional dynamic soil interaction analysis in time domain through the soft computing

  • Han, Bin;Sun, J.B.;Heidarzadeh, Milad;Jam, M.M. Nemati;Benjeddou, O.
    • Steel and Composite Structures
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    • 제41권5호
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    • pp.761-773
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    • 2021
  • This study presents a 3D non-linear finite element (FE) assessment of dynamic soil-structure interaction (SSI). The numerical investigation has been performed on the time domain through a Finite Element (FE) system, while considering the nonlinear behavior of soil and the multi-directional nature of genuine seismic events. Later, the FE outcomes are analyzed to the recorded in-situ free-field and structural movements, emphasizing the numerical model's great result in duplicating the observed response. In this work, the soil response is simulated using an isotropic hardening elastic-plastic hysteretic model utilizing HSsmall. It is feasible to define the non-linear cycle response from small to large strain amplitudes through this model as well as for the shift in beginning stiffness with depth that happens during cyclic loading. One of the most difficult and unexpected tasks in resolving soil-structure interaction concerns is picking an appropriate ground motion predicted across an earthquake or assessing the geometrical abnormalities in the soil waves. Furthermore, an artificial neural network (ANN) has been utilized to properly forecast the non-linear behavior of soil and its multi-directional character, which demonstrated the accuracy of the ANN based on the RMSE and R2 values. The total result of this research demonstrates that complicated dynamic soil-structure interaction processes may be addressed directly by passing the significant simplifications of well-established substructure techniques.

Tunnel wall convergence prediction using optimized LSTM deep neural network

  • Arsalan, Mahmoodzadeh;Mohammadreza, Taghizadeh;Adil Hussein, Mohammed;Hawkar Hashim, Ibrahim;Hanan, Samadi;Mokhtar, Mohammadi;Shima, Rashidi
    • Geomechanics and Engineering
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    • 제31권6호
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    • pp.545-556
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    • 2022
  • Evaluation and optimization of tunnel wall convergence (TWC) plays a vital role in preventing potential problems during tunnel construction and utilization stage. When convergence occurs at a high rate, it can lead to significant problems such as reducing the advance rate and safety, which in turn increases operating costs. In order to design an effective solution, it is important to accurately predict the degree of TWC; this can reduce the level of concern and have a positive effect on the design. With the development of soft computing methods, the use of deep learning algorithms and neural networks in tunnel construction has expanded in recent years. The current study aims to employ the long-short-term memory (LSTM) deep neural network predictor model to predict the TWC, based on 550 data points of observed parameters developed by collecting required data from different tunnelling projects. Among the data collected during the pre-construction and construction phases of the project, 80% is randomly used to train the model and the rest is used to test the model. Several loss functions including root mean square error (RMSE) and coefficient of determination (R2) were used to assess the performance and precision of the applied method. The results of the proposed models indicate an acceptable and reliable accuracy. In fact, the results show that the predicted values are in good agreement with the observed actual data. The proposed model can be considered for use in similar ground and tunneling conditions. It is important to note that this work has the potential to reduce the tunneling uncertainties significantly and make deep learning a valuable tool for planning tunnels.

레이저 스캐닝 측점군에 의한 터널 3차원 형상의 재현 (3D Tunnel Shape Fitting by Means of Laser Scanned Point Cloud)

  • 권기욱;이종달
    • 대한토목학회논문집
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    • 제29권4D호
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    • pp.555-561
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    • 2009
  • 굴착된 터널 형상 재현에서 단면의 종단 데이터는 터널의 유지를 위해서는 아주 중요하다. 터널이 완성되기전에 설계된 모델을 고려한 완성된 터널의 변형이 고려되어져야 한다. 그리고 변형은 터널 단면 전체를 따라 연속적으로 나타날 수 있다. 본 연구에서는 먼저 수학적 분석으로 접근하였고, 그것을 관측된 터널단면 데이터에 실험 하였다. 그 다음 선추적 방법, 유전자 알고리즘, 패턴 추적 방법 등으로 3D 터널 형상 재현을 비교하였다. 수학적 방법론은 철도 터널과 같은 간단한 원통형은 쉽게 해결이 되었으나, 도로터널과 같은 더욱 복잡한 모델(복심 곡선형과 비원통형)은 구속된 상태하에서 소프트 컴퓨팅 툴을 가지고 해결할 수 있었다. 유전자 알고리즘과 직접탐색법은 많은 계산 시간이 걸렸으나 복잡한 상태하에서 더욱 유연함을 보였으며, 선추적 방법은 초기값들이 제한된 범위 하에서 가장 빠르게 계산되어졌다.

Ensembles of neural network with stochastic optimization algorithms in predicting concrete tensile strength

  • Hu, Juan;Dong, Fenghui;Qiu, Yiqi;Xi, Lei;Majdi, Ali;Ali, H. Elhosiny
    • Steel and Composite Structures
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    • 제45권2호
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    • pp.205-218
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    • 2022
  • Proper calculation of splitting tensile strength (STS) of concrete has been a crucial task, due to the wide use of concrete in the construction sector. Following many recent studies that have proposed various predictive models for this aim, this study suggests and tests the functionality of three hybrid models in predicting the STS from the characteristics of the mixture components including cement compressive strength, cement tensile strength, curing age, the maximum size of the crushed stone, stone powder content, sand fine modulus, water to binder ratio, and the ratio of sand. A multi-layer perceptron (MLP) neural network incorporates invasive weed optimization (IWO), cuttlefish optimization algorithm (CFOA), and electrostatic discharge algorithm (ESDA) which are among the newest optimization techniques. A dataset from the earlier literature is used for exploring and extrapolating the STS behavior. The results acquired from several accuracy criteria demonstrated a nice learning capability for all three hybrid models viz. IWO-MLP, CFOA-MLP, and ESDA-MLP. Also in the prediction phase, the prediction products were in a promising agreement (above 88%) with experimental results. However, a comparative look revealed the ESDA-MLP as the most accurate predictor. Considering mean absolute percentage error (MAPE) index, the error of ESDA-MLP was 9.05%, while the corresponding value for IWO-MLP and CFOA-MLP was 9.17 and 13.97%, respectively. Since the combination of MLP and ESDA can be an effective tool for optimizing the concrete mixture toward a desirable STS, the last part of this study is dedicated to extracting a predictive formula from this model.