• Title, Summary, Keyword: ANN

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Prediction of creep in concrete using genetic programming hybridized with ANN

  • Hodhod, Osama A.;Said, Tamer E.;Ataya, Abdulaziz M.
    • Computers and Concrete
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    • v.21 no.5
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    • pp.513-523
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    • 2018
  • Time dependent strain due to creep is a significant factor in structural design. Multi-gene genetic programming (MGGP) and artificial neural network (ANN) are used to develop two models for prediction of creep compliance in concrete. The first model was developed by MGGP technique and the second model by hybridized MGGP-ANN. In the MGGP-ANN, the ANN is working in parallel with MGGP to predict errors in MGGP model. A total of 187 experimental data sets that contain 4242 data points are filtered from the NU-ITI database. These data are used in developing the MGGP and MGGP-ANN models. These models contain six input variables which are: average compressive strength at 28 days, relative humidity, volume to surface ratio, cement type, age at start of loading and age at the creep measurement. Practical equation based on MGGP was developed. A parametric study carried out with a group of hypothetical data generated among the range of data used to check the generalization ability of MGGP and MGGP-ANN models. To confirm validity of MGGP and MGGP-ANN models; two creep prediction code models (ACI209 and CEB), two empirical models (B3 and GL 2000) are used to compare their results with NU-ITI database.

Performance comparison of SVM and ANN models for solar energy prediction (태양광 에너지 예측을 위한 SVM 및 ANN 모델의 성능 비교)

  • Jung, Wonseok;Jeong, Young-Hwa;Park, Moon-Ghu;Lee, Chang-Kyo;Seo, Jeongwook
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.626-628
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    • 2018
  • In this paper, we compare the performances of SVM (Support Vector Machine) and ANN (Artificial Neural Network) machine learning models for predicting solar energy by using meteorological data. Two machine learning models were built by using fifteen kinds of weather data such as long and short wave radiation average, precipitation and temperature. Then the RBF (Radial Basis Function) parameters in the SVM model and the number of hidden layers/nodes and the regularization parameter in the ANN model were found by experimental studies. MAPE (Mean Absolute Percentage Error) and MAE (Mean Absolute Error) were considered as metrics for evaluating the performances of the SVM and ANN models. Sjoem Simulation results showed that the SVM model achieved the performances of MAPE=21.11 and MAE=2281417.65, and the ANN model did the performances of MAPE=19.54 and MAE=2155345.10776.

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Prediction of Shear Strength of FRP Concrete Beams without Stirrups by Artificial Neural Networks (인공신경망에 의한 스터럽 없는 FRP 콘크리트 보의 전단강도 예측)

  • Lee, Cha-Don;Kim, Won-Chul
    • Proceedings of the Korea Concrete Institute Conference
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    • pp.801-804
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    • 2008
  • Fiber reinforced plastics (FRP) are light in weight, non-corrosive and exhibits high tensile strength. FRPs having superior material properties to corrosive steels have been widely replacing steel bars or tendons used in concrete structures as flexural reinforcements. Although current design guidelines for estimating shear strength of FRP concrete beam follow the format of conventional reinforced concrete design method, there are noticeable differences among the existing formulas in calculating the contributions of concrete to shear resistance. In this paper, the artificial neural network (ANN) technique is employed as an analytical alternative to existing methods for predicting shear capacity of FRP concrete beams. Influential factors on shear strength were identified through literature review and input in ANN and the ANN was trained for the target ultimate shear obtained from database. The results from ANN were compared with existing formulas for its accuracy. It was found that the developed ANN were more closely predicting the test data than those of the currently available predictive equations.

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A Study of the Automatic Berthing System of a Ship Using Artificial Neural Network (인공신경망을 이용한 선박의 자동접안 제어에 관한 연구)

  • Bae, Cheol-Han;Lee, Seung-Keon;Lee, Sang-Eui;Kim, Ju-Han
    • Journal of Navigation and Port Research
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    • v.32 no.8
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    • pp.589-596
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    • 2008
  • In this paper, Artificial Neural Network(ANN) is applied to automatic berthing control for a ship. ANN is suitable for a maneuvering such as ship's berthing, because it can describe non-linearity of the system. Multi-layer perceptron which has more than one hidden layer between input layer and output layer is applied to ANN. Using a back-propagation algorithm with teaching data, we trained ANN to get a minimal error between output value and desired one. For the automatic berthing control of a containership, we introduced low speed maneuvering mathematical models. The berthing control with the structure of 8 input layer units in ANN is compared to 6 input layer units. From the simulation results, the berthing conditions are satisfied, even though the berthing paths are different.

Fault Diagnosis of a Rotating Blade using HMM/ANN Hybrid Model (HMM/ANN복합 모델을 이용한 회전 블레이드의 결함 진단)

  • Kim, Jong Su;Yoo, Hong Hee
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.23 no.9
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    • pp.814-822
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    • 2013
  • For the fault diagnosis of a mechanical system, pattern recognition methods have being used frequently in recent research. Hidden Markov model(HMM) and artificial neural network(ANN) are typical examples of pattern recognition methods employed for the fault diagnosis of a mechanical system. In this paper, a hybrid method that combines HMM and ANN for the fault diagnosis of a mechanical system is introduced. A rotating blade which is used for a wind turbine is employed for the fault diagnosis. Using the HMM/ANN hybrid model along with the numerical model of the rotating blade, the location and depth of a crack as well as its presence are identified. Also the effect of signal to noise ratio, crack location and crack size on the success rate of the identification is investigated.

KNN / ANN Hybrid algorithm Using indoor positioning Method (KNN/ANN Hybrid 알고리즘을 활용한 실내위치 측위 기법)

  • Kim, Beom-mu;Thapa, Prakash;Paudel, Prebesh;Jeong, Min-A;Lee, Seong-Ro
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.1205-1207
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    • 2015
  • Fingerprinting 방식에서 KNN은 WLAN 기반 실내 측위에 가장 많이 적용되고 있지만 KNN의 성능은 k개의 이웃 수와 RP의 수에 따라 민감하다. 논문에서는 KNN 성능을 향상시키기 위해 ANN 군집화를 적용한 KNN과 ANN을 혼합한 알고리즘을 제안하였다. 제안한 알고리즘은 신호잡음비 데이터를 KNN 방법에 적용하여 k개의 RP을 선택한 후 선택된 RP의 신호잡음비를 ANN에 적용하여 k개의 RP를 군집하여 분류한다. 실험 결과에서는 위치 오차가 2m 이내에서 KNN/ANN 알고리즘이 KNN 알고리즘보다 성능이 우수하다.

A new empirical formula for prediction of the axial compression capacity of CCFT columns

  • Tran, Viet-Linh;Thai, Duc-Kien;Kim, Seung-Eock
    • Steel and Composite Structures
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    • v.33 no.2
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    • pp.181-194
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    • 2019
  • This paper presents an efficient approach to generate a new empirical formula to predict the axial compression capacity (ACC) of circular concrete-filled tube (CCFT) columns using the artificial neural network (ANN). A total of 258 test results extracted from the literature were used to develop the ANN models. The ANN model having the highest correlation coefficient (R) and the lowest mean square error (MSE) was determined as the best model. Stability analysis, sensitivity analysis, and a parametric study were carried out to estimate the stability of the ANN model and to investigate the main contributing factors on the ACC of CCFT columns. Stability analysis revealed that the ANN model was more stable than several existing formulae. Whereas, the sensitivity analysis and parametric study showed that the outer diameter of the steel tube was the most sensitive parameter. Additionally, using the validated ANN model, a new empirical formula was derived for predicting the ACC of CCFT columns. Obviously, a higher accuracy of the proposed empirical formula was achieved compared to the existing formulae.

Hybrid ANN-based techniques in predicting cohesion of sandy-soil combined with fiber

  • Armaghani, Danial Jahed;Mirzaei, Fatemeh;Shariati, Mahdi;Trung, Nguyen Thoi;Shariati, Morteza;Trnavac, Dragana
    • Geomechanics and Engineering
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    • v.20 no.3
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    • pp.191-205
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    • 2020
  • Soil shear strength parameters play a remarkable role in designing geotechnical structures such as retaining wall and dam. This study puts an effort to propose two accurate and practical predictive models of soil shear strength parameters via hybrid artificial neural network (ANN)-based models namely genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. To reach the aim of this study, a series of consolidated undrained Triaxial tests were conducted to survey inherent strength increase due to addition of polypropylene fibers to sandy soil. Fiber material with different lengths and percentages were considered to be mixed with sandy soil to evaluate cohesion (as one of shear strength parameter) values. The obtained results from laboratory tests showed that fiber percentage, fiber length, deviator stress and pore water pressure have a significant impact on cohesion values and due to that, these parameters were selected as model inputs. Many GA-ANN and PSO-ANN models were constructed based on the most effective parameters of these models. Based on the simulation results and the computed indices' values, it is observed that the developed GA-ANN model with training and testing coefficient of determination values of 0.957 and 0.950, respectively, performs better than the proposed PSO-ANN model giving coefficient of determination values of 0.938 and 0.943 for training and testing sets, respectively. Therefore, GA-ANN can provide a new applicable model to effectively predict cohesion of fiber-reinforced sandy soil.

Artificial neural network controller for automatic ship berthing using head-up coordinate system

  • Im, Nam-Kyun;Nguyen, Van-Suong
    • International Journal of Naval Architecture and Ocean Engineering
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    • v.10 no.3
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    • pp.235-249
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    • 2018
  • The Artificial Neural Network (ANN) model has been known as one of the most effective theories for automatic ship berthing, as it has learning ability and mimics the actions of the human brain when performing the stages of ship berthing. However, existing ANN controllers can only bring a ship into a berth in a certain port, where the inputs of the ANN are the same as those of the teaching data. This means that those ANN controllers must be retrained when the ship arrives to a new port, which is time-consuming and costly. In this research, by using the head-up coordinate system, which includes the relative bearing and distance from the ship to the berth, a novel ANN controller is proposed to automatically control the ship into the berth in different ports without retraining the ANN structure. Numerical simulations were performed to verify the effectiveness of the proposed controller. First, teaching data were created in the original port to train the neural network; then, the controller was tested for automatic berthing in other ports, where the initial conditions of the inputs in the head-up coordinate system were similar to those of the teaching data in the original port. The results showed that the proposed controller has good performance for ship berthing in ports.

Estimating Reference Crop Evapotranspiration Using Artificial Neural Network and Temperature-based Climatic Data (인공신경망모형을 이용한 기온기반 기준증발산량 산정)

  • Lee, Sung-Hack;Kim, Maga;Choi, Jin-Yong;Bang, Jehong
    • Journal of The Korean Society of Agricultural Engineers
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    • v.61 no.1
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    • pp.95-105
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    • 2019
  • Evapotranpiration (ET) is one of the important factor in Hydrological cycle and irrigation planning. In this study, temperature-based artificial neural network (ANN) model for daily reference crop ET estimation was developed and compared with reference crop evapotranpiration ($ET_0$) from FAO-56 Penman-Monteith method (FAO-56 PM) and parameter regionalized Hargreaves method. The ANN model was trained and tested for 10 weather stations (5 inland stations and 5 costal stations) and two input climate factors, maximum temperature ($T_{max}$), minimum temperature ($T_{min}$), and extraterrestrial radiation (RA) were used for training and validation of temperature-based ANN model. Monthly reference ET by the ANN model also compared with parameter regionalized Hargreaves method for ANN model applicability evaluation. The ANN model evapotranspiration demonstrated more accordance to FAO-56 PM evapotranspiration than the $ET_0$ from parameter regionalized Hargreaves method(R-Hargreaves). The results of this study proposed that daily reference crop ET estimated by the ANN model could be used in the condition of no sufficient climate data.