Hybrid Artificial Neural Network Method Research Articles

Development of a hybrid artificial neural network method for evaluation of the sustainable construction projects

Planned methods may be developed to improve the efficiency of building construction. The construction business is profoundly impacted by the prevalence of inaccurate cost and schedule prediction. The main strategy to improve the project performance is to evaluate the hybrid sustainable materials using the artificial neural network (ANN) method based on the effective factors in construction projects in Iraq. This strategy needs an effective method to classify the project input representation and specify the accurate activity of each factor. This paper uses a hybrid artificial neural network to correlate and classify the sustainable hybrid of construction projects to evaluate their performance. The contribution of this method is the selection of the Multi-Criteria Decision-Maker method (MCDM) based on time and cost-effective factors correlated with the artificial neural network method. A dynamic selection procedure for project materials may be created using the existing technique as an evolutionary model for successful project completion. The MCDM observed that the appropriate sustainable material was considered as the main factor with a rank of 0.823 for cost effect and 0.735 for time effect and the main influence factor in Iraqi projects was the building height. The results present superior functional cost evaluation results correlated with the selection of hybrid sustainable materials.

NEAR SURFACE AIR TEMPERATURE ESTIMATION THROUGH PARAMETRIZATION OF MODIS PRODUCTS

Abstract. Near-surface air temperature is a key factor in many studies and its spatiotemporal patterns are highly dependent on the ground surface characteristics and vary over time and space. So Land Surface Temperature (LST) is an important parameter for air temperature estimation. In this study, it is tried to model the air temperature by deploying some of the parameters that affect it . The parameters that have been taken into account in this study include land surface temperature, Normalized Difference Vegetation Index (NDVI), Vapor Pressure (VP) and Lifted Index (LI) as a measure of atmospheric stability. To assess the impact of each of these parameters, different linear regression models, were tested. Support Vector Regression (SVR) and hybrid artificial neural network methods were also performed. To model and evaluate the time series data of Georgia State in The United States of America over 1 year have been used. The NDVI, Total Precipitable Water (TPW), LST and LI parameters are products of MODIS. VP is calculated by using a logarithmic model from the TPW. Finally, it was found out that the LST and VP have positive effects, LI has negative and NDVI had a slightly positive impact on the air temperature at 2 meters height. The achieved accuracy in the linear model when all parameters are involved was 2.29 °C with a correlation coefficient of R=0.96. Next, the SVR model was examined and applied to the linear model taking all parameters into account. It was found that it does not end up to any significant increase in accuracy but certainly increases the computation time. The accuracy of this model was about 2.25 °C with a correlation coefficient of 0.96. Finally, a hybrid artificial neural network was examined. It was found that it increases the accuracy but certainly increases the computation time. The achieved accuracy of this model was about 2.14 °C with a correlation coefficient of 0.96.

Measurement of oxygen content in water with purity through soft sensor model

The development and commercial use of virtualized sensing (smooth) in the manufacturing of Polyethylene Terephthalate (PET) and sewage treatment are both covered in this paper. The major purpose of using based on an input Artificial Neural Network (ANN) to give operations with real-time estimates of the PET permeability. Additionally, the redundancy viscosity measures provided by the ANN-based soft-sensor (ANNSES) were used to compare the outcomes of the procedure fully equipped. Everything was established that the proposed ANNSES could accurately infer the polymeric viscosity, allowing for the implementation of this soft sensor in real-time system integration. Multidisciplinary modeling, ANN, & a composite method that includes a primary clustering technique are used for data from industrial wastewater operations. The combined ANN approach decreases the overfitting problem with biological systems & improves forecasting accuracy. The results show that the hybrid ANN method can be used to both explain why difficult wastewater treatment processes are nonlinear & to obtain knowledge in imbalanced datasets.

Prediction of wind pressures on tall buildings using wavelet neural network

For wind-resistant design of tall buildings, it is routine to obtain complete surface wind pressure distribution based on experimental data recorded at limited locations. The main goal of this study is to examine the usability of a hybrid artificial neural network method, i.e., Wavelet Neural Network (WNN), for simulating and interpolating wind pressures on tall buildings. Wind pressure measurement tests were carried out on two scaled tall building models. The performance of three different prediction models, namely back-propagation neural network (BPNN), genetic algorithm-back-propagation neural network (GA-BP), and WNN were examined for comparison purposes. The results are overall promising, in which all the three models were capable to reasonably replicating wind pressure characteristics (i.e., time series, power spectra and wind pressure coefficient distribution) using experimental data at selected locations. In particular, WNN was shown to produce the most satisfactory prediction results. This evidences that WNN can be considered as a useful and reliable tool to predict surface wind pressure on tall buildings. The outcomes of this study are expected to provide important implications to practically aid the wind-resistant design of tall buildings.

Artificial intelligence based high voltage cable bonding to prevent cable termination faults

Cable termination fault (CTF) is a major problem for high voltage cable lines (HVCL). Increasing of the sheath voltage (SV), zero sequence current (ZC) and current harmonic distortion (THDI) on metallic sheath (MS) of HVC are major factors for CTF. MS is grounded according to IEEE 575-1988 standard to reduce SV. However, these methods are not sufficient to prevent CTF based on ZC and THDI. The aims of this paper are minimization of SV, ZC and THDI to prevent CTF based on ZC and THDI. Thus, LSSB method is developed as a new bonding method. Also, LSSB parameters should be optimized to make the most economical and practical bonding. GA, DEA, PSO and iPSO are used optimization methods for optimization of LSSB. SV and THDI should be known for optimization of LSSB, so the forecasting methods (FM) are used as fitness function of optimization methods in LSSB optimization. The regression and hybrid artificial neural network methods are compared to determine the most suitable FM. When the optimized LSSB is used for bonding of long HVCL, SV reduces approximately 90%, ZC reduces approximately 93%, and THDI reduces approximately 70%. Thus CTF risk is minimized by using the optimized LSSB in HVCL.

Comparative study of hybrid artificial neural network methods under stationary and nonstationary data in stock market

In this study, a new methodology is proposed to automatically determine six parameters of artificial neural network using population‐based metaheuristics. We considered following three issues: What is the effect of used metaheuristic on performance? Which parameters are mostly selected? Is there a difference between the forecasting results when using stationary or nonstationary dataset that are selected according to the augmented Dickey–Fuller test statistics? Based upon results of performance measures, proposed method leads to significant opportunities to forecast stock market more effectively. We also expect proposed methodology can provide remarkable advantages for other complex, dynamic, and nonlinear forecasting problems.

OSSB and Hybrid Methods to Prevent Cable Faults for Harmonic Containing Networks

High temperature causes aging of insulation material of high voltage underground cable line (HVUL), so cable faults occurs. The sheath current (SC) is the most important factor for increasing of cable temperature, and SC can include harmonic, so cable temperature quickly increases. Namely harmonics facilitate SC based cable faults. Metallic sheath of HVUL is grounded to prevent SC based faults. Three bonding methods are used for grounding of metallic sheath in literature, but these methods do not exactly prevent SC effects, so the sectional solid bonding (SSB) is developed in literature, but these methods are not evaluated at the high harmonic distortion case. SSB should be optimized according to SC value. However, SC of HVUL which will be installed as a new line at project phase is not known. Thus, artificial neural network (ANN) and hybrid ANN methods are used to forecast SC of new line at the project phase. iPSO, PSO, GA and DEA methods are used for hybrid ANN methods and optimization of SSB. Performance of the optimized SSB (OSSB) at the high harmonic distortion case is evaluated to prove that use of OSSB for HVUL.DOI: http://dx.doi.org/10.5755/j01.eie.24.2.20633

Design of a new hybrid artificial neural network method based on decision trees for calculating the Froude number in rigid rectangular channels

Abstract A vital topic regarding the optimum and economical design of rigid boundary open channels such as sewers and drainage systems is determining the movement of sediment particles. In this study, the incipient motion of sediment is estimated using three datasets from literature, including a wide range of hydraulic parameters. Because existing equations do not consider the effect of sediment bed thickness on incipient motion estimation, this parameter is applied in this study along with the multilayer perceptron (MLP), a hybrid method based on decision trees (DT) (MLP-DT), to estimate incipient motion. According to a comparison with the observed experimental outcome, the proposed method performs well (MARE = 0.048, RMSE = 0.134, SI = 0.06, BIAS = -0.036). The performance of MLP and MLP-DT is compared with that of existing regression-based equations, and significantly higher performance over existing models is observed. Finally, an explicit expression for practical engineering is also provided.

A hybrid artificial neural network method with uniform design for structural optimization

This paper presents a new hybrid artificial neural network (ANN) method for structural optimization. The method involves the selection of training datasets for establishing an ANN model by uniform design method, approximation of the objective or constraint functions by the trained ANN model and yields solutions of structural optimization problems using the sequential quadratic programming method (SQP). In the proposed method, the use of the uniform design method can improve the quality of the selected training datasets, leading to a better performance of the ANN model. As a result, the ANN dramatically reduces the number of required trained datasets, and shows a good ability to approximate the objective or constraint functions and then provides an accurate estimation of the optimum solution. It is shown through three numerical examples that the proposed method provides accurate and computationally efficient estimates of the solutions of structural optimization problems.