scholarly journals Prediction-Based Maintenance of Existing Bridges Using Neural Network and Sensitivity Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Pengyong Miao
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Predictive Ability ◽  
Relative Effect ◽  
Analysis Method ◽  
Ann Model ◽  
Sensitivity Analysis Method ◽  
Artificial Neural Network Ann ◽  
Existing Bridges ◽  
Inspection Data

Bridge deterioration is affected by various factors. However, neither the relationships between these factors and deterioration are explicitly determined, nor the relative effect of each factor on deterioration is well understood. This study proposed a methodology to resolve these issues by integrating an artificial neural network (ANN) and sensitivity analysis method. The ANN was used to predict deterioration, and the sensitivity analysis method was applied to evaluate the influence of each factor on deterioration. Testing the methodology with 3,368 bridge inspection data pieces indicates that (1) the developed ANN obtained an accuracy of about 65%; and (2) seven factors were identified affecting deterioration. The established ANN model has equivalent performance for three deterioration grades and four types of bridges. Two sensitivity analysis (the Shapley value and the Sobol indices) methods were compared, and they identified the same five most important factors. Consequently, the methodology can effectively avoid the uncertainty of factors on deterioration by providing a relative importance list of factors. The methodology’s predictive ability and factor importance identification ability make it suitable for decision-makers to understand the deterioration situations and to schedule a further inspection and corresponding maintenance strategies.

MULTI-PARAMETER ANN MODEL FOR FLAT-END MILLING

2008 ◽  
Vol 32 (3-4) ◽  
pp. 523-536 ◽  
Author(s):  
Hazim El Mounayri ◽  
M. Affan Badar ◽  
Gustavo A. Rengifo
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Cutting Force ◽  
Process Parameters ◽  
End Milling ◽  
Predictive Ability ◽  
Ann Model ◽  
Network Output ◽  
Input Variables ◽  
Artificial Neural Network Ann

The quality, productivity and safety of machining can be significantly improved through the optimization of cutting conditions. The first step in achieving such an objective is the development of accurate and reliable models for predicting the critical process parameters. In this paper, an innovative Artificial Neural Network (ANN) model that predicts both cutting force and surface roughness in end milling is developed and validated. A set of five input variables is selected to represent the machining conditions while twelve quantities representing two key process parameters, namely, cutting force and surface roughness, form the variables of the network output. Full factorial design of experiments is used to generate data for both training and validation. Successful training of the neural network is demonstrated through comparison of simulated and experimental results for four different output variables, namely cutting force, surface roughness, feed marks, and tooth passing frequency. The predictive ability of the model is verified experimentally by comparing simulated output variables with their experimental counterparts. A good agreement is observed.

Sensitivity Analysis in Artificial Neural Network and it's Applications on the Research of Attributes Correlation

2013 ◽  
Vol 785-786 ◽  
pp. 1441-1446
Author(s):  
Hong Yan Lin ◽  
Hai Hua Xing
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Calculation Method ◽  
Strong Correlation ◽  
Analysis Method ◽  
Model Input ◽  
Network Connection ◽  
Sensitivity Analysis Method ◽  
Artificial Neural

Sensitivity analysis method can evaluate the importance of model input attributes. A multivariable sensitivity analysis method based on neural network connection weights and a calculation method of attributes correlation are proposed in this paper, and are applied to the research of attributes correlation. To verify the effectiveness of the proposed methods, this study employed a man-made example and a UCI-IRIS dataset to test the performance of the method. The results show that the sensitivity analysis method can really identify important and strong correlation attributes of model, and can simplify the model effectively, and can improve the accuracy of the model.

scholarly journals Prediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods

2021 ◽  
Author(s):  
Shabnam Hosseinzadeh ◽  
Amir Etemad-Shahidi ◽  
Ali Koosheh
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Gaussian Process Regression ◽  
Structural Features ◽  
Support Vector ◽  
Ann Model ◽  
Wave Overtopping ◽  
Taylor Diagram ◽  
The Mean ◽  
Artificial Neural Network Ann

Abstract The accurate prediction of the mean wave overtopping rate at breakwaters is vital to have a safe design. Hence, providing a robust tool as a preliminary estimator can be useful for practitioners. Recently, soft computing tools such as artificial neural network (ANN) have been developed as alternatives to traditional overtopping formulae. The goal of this paper is to assess the capabilities of two kernel-based methods namely Gaussian process regression (GPR) and support vector regression for the prediction of mean wave overtopping rate at sloped breakwaters. An extensive dataset taken from EurOtop (2018) database, including rubble mound structures with permeable core, straight slopes, without berm, and crown wall, was employed to develop the models. Different combinations of the important dimensionless parameters representing structural features and wave conditions were tested based on the sensitivity analysis for developing the models. The obtained results were compared with those of the ANN model and the existing empirical formulae. The modified Taylor diagram was used to compare the models graphically. The results showed the superiority of kernel-based models, especially the GPR model over the ANN model and empirical formulae. In addition, the optimal input combination was introduced based on accuracy and the number of input parameters criteria. Finally, the physical consistencies of developed models were investigated the results, of which demonstrated the reliability of kernel-based models in terms of delivering physics of overtopping phenomenon.

scholarly journals Simplifying Neural Network Based Model for Ship Motion Prediction: A Comparative Study of Sensitivity Analysis

2017 ◽  
Author(s):  
Xu Cheng ◽  
Shengyong Chen ◽  
Chen Diao ◽  
Mengna Liu ◽  
Guoyuan Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Comparative Study ◽  
Structural Complexity ◽  
Cost Effective ◽  
Ship Motion ◽  
Superior Performance ◽  
Motion Prediction ◽  
Ann Model ◽  
Artificial Neural Network Ann

This paper presents a comparative study of sensitivity analysis (SA) and simplification on artificial neural network (ANN) based model used for ship motion prediction. Considering traditional structural complexity of ANN usually results in slow convergence, SA, as an efficient tool for correlation analysis, can help to reconstruct the ANN model for ship motion prediction. An ANN-Garson method and an ANN-EFAST method are proposed, both of which utilize the ANN for modeling but select the input parameters in a local and a global fashion, respectively. Through the benchmark tests, ANN-EFAST exhibits superior performance in both linear and nonlinear systems. Further test on ANN-EFAST via a case study of ship heading prediction shows its cost-effective and timely in compacting the ANN based prediction model.

scholarly journals Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

2021 ◽  
Vol 18 (10) ◽  
pp. 5110
Author(s):  
Shu-Farn Tey ◽  
Chung-Feng Liu ◽  
Tsair-Wei Chien ◽  
Chin-Wei Hsu ◽  
Kun-Chen Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Early Stage ◽  
Characteristic Curve ◽  
Patient Discharge ◽  
Ann Model ◽  
Healthcare Settings ◽  
High Area ◽  
Icd 10 ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; ≅70%) and test (n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

Sign in / Sign up

Export Citation Format

Share Document