scholarly journals Pavement diagnosis accuracy with controlled application of artificial neural network

2015 ◽  
Vol 10 (4) ◽  
pp. 355-364 ◽  
Author(s):  
Andrzej Pożarycki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Management System ◽  
Empirical Relationship ◽  
Pavement Management ◽  
Training Set ◽  
Pavement Management System ◽  
Neural Network Learning ◽  
Pavement Structure ◽  
Artificial Neural

Results of research studies, the amount of input data available in pavement management system databases, and artificial intelligence methods serve as versatile tools, well-suited for the analysis conducted as a part of pavement management system. The key source of new and to be employed knowledge is provided. In terms of e.g. assessing thickness of bituminous pavement layers, the default solution is pavement drilling, but for the purposes of pavement management it is prohibitively expensive. This paper attempts to test the original concept of employing an empirical relationship in an algorithm verifying results produced by the artificial neural network method. The assumed multistage asphalt pavement layer thickness identification control process boils down to evaluating test results of the road section built using both, reinforced and non-reinforced pavement structure. By default, the artificial neural network training set has not included the reinforced pavement sections. Hence, it has been possible to identify “perturbations” in assumptions underlying the training set. Pavement test section points’ results are indicated in the automated manner, which, in line with implemented methods, is not generated by perturbations caused by divergence between actual pavement structure and assumptions taken for purposes of building pavement management system database, and the artificial neural network learning dataset is based on.

Expert Project Recommendation Procedure for Arizona Department of Transportation’s Pavement Management System

1997 ◽  
Vol 1592 (1) ◽  
pp. 26-34 ◽  
Author(s):  
Gerardo W. Flintsch ◽  
John P. Zaniewski
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Management System ◽  
Trigger Points ◽  
Pavement Management ◽  
Network Simulator ◽  
Pavement Management System ◽  
Remaining Service Life ◽  
Artificial Neural ◽  
Condition Indicator

The Arizona Department of Transportation (ADOT) uses a network-level pavement management system to determine budget requirements for its annual pavement preservation program. Although this is a valuable tool for preservation programming, it does not assist the engineers with the selection of projects and rehabilitation treatments. The documented research was designed to enhance the capability of ADOT’s pavement management system to include project selection. An automatic project recommendation procedure was developed and implemented in a user-friendly, modular computer program. This automatic system is expected to reduce considerably the effort required to develop the preservation programs. It should improve the consistency of the decision process. The analysis starts with a section delineation procedure that delineates uniform roadway sections. It then computes the remaining service life of each uniform section by using linear performance equations and trigger points defined for each condition indicator. An artificial neural network simulator is used to screen and recommend roadway sections for the preservation program. The trained artificial neural network prepares a list of candidate sections, using the criteria learned from past selections and the current condition of all pavement sections. This preliminary list of candidate sections is further analyzed by a project recommendation procedure. This procedure recommends a preservation treatment, assigns a priority rating to each section in the list, and sorts the projects by priority. Funding is assigned to the highest-priority sections within each roadway group until the budget recommendation provided by the network optimization process is reached.

scholarly journals Effect of Reconstruction Algorithm on the Identification of 3D Printing Polymers Based on Hyperspectral CT Technology Combined with Artificial Neural Network

Materials ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1963 ◽  
Author(s):  
Zheng Fang ◽  
Renbin Wang ◽  
Mengyi Wang ◽  
Shuo Zhong ◽  
Liquan Ding ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
3D Printing ◽  
Principal Components ◽  
Reconstruction Technique ◽  
Ct Reconstruction ◽  
Training Set ◽  
X Ray ◽  
Test Sets ◽  
Artificial Neural

Hyperspectral X-ray CT (HXCT) technology provides not only structural imaging but also the information of material components therein. The main purpose of this study is to investigate the effect of various reconstruction algorithms on reconstructed X-ray absorption spectra (XAS) of components shown in the CT image by means of HXCT. In this paper, taking 3D printing polymer as an example, seven kinds of commonly used polymers such as thermoplastic elastomer (TPE), carbon fiber reinforced polyamide (PA-CF), acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), ultraviolet photosensitive resin (UV9400), polyethylene terephthalate glycol (PETG), and polyvinyl alcohol (PVA) were selected as samples for hyperspectral CT reconstruction experiments. Seven kinds of 3D printing polymer and two interfering samples were divided into a training set and test sets. First, structural images of specimens were reconstructed by Filtered Back-Projection (FBP), Algebra Reconstruction Technique (ART) and Maximum-Likelihood Expectation-Maximization (ML-EM). Secondly, reconstructed XAS were extracted from the pixels of region of interest (ROI) compartmentalized in the images. Thirdly, the results of principal component analysis (PCA) demonstrated that the first four principal components contain the main features of reconstructed XAS, so we adopted Artificial Neural Network (ANN) trained by the reconstructed XAS expressed by the first four principal components in the training set to identify that the XAS of corresponding polymers exist in both of test sets from the training set. The result of ANN displays that FBP has the best performance of classification, whose ten-fold cross-validation accuracy reached 99%. It suggests that hyperspectral CT reconstruction is a promising way of getting image features and material features at the same time, which can be used in medical imaging and nondestructive testing.

Artificial Neural Network Learning of Nonstationary Behavior in Time Series

2003 ◽  
Vol 13 (02) ◽  
pp. 103-109 ◽  
Author(s):  
María I. Széliga ◽  
Pablo F. Verdes ◽  
Pablo M. Granitto ◽  
H. Alejandro Ceccatto
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Error Function ◽  
Input Parameter ◽  
Input Unit ◽  
Validation Data ◽  
Neural Network Learning ◽  
Secular Evolution ◽  
Artificial Neural

We refine and complement a previously-proposed artificial neural network method for learning hidden signals forcing nonstationary behavior in time series. The method adds an extra input unit to the network and feeds it with the proposed profile for the unknown perturbing signal. The correct time evolution of this new input parameter is learned simultaneously with the intrinsic stationary dynamics underlying the series, which is accomplished by minimizing a suitably-defined error function for the training process. We incorporate here the use of validation data, held out from the training set, to accurately determine the optimal value of a hyperparameter required by the method. Furthermore, we evaluate this algorithm in a controlled situation and show that it outperforms other existing methods in the literature. Finally, we discuss a preliminary application to the real-world sunspot time series and link the obtained hidden perturbing signal to the secular evolution of the solar magnetic field.

scholarly journals Radial Basis Function Artificial Neural Network for the Investigation of Thyroid Cytological Lesions

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Christos Fragopoulos ◽  
Abraham Pouliakis ◽  
Christos Meristoudis ◽  
Emmanouil Mastorakis ◽  
Niki Margari ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Histological Evaluation ◽  
Image Analysis System ◽  
Training Set ◽  
Test Set ◽  
Radial Basis ◽  
Artificial Neural

Objective. This study investigates the potential of an artificial intelligence (AI) methodology, the radial basis function (RBF) artificial neural network (ANN), in the evaluation of thyroid lesions. Study Design. The study was performed on 447 patients who had both cytological and histological evaluation in agreement. Cytological specimens were prepared using liquid-based cytology, and the histological result was based on subsequent surgical samples. Each specimen was digitized; on these images, nuclear morphology features were measured by the use of an image analysis system. The extracted measurements (41,324 nuclei) were separated into two sets: the training set that was used to create the RBF ANN and the test set that was used to evaluate the RBF performance. The system aimed to predict the histological status as benign or malignant. Results. The RBF ANN obtained in the training set has sensitivity 82.5%, specificity 94.6%, and overall accuracy 90.3%, while in the test set, these indices were 81.4%, 90.0%, and 86.9%, respectively. Algorithm was used to classify patients on the basis of the RBF ANN, the overall sensitivity was 95.0%, the specificity was 95.5%, and no statistically significant difference was observed. Conclusion. AI techniques and especially ANNs, only in the recent years, have been studied extensively. The proposed approach is promising to avoid misdiagnoses and assists the everyday practice of the cytopathology. The major drawback in this approach is the automation of a procedure to accurately detect and measure cell nuclei from the digitized images.

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