Neuronet-Based Approach To Modeling the Durability of Aggregate in Concrete Pavement Construction

1997 ◽  
Vol 1582 (1) ◽  
pp. 29-33 ◽  
Author(s):  
Yacoub M. Najjar ◽  
Imad A. Basheer ◽  
Richard L. McReynolds
Keyword(s):  
Physical Properties ◽  
Network Models ◽  
Concrete Pavements ◽  
Department Of Transportation ◽  
Freezing And Thawing ◽  
Neural Network Models ◽  
Durability Test ◽  
Wide Scatter ◽  
Pavement Construction ◽  
Concrete Elements

The durability of aggregate used in concrete pavements construction is commonly assessed by subjecting small concrete beams containing the aggregate to cyclic freezing and thawing. The durability of aggregate and concrete specimens is quantified by measuring the durability factor (DF) and percent expansion (EXP). A typical durability test may last 3 to 5 months and involve high costs. It was assumed that the durability of aggregate used as a constituent in concrete elements may be related to some easily measured physical properties of the aggregate. A data base obtained from records of the Kansas Department of Transportation contained a total of 750 durability tests. The observed wide scatter in the experimental data when DF or EXP is related to one physical parameter suggested the use of artificial neural networks to model durability. Neural network models were developed to predict durability of aggregate from five basic physical properties of the aggregate. The models were found to classify the aggregates with regard to their durability with a relatively high accuracy. In addition, the models were used to assess the reliability of prediction. To illustrate the use of the models, numerical examples are presented.

Prediction of Concrete Strength Using Support Vector Machines Algorithm

2020 ◽  
Vol 986 ◽  
pp. 9-17
Author(s):  
Ashraf Shaqadan
Keyword(s):  
Compressive Strength ◽  
Physical Properties ◽  
Prediction Models ◽  
Concrete Strength ◽  
Network Models ◽  
Support Vector ◽  
Ann Model ◽  
Neural Network Models ◽  
Data Set ◽  
Different Levels

A laboratory analysis of concrete samples requires significant experimental time and cost. In addition, advancement in data mining provide valuable tool for researchers to extract information regarding relations among experiment and physical properties in a more elaborate way to improve prediction models performance and guide concrete mix design. A 90 samples data set is developed and used in this research. The experiment is designed to study the effect of natural silica addition at different levels on physical properties of concrete mainly compressive strength. Compressive strength is measured after 3 and 28 days for different levels of milling time. Support vector regression and neural network models are developed for predicting the compressive strength of concrete using five input variables including silica additive fraction. The SVR model metrics are compared with ANN model and showed good correlation coefficient of 0.929 but less than ANN. The advantage of SVR over ANN is shown in the developed regression model which can be interpreted physically. The silica fraction variable ranked third after curing time and cement ratio variable which indicates its importance.

scholarly journals An Overview of Corrosion Risk of Metal Culverts in Arkansas

2019 ◽  
Vol 271 ◽  
pp. 02004
Author(s):  
Mdariful Hasan ◽  
Zahid Hossain
Keyword(s):  
Laboratory Experiments ◽  
Relevant Literature ◽  
Network Models ◽  
Environmental Data ◽  
Department Of Transportation ◽  
Neural Network Models ◽  
The Public ◽  
Corrosion Risk ◽  
User Friendly ◽  
Highway System

Metal culverts or pipes used in cross-drains along or across the Arkansas highway system are susceptible to corrode over time. Catastrophic incidents such as a complete washout of metal culverts along with roadway can be prevented if proper metals can be selected during the construction project. At present, the Arkansas Department of Transportation (ArDOT) does not have enough information about corrosion effects on metal culverts. The main objective of this study is to develop a user-friendly corrosion map for Arkansas by analyzing soil properties, water properties, and environmental data collected from the public domain as well as those gathered from laboratory experiments. Experimental data will be used to develop mathematical models to predict the resistivity and corrosive nature of soils. In this paper, relevant literature has been reviewed to narrow down the specific gaps in the available data and limitations in using methods to analyze the risk, challenges in developing regression and neural network models and risk mapping. Findings of this study have helped the research team to design the experimental plan and appropriate metrics need to be considered for developing the predictive models for this study.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

Measuring the World

2018 ◽  
Author(s):  
Ann-Sophie Barwich
Keyword(s):  
Sensory Information ◽  
Network Models ◽  
Model System ◽  
Stimulus Input ◽  
Expectancy Effects ◽  
Neural Network Models ◽  
Perceptual Object ◽  
External Objects ◽  
The Common ◽  
The Brain

How much does stimulus input shape perception? The common-sense view is that our perceptions are representations of objects and their features and that the stimulus structures the perceptual object. The problem for this view concerns perceptual biases as responsible for distortions and the subjectivity of perceptual experience. These biases are increasingly studied as constitutive factors of brain processes in recent neuroscience. In neural network models the brain is said to cope with the plethora of sensory information by predicting stimulus regularities on the basis of previous experiences. Drawing on this development, this chapter analyses perceptions as processes. Looking at olfaction as a model system, it argues for the need to abandon a stimulus-centred perspective, where smells are thought of as stable percepts, computationally linked to external objects such as odorous molecules. Perception here is presented as a measure of changing signal ratios in an environment informed by expectancy effects from top-down processes.

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