Asphalt concrete stability estimation from non-destructive test methods with artificial neural networks

2012 ◽  
Vol 23 (3-4) ◽  
pp. 989-997
Author(s):  
Serdal Terzi ◽  
Mustafa Karaşahin ◽  
Süleyman Gökova ◽  
Mustafa Tahta ◽  
Nihat Morova ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Asphalt Concrete ◽  
Test Methods ◽  
Stability Estimation ◽  
Destructive Test ◽  
Artificial Neural ◽  
Non Destructive

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

Application of Artificial Neural Networks for Estimating Dynamic Modulus of Asphalt Concrete

2009 ◽  
Vol 2127 (1) ◽  
pp. 173-186 ◽  
Author(s):  
Maryam Sadat Sakhaei Far ◽  
B. Shane Underwood ◽  
S. Ranji Ranjithan ◽  
Y. Richard Kim ◽  
Newton Jackson
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Asphalt Concrete ◽  
Dynamic Modulus ◽  
Artificial Neural

scholarly journals Predicting Performance of Lightweight Concrete with Granulated Expanded Glass and Ash Aggregate by Means of Using Artificial Neural Networks

Materials ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2002 ◽  
Author(s):  
Marzena Kurpinska ◽  
Leszek Kułak
Keyword(s):  
Thermal Conductivity ◽  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Lightweight Concrete ◽  
Construction Materials ◽  
Test Methods ◽  
Predicting Performance ◽  
Artificial Neural ◽  
Reduction Of Co2

Lightweight concrete (LWC) is a group of cement composites of the defined physical, mechanical, and chemical performance. The methods of designing the composition of LWC with the assumed density and compressive strength are used most commonly. The purpose of using LWC is the reduction of the structure’s weight, as well as the reduction of thermal conductivity index. The highest possible strength, durability and low thermal conductivity of construction materials are important factors and reasons for this field’s development, which lies largely in modification of materials’ composition. Higher requirements for construction materials are related to activities aiming at environment protection. The purpose of the restrictions is the reduction of energy consumption and, as a result, the reduction of CO2 emission. To limit the scope of time-consuming and often high-cost laboratory works necessary to calibrate models used in the test methods, it is possible to apply Artificial Neural Networks (ANN) to predict any of the concrete properties. The aim of this study is to demonstrate the applicability of this tool for solving the problems, related to establishing the relation between the choice of type and quantity of lightweight aggregates and the porosity, bulk density and compressive strength of LWC. For the tests porous lightweight Granulated Expanded Glass Aggregate (GEGA) and Granulated Ash Aggregate (GAA) have been used.

Sign in / Sign up

Export Citation Format

Share Document