Non-Destructive in Situ Identification of the Moisture Content in Saline Brick Walls Using Artificial Neural Networks

doi 10.3311/ccc2019-012
Full Text
Abstract

Available in full text

Date
Authors
Publisher

Budapest University of Technology and Economics

Related search

In-Situ Moisture Assessment in External Walls of Historic Building Using Non-Destructive Methods

Environmental and Climate Technologies
2019English

Parametric Identification of Control Systems Using Artificial Neural Networks

MATEC Web of Conferences
Materials ScienceEngineeringChemistry
2015English

Application of Artificial Neural Networks in Vibration-Based Damage Identification

English

Fish School Identification in the Bali Strait Using Acoustic Descriptor and Artificial Neural Networks Techniques

International Journal of Remote Sensing and Earth Sciences (IJReSES)
2010English

Earthquake Forecasting Using Artificial Neural Networks

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
2018English

Method of Laser Monitoring of Oil Pollution in Sea Water (In Situ) Using Variable Strobing, Non-Linear Fluorimetry, and Artificial Neural Networks Techniques

English

Text Classification Using Artificial Neural Networks

International Journal of Engineering and Technology(UAE)
ArchitectureHardwareEngineeringChemical EngineeringBiotechnologyEnvironmental EngineeringComputer Science
2017English

Thunderstorm Predictions Using Artificial Neural Networks

2016English

Application of Neural Networks to Predict the Moisture Content in Oil-Filled Power Transformers Insulation

Bulletin of NTU "KhPI". Series: Problems of Electrical Machines and Apparatus Perfection. The Theory and Practice
2019English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2024 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy