Machine Learning Based Quantitative Damage Monitoring of Composite Structure

نویسندگان

چکیده

Composite materials have been widely used in many industries due to their excellent mechanical properties. It is difficult analyze the integrity and durability of composite structures because own characteristics complexity load environments. Structural health monitoring (SHM) based on built-in sensor networks has evaluated as a method improve safety reliability reduce operational cost. With rapid development machine learning, large number learning algorithms applied disciplines, also are being field SHM avoid limitations resulting from need physical models. In this paper, damage technologies often for briefly outlined, applications concisely reviewed. Then, challenges solutions quantitative discussed, focusing complete acquisition data, deep analysis correlation between signal eigenvalues structure states, intelligent identification delamination damage. Finally, trend learning-based discussed.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Structure Health Monitoring in Extreme Events from Machine Learning Perspective

Structure health monitoring utilizes the statistical signal information gathered from sensors implemented on structures to detect the building behavior. This information is more accurate and easier to analyze than traditional structural analysis method, which detects the building damage using dynamic properties directly. In this project, acceleration time history records of a Benchmark structur...

متن کامل

Evaluation of remote sensing indicators in drought monitoring using machine learning algorithms (Case study: Marivan city)

Remote sensing indices are used to analyze the Spatio-temporal distribution of drought conditions and to identify the severity of drought. This study, using various drought indices generated from Madis and TRMM satellite data extracted from Google Earth Engine (GEE) platform. Drought conditions in Marivan city from February to November for the years 2001 to 2017 were analyzed based on spatial a...

متن کامل

Local context encoding enables machine learning-based quantitative photoacoustics

Real-time monitoring of functional tissue parameters, such as local blood oxygenation, based on optical imaging could provide groundbreaking advances in the diagnosis and interventional therapy of various diseases. While photoacoustic (PA) imaging is a novel modality with great potential to measure optical absorption deep inside tissue, quantification of the measurements remains a major challen...

متن کامل

Classification of composite damage from FBG load monitoring signals

This paper describes a new method for the classification and identification of two major types of defects in composites, namely delamination and matrix cracks, by classification of the spectral features of fibre Bragg grating (FBG) signals. In aeronautical applications of composites, after a damage is detected, it is very useful to know the type of damage prior to determining the treatment meth...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: International Journal of Smart and Nano Materials

سال: 2022

ISSN: ['1947-542X', '1947-5411']

DOI: https://doi.org/10.1080/19475411.2022.2054878