scholarly journals A Damage Detection Method Using Neural Network Optimized by Multiple Particle Collision Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Sergio V. Farias ◽  
Osamu Saotome ◽  
Haroldo F. Campos Velho ◽  
Elcio H. Shiguemori
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Lamb Wave ◽  
Learning Strategy ◽  
Damage Identification ◽  
Particle Collision ◽  
Learning Approaches ◽  
Propagation Methods ◽  
Artificial Neural Network Ann ◽  
Multiple Particle

A critical task of structural health monitoring is damage detection and localization. Lamb wave propagation methods have been successfully applied for damage identification in plate-like structures. However, Lamb wave processing is still a challenging task due to its multimodal and dispersive characteristics. To address this issue, data-driven machine learning approaches as artificial neural network (ANN) have been proposed. However, the effectiveness of ANN can be improved based on its architecture and the learning strategy employed to train it. The present paper proposes a Multiple Particle Collision Algorithm (MPCA) to design an optimum ANN architecture to detect and locate damages in plate-like structures. For the first time in the literature, the MPCA is applied to find damages in plate-like structures. The present work uses one piezoelectric transducer to generate Lamb wave signals on an aluminum plate structure and a linear array of four transducers to capture the scattered signals. The continuous wavelet transform (CWT) processes the captured signals to estimate the time-of-flight (ToF) that is the ANN inputs. The ANN output is the damage spatial coordinates. In addition to MPCA optimization, this paper uses a quantitative entropy-based criterion to find the best mother wavelet and the scale values. The presented experimental results show that MPCA is capable of finding a simple ANN architecture with good generalization performance in the proposed damage localization application. The proposed method is compared with the 1-dimensional convolutional neural network (1D-CNN). A discussion about the advantages and limitations of the proposed method is presented.

Vibration–Based Damage Detection Of Slab Structure Using Artificial Neural Network

2012 ◽  
Author(s):  
Norhisham Bakhary
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Damage Detection ◽  
Ann Model ◽  
Element Analysis ◽  
Severity Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Kertas kerja ini memaparkan kajian berkenaan keberkesanan Artificial Neural Network (ANN) dalam mengenal pasti kerosakan di dalam struktur. Data dari getaran seperti frekuensi semula jadi dan mod bentuk digunakan sebagai data masukan bagi ANN untuk meramalkan lokasi dan tahap kerosakan bagi struktur lantai. Analisis unsur terhingga (Finite Element Analysis) telah digunakan untuk memperoleh ciri–ciri dinamik bagi struktur–struktur rosak dan tidak rosak untuk ‘melatih’ model ‘neural network’. Senario kerosakan yang berbeza disimulasikan dengan mengurangkan kekukuhan elemen pada lokasi yang berbeza sepanjang struktur tersebut. Berbagai kombinasi data masukan bagi mod yang berbeza telah digunakan untuk memperolehi model ANN yang terbaik. Hasil kajian ini menunjukkan ANN mampu memberikan keputusan yang baik dalam meramal kerosakan pada struktur lantai tersebut. Kata kunci: Ramalan kerosakan struktur, Artificial Neural Network This paper investigates the effectiveness of artificial neural network (ANN) in identifying damages in structures. Global (natural frequencies) and local (mode shapes) vibration–based data has been used as the input to ANN for location and severity prediction of damages in a slab–like structure. A finite element analysis has been used to obtain the dynamic characteristics of intact and damaged structure to train the neural network model. Different damage scenarios have been introduced by reducing the local stiffness of the selected elements at different locations along the structure. Several combinations of input variables in different modes have been used in order to obtain a reliable ANN model. The trained ANN model is validated using laboratory test data. The results show that ANN is capable of providing acceptable result on damage prediction of tested slab structure. Key words: Structural damage detection, artificial neural network

scholarly journals A Neural-Wavelet Technique for Damage Identification in the ASCE Benchmark Structure Using Phase II Experimental Data

2010 ◽  
Vol 2010 ◽  
pp. 1-13 ◽  
Author(s):  
Mahmoud M. Reda Taha
Keyword(s):  
Damage Detection ◽  
Damage Identification ◽  
Damage Classification ◽  
Integrated Method ◽  
Damage Recognition ◽  
Wavelet Energy ◽  
Challenging Tasks ◽  
Damage States ◽  
Artificial Neural Network Ann ◽  
American Society

Damage pattern recognition research represents one of the most challenging tasks in structural health monitoring (SHM). The vagueness in defining damage and the significant overlap between damage states contribute to the challenges associated with proper damage classification. Uncertainties in the damage features and how they propagate during the damage detection process also contribute to uncertainties in SHM. This paper introduces an integrated method for damage feature extraction and damage recognition. We describe a robust damage detection method that is based on using artificial neural network (ANN) to compute the wavelet energy of acceleration signals acquired from the structure. We suggest using the wavelet energy as a damage feature to classify damage states in structures. A case study is presented that shows the ability of the proposed method to detect and pattern damage using the American Society of Civil Engineers (ASCEs) benchmark structure. It is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy to varying levels of damage.

scholarly journals Fast Detection and Traceback-based Mitigation of Interest Flooding Attack

2021 ◽  
Author(s):  
Naveen Kumar ◽  
Shashank Srivast
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Learning Approaches ◽  
Previous Approach ◽  
State Information ◽  
Fast Detection ◽  
Flooding Attack ◽  
Artificial Neural Network Ann ◽  
Ann Classifier ◽  
Better Than

Abstract NDN Pending Interest Table (PIT) helps NDN by storing the state of a request within the router. This state information helps the router to redirect the data packet towards the requester. However, an attacker can send malicious requests, which could flood the PIT; this attack is known as Interest Flooding Attack (IFA). In our previous work, we have found the most relevant features needed to detect IFA and applied a few machine learning approaches for the offline detection of IFA. In this article, a trained Artificial Neural Network (ANN) classifier has been deployed on each NDN router for the online detection of IFA. A novel traceback-based mitigation is proposed, which is triggered after the detection. The proposed approach is found better than the previous approach in terms of the satisfaction ratio and throughput of the legitimate consumers.

A Hybrid Learning Strategy for Real Hardware of Swing-Up Pendulum

2007 ◽  
Vol 11 (8) ◽  
pp. 972-978 ◽  
Author(s):  
Shingo Nakamura ◽  
◽  
Ryo Saegusa ◽  
Shuji Hashimoto
Keyword(s):  
Neural Network ◽  
Learning Strategy ◽  
Back Propagation ◽  
Optimal Controller ◽  
Learning Approaches ◽  
Learning Method ◽  
Data Sampling ◽  
Target Task ◽  
The Real ◽  
Real Hardware

Generally, the bottom-up learning approaches, such as neural-network, to obtain the optimal controller of target task for mechanical system face a problem including huge number of trials, which require much time and give stress against the hardware. To avoid such problems, a simulator is often built and performed with a learning method. However, there are also problems that how simulator is constructed and how accurate it performs. In this paper, we are considering a construction of simulator directly from the real hardware. Afterward a constructed simulator is used for learning target task and the obtained optimal controller is applied to the real hardware. As an example, we picked up the pendulum swing-up task which was a typical nonlinear control problem. The construction of a simulator is performed by back-propagation method with neural-network and the optimal controller is obtained by reinforcement learning method. Both processes are implemented without using the real hardware after the data sampling, therefore, load against the hardware gets sufficiently smaller, and the objective controller can be obtained faster than using only the hardware. And we consider that our proposed method can be a basic learning strategy to obtain the optimal controller of mechanical systems.

Application of Improved BPNN to Damage Detection of Composite Materials

2011 ◽  
Vol 467-469 ◽  
pp. 1097-1101
Author(s):  
Xiao Ma Dong
Keyword(s):  
Neural Network ◽  
Composite Materials ◽  
Damage Detection ◽  
Bp Neural Network ◽  
Dynamic Method ◽  
Damage Identification ◽  
Wavelet Series ◽  
Improved Algorithm

A dynamic method based on improved algorithm BP neural network for damage identification of composite materials was proposed. By using wavelet series, the features of signals were extracted and input to improved algorithm BP neural network for training the network and identifying the damages. Finally, the experiment results show that this proposed method can exactly identify the faults of composite materials.

scholarly journals Ecological Footprint Prediction based on Global Macro Indicators in G-20 Countries using Machine Learning Approaches

2021 ◽  
Author(s):  
Ahmad Roumiani ◽  
Abbas Mofidi
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Ecological Impact ◽  
Forest Products ◽  
Predictive Performance ◽  
Penalized Regression ◽  
Study Data ◽  
Elastic Net ◽  
Learning Approaches ◽  
Artificial Neural Network Ann

Abstract Paying attention to human activities in terms of land grazing infrastructure, crops, forest products and carbon impact, the so-called ecological impact (EF) is one of the most important economic issues in the world. In the present study, data from global databases were used. The ability of the penalized regression approach (PR including Ridge, Lasso and Elastic Net) and artificial neural network (ANN) to predict EF indices in the G-20 over the past two decades (1999–2018) was depicted and compared. For this purpose, 10-fold cross-validation was used to assess predictive performance and to specify a penalty parameter for PR models. Based on the results, a slight improvement in prediction performance was observed over linear regression. Using the Elastic Net model, more global macro indices were selected than Lasso. Although Lasso included only some indicators, it still had better predictive performance among PR models. Although the findings using PR methods were only slightly better than linear regression, their interest in selecting a subset of controllable indicators by shrinking the coefficients and creating a parsimonious model was apparent. As a result, penalized regression methods would be preferred, using feature selectivity and interpretive considerations rather than predictive performance alone. On the other hand, neural network-based models with higher values of coefficients of determination (R2) and values lower of RMSE than PR and OLS had significant performance and showed that they are more accurate in predicting EF. The results showed that the ANN network could provide considerable and appropriate predictions for EF indicators in the G-20 countries. predictions

Demand Forecasting in Supply Chain Management Using Different Deep Learning Methods

2021 ◽  
pp. 140-170
Author(s):  
Asma Husna ◽  
Saman Hassanzadeh Amin ◽  
Bharat Shah
Keyword(s):  
Neural Network ◽  
Supply Chain ◽  
Deep Learning ◽  
Supply Chain Management ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Learning Approaches ◽  
Chain Management ◽  
Lstm Network ◽  
Artificial Neural Network Ann

Supply chain management (SCM) is a fast growing and largely studied field of research. Forecasting of the required materials and parts is an important task in companies and can have a significant impact on the total cost. To have a reliable forecast, some advanced methods such as deep learning techniques are helpful. The main goal of this chapter is to forecast the unit sales of thousands of items sold at different chain stores located in Ecuador with holistic techniques. Three deep learning approaches including artificial neural network (ANN), convolutional neural network (CNN), and long short-term memory (LSTM) are adopted here for predictions from the Corporación Favorita grocery sales forecasting dataset collected from Kaggle website. Finally, the performances of the applied models are evaluated and compared. The results show that LSTM network tends to outperform the other two approaches in terms of performance. All experiments are conducted using Python's deep learning library and Keras and Tensorflow packages.

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