scholarly journals A data-based structural health monitoring approach for damage detection in steel bridges using experimental data

2021 ◽  
Author(s):  
Bjørn T. Svendsen ◽  
Gunnstein T. Frøseth ◽  
Ole Øiseth ◽  
Anders Rønnquist
Keyword(s):  
Machine Learning ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Damage ◽  
Learning Algorithms ◽  
Steel Bridges ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Unsupervised Machine Learning

AbstractThere is a need for reliable structural health monitoring (SHM) systems that can detect local and global structural damage in existing steel bridges. In this paper, a data-based SHM approach for damage detection in steel bridges is presented. An extensive experimental study is performed to obtain data from a real bridge under different structural state conditions, where damage is introduced based on a comprehensive investigation of common types of steel bridge damage reported in the literature. An analysis approach that includes a setup with two sensor groups for capturing both the local and global responses of the bridge is considered. From this, an unsupervised machine learning algorithm is applied and compared with four supervised machine learning algorithms. An evaluation of the damage types that can best be detected is performed by utilizing the supervised machine learning algorithms. It is demonstrated that relevant structural damage in steel bridges can be found and that unsupervised machine learning can perform almost as well as supervised machine learning. As such, the results obtained from this study provide a major contribution towards establishing a methodology for damage detection that can be employed in SHM systems on existing steel bridges.

scholarly journals Structural Health Monitoring using Varied Machine Learning Algorithms

2018 ◽  
Vol 7 (3.12) ◽  
pp. 793 ◽  
Author(s):  
B Shanthi ◽  
Mahalakshmi N ◽  
Shobana M
Keyword(s):  
Machine Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
Structural Health

Structural Health Monitoring is essential in today’s world where large amount of money and labour are involved in building a structure. There arises a need to periodically check whether the built structure is strong and flawless, also how long it will be strong and if not how much it is damaged. These information are needed so that the precautions can be made accordingly. Otherwise, it may result in disastrous accidents which may take away even human lives. There are various methods to evaluate a structure. In this paper, we apply various classification algorithms like J48, Naive Bayes and many other classifiers available, to the dataset to check on the accuracy of the prediction determined by all of these classification algorithms and ar-rive at the conclusion of the best possible classifier to say whether a structure is damaged or not.  

scholarly journals Wind Turbine Blade Damage Detection Using Supervised Machine Learning Algorithms

2017 ◽  
Vol 139 (6) ◽  
Author(s):  
Taylor Regan ◽  
Christopher Beale ◽  
Murat Inalpolat
Keyword(s):  
Machine Learning ◽  
Damage Detection ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Learning Algorithms ◽  
Turbine Blades ◽  
Machine Learning Algorithms ◽  
Test Rig ◽  
Wind Turbine Blades ◽  
Baseline Characteristics

Wind turbine blades undergo high operational loads, experience variable environmental conditions, and are susceptible to failure due to defects, fatigue, and weather-induced damage. These large-scale composite structures are fundamentally enclosed acoustic cavities and currently have limited, if any, structural health monitoring (SHM) in place. A novel acoustics-based structural sensing and health monitoring technique is developed, requiring efficient algorithms for operational damage detection of cavity structures. This paper describes the selection of a set of statistical features for acoustics-based damage detection of enclosed cavities, such as wind turbine blades, as well as a systematic approach used in the identification of competent machine learning algorithms. Logistic regression (LR) and support vector machine (SVM) methods are identified and used with optimal feature selection for decision-making via binary classification algorithms. A laboratory-scale wind turbine with hollow composite blades was built for damage detection studies. This test rig allows for testing of stationary or rotating blades, of which time and frequency domain information can be collected to establish baseline characteristics. The test rig can then be used to observe any deviations from the baseline characteristics. An external microphone attached to the tower will be utilized to monitor blade health while blades are internally ensonified by wireless speakers. An initial test campaign with healthy and damaged blade specimens is carried out to arrive at several conclusions on the detectability and feature extraction capabilities required for damage detection.

scholarly journals Crop price prediction using supervised machine learning algorithms

2021 ◽  
Vol 1916 (1) ◽  
pp. 012042
Author(s):  
Ranjani Dhanapal ◽  
A AjanRaj ◽  
S Balavinayagapragathish ◽  
J Balaji
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Price Prediction

scholarly journals Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm

2021 ◽  
Vol 11 (15) ◽  
pp. 6728
Author(s):  
Muhammad Asfand Hafeez ◽  
Muhammad Rashid ◽  
Hassan Tariq ◽  
Zain Ul Abideen ◽  
Saud S. Alotaibi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tabu Search ◽  
Decision Tree ◽  
Decision Trees ◽  
Search Algorithm ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Tabu Search Algorithm

Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf and decision nodes in the corresponding decision trees. Additionally, the used tabu search algorithm is responsible to balance the entropy of the corresponding decision trees. For training the model, we used the clinical data of COVID-19 patients to predict whether a patient is suffering. The experimental results were obtained using our proposed classifier based on the built-in sci-kit learn library in Python. The extensive analysis for the performance comparison was presented using Big O and statistical analysis for conventional supervised machine learning algorithms. Moreover, the performance comparison to optimized state-of-the-art classifiers is also presented. The achieved accuracy of 98%, the required execution time of 55.6 ms and the area under receiver operating characteristic (AUROC) for proposed method of 0.95 reveals that the proposed classifier algorithm is convenient for large datasets.

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