Predicting fatigue damage of highway suspension bridge hangers using weigh-in-motion data and machine learning

2020 ◽  
Vol 17 (2) ◽  
pp. 233-248
Author(s):  
Yang Deng ◽  
Meng Zhang ◽  
Dong-Ming Feng ◽  
Ai-Qun Li
Keyword(s):  
Machine Learning ◽  
Fatigue Damage ◽  
Suspension Bridge ◽  
Motion Data ◽  
Weigh In Motion

Fatigue performance investigation for hangers of suspension bridges based on site-specific vehicle loads

2018 ◽  
Vol 18 (3) ◽  
pp. 934-948 ◽  
Author(s):  
Yang Deng ◽  
Aiqun Li ◽  
Dongming Feng
Keyword(s):  
Structural Integrity ◽  
Suspension Bridge ◽  
Fatigue Performance ◽  
Suspension Bridges ◽  
Time Step ◽  
Site Specific ◽  
Motion Data ◽  
Motion System ◽  
Weigh In Motion ◽  
Vehicle Loads

Hangers or suspenders of a suspension bridge are the primary load-carrying members and are vital to the structural integrity and service life of the bridge. Site-specific vehicle loads monitored by the weigh-in-motion system can assist to obtain the operational cyclic stresses of hangers. Differing from most existing studies, herein, a framework for fatigue performance investigation for hangers of suspension bridges is proposed utilizing the full information of the weigh-in-motion data. This framework includes four steps: (1) generate influence surfaces for hangers, (2) reconstruct vehicular loading flows based on the weigh-in-motion data, (3) calculate time histories of hanger tension forces, and (4) evaluate fatigue damages and predict fatigue lives. Critical issues, such as the loading configuration of trucks, the threshold of the gross vehicle weight, and the time step for stress calculation, have been studied and discussed in detail. Based on 8-month weigh-in-motion data of a prototype suspension bridge, it is shown that the fatigue damage of hangers can be evaluated day by day, and subsequently the fatigue lives can be predicted. The correlation between the fatigue damages and vehicular loads is also investigated in this study.

scholarly journals Machine learning of Truck Traffic Classification groups from Weigh-in-Motion data

2021 ◽  
pp. 100178
Author(s):  
Narges Tahaei ◽  
Jidong J. Yang ◽  
Mi Geum Chorzepa ◽  
S. Sonny Kim ◽  
Stephan A. Durham
Keyword(s):  
Machine Learning ◽  
Traffic Classification ◽  
Truck Traffic ◽  
Motion Data ◽  
Weigh In Motion

Research on the expression of new visual intelligence system based on machine learning technology

2021 ◽  
pp. 1-9
Author(s):  
Zuoshan Li
Keyword(s):  
Machine Learning ◽  
Intelligent System ◽  
Science And Technology ◽  
Learning Technology ◽  
3D Animation ◽  
Motion Data ◽  
Intelligence System ◽  
New Science ◽  
Continuous Progress ◽  
Internet Of Things Technology

With the continuous progress of society, the level of science and technology of the country has made a leap forward development, the research energy of various industries on new science and technology continues to deepen, greatly promoting the promotion of science and technology. At the same time, with the increase in social pressure, more and more people pursue spiritual relaxation, and appropriate leisure and entertainment activities have gradually become a part of people’s life. Film plays an irreplaceable role in leisure and entertainment. Mainly from the background of the development of the film industry towards intelligent direction, and then use machine learning technology to study the application of film animation production and film virtual assets analysis and investigation. Based on the Internet of things technology, we also vigorously develop the ways and methods of visual expression of movies, and at the same time introduce new expression modes to promote the expression effect of the intelligent system. Finally, by comparing various algorithms in machine learning technology, the results of intelligent expression of random number forest algorithm in machine learning technology are more accurate. The system is also applied to 3D animation production to observe the measurement error of 3D motion data and facial expression data.

Duration prediction of Chilean strong motion data using machine learning

2021 ◽  
Vol 109 ◽  
pp. 103253
Author(s):  
Sarit Chanda ◽  
M.C. Raghucharan ◽  
K.S.K. Karthik Reddy ◽  
Vasudeo Chaudhari ◽  
Surendra Nadh Somala
Keyword(s):  
Machine Learning ◽  
Strong Motion ◽  
Strong Motion Data ◽  
Motion Data ◽  
Duration Prediction

Detecting Face Touching Using Smartwatches to Mitigate the Spread of COVID-19: Pilot Study (Preprint)

2021 ◽  
Author(s):  
Chen Bai ◽  
Yu-Peng Chen ◽  
Adam Wolach ◽  
Lisa Anthony ◽  
Mamoun Mardini
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Respiratory Diseases ◽  
Window Size ◽  
Support Vector ◽  
Accelerometer Data ◽  
Respiratory Illnesses ◽  
Motion Data ◽  
Machine Learning Methods

BACKGROUND Frequent spontaneous facial self-touches, predominantly during outbreaks, have the theoretical potential to be a mechanism of contracting and transmitting diseases. Despite the recent advent of vaccines, behavioral approaches remain an integral part of reducing the spread of COVID-19 and other respiratory illnesses. Real-time biofeedback of face touching can potentially mitigate the spread of respiratory diseases. The gap addressed in this study is the lack of an on-demand platform that utilizes motion data from smartwatches to accurately detect face touching. OBJECTIVE The aim of this study was to utilize the functionality and the spread of smartwatches to develop a smartwatch application to identifying motion signatures that are mapped accurately to face touching. METHODS Participants (n=10, 50% women, aged 20-83) performed 10 physical activities classified into: face touching (FT) and non-face touching (NFT) categories, in a standardized laboratory setting. We developed a smartwatch application on Samsung Galaxy Watch to collect raw accelerometer data from participants. Then, data features were extracted from consecutive non-overlapping windows varying from 2-16 seconds. We examined the performance of state-of-the-art machine learning methods on face touching movements recognition (FT vs NFT) and individual activity recognition (IAR): logistic regression, support vector machine, decision trees and random forest. RESULTS Machine learning models were accurate in recognizing face touching categories; logistic regression achieved the best performance across all metrics (Accuracy: 0.93 +/- 0.08, Recall: 0.89 +/- 0.16, Precision: 0.93 +/- 0.08, F1-score: 0.90 +/- 0.11, AUC: 0.95 +/- 0.07) at the window size of 5 seconds. IAR models resulted in lower performance; the random forest classifier achieved the best performance across all metrics (Accuracy: 0.70 +/- 0.14, Recall: 0.70 +/- 0.14, Precision: 0.70 +/- 0.16, F1-score: 0.67 +/- 0.15) at the window size of 9 seconds. CONCLUSIONS Wearable devices, powered with machine learning, are effective in detecting facial touches. This is highly significant during respiratory infection outbreaks, as it has a great potential to refrain people from touching their faces and potentially mitigate the possibility of transmitting COVID-19 and future respiratory diseases.

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