Vibrational health risk assessment for truck operators in mining using artificial neural network

2021 ◽  
pp. 095440702110644
Author(s):  
Mohammad Javad Rahimdel ◽  
Mehdi Mirzaei ◽  
Javad Sattarvand
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Health Risk ◽  
Truck Drivers ◽  
Open Pit ◽  
Whole Body ◽  
Risk Level ◽  
Speed Level ◽  
Haul Road ◽  
Artificial Neural

Operators of mining vehicles are frequently exposed to harmful levels of whole-body vibration (WBV). Long time exposure to WBV causes backache and has non-ergonomic effects on the human body. Exposure levels of the WBV have already been evaluated for different vehicles. Among these vehicles, mining trucks usually operate at the various working phases and also in different haul road conditions. This paper aims to develop a simultaneous integrated model to predict the WBV exposure for mining truck drivers. Considering the effect of the speed level, weight and geometry of load on the WBV exposure for the mining truck drivers are limited. There is not much research to predict the vibrational health risk level in conditions with no or missing data, as well. The root mean squire (RMS) of the vertical vibration of the seat and cabin floor was obtained during different operational conditions of an open pit mine in Iran. Then an artificial neural network was designed for the prediction of the vibrational health risk level. Regarding the results of this study, haul road quality, speed level, and load profile had a significant effect on vibration exposure. The average of the RMS values were 0.942 and 1.176 m/s2 for the good and poor road conditions, respectively that are in the high health risk levels. However, there was no significant relationship between the payloads, in the range of 20 to 30 tons, in the RMS values. At speeds higher than 30 km/h, the vibrational health risk was at high level for all conditions. Moreover, there were 93.83% correlation between the measured and simulated RMS values was found in the application of the neural network. This paper helps the mine managers to predict the unsafe conditions and consider the practical approach for the WBV risk reduction.

scholarly journals Deriving pQCT-Type Spatial Density from DXA Images

2020 ◽  
Vol 185 (Supplement_1) ◽  
pp. 430-434
Author(s):  
Anthony E Iyoho ◽  
Brenda A Niederberger ◽  
Dale Bergquist-Turori ◽  
Karen R Kelly ◽  
Laurel J Ng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Injury Risk ◽  
Whole Body ◽  
Spatial Density ◽  
Military Recruits ◽  
Artificial Neural

Abstract Introduction Musculoskeletal overuse injuries are a serious problem in the military, particularly in basic combat training, resulting in hundreds of millions of dollars lost because of limited duty days, medical treatment, and high rates of reinjury. Injury risk models have been developed using peripheral computed tomography (pQCT)-based injury correlates. However, pQCT image capture on large number of recruits is not practical for military settings. Thus, this article presents a method to derive spatial density pQCT images from much lower resolution but more accessible dual-energy X-ray absorptiometry (DXA). Materials and Methods Whole-body DXA images and lower leg pQCT images for 51 male military recruits were collected before a 40-day School of Infantry. An artificial neural network model was constructed to relate the DXA density profiles to spatial pQCT density at the 38% and 66% tibial locations. Results Strong correlation, R2 = 0.993 and R2 = 0.990 for the 38% and 66% pQCT slices, respectively, was shown between spatial density predicted by the artificial neural network model and raw data. Conclusions High potential exists to create a practical protocol using DXA in place of pQCT to assess stress fracture risk and aid in mitigating musculoskeletal injuries seen in military recruits.

scholarly journals Recognition of Daily Human Activity Using an Artificial Neural Network and Smartwatch

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Min-Cheol Kwon ◽  
Sunwoong Choi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Body Movement ◽  
Human Activity Recognition ◽  
Daily Activities ◽  
Whole Body ◽  
Wide Range ◽  
Artificial Neural

Human activity recognition using wearable devices has been actively investigated in a wide range of applications. Most of them, however, either focus on simple activities wherein whole body movement is involved or require a variety of sensors to identify daily activities. In this study, we propose a human activity recognition system that collects data from an off-the-shelf smartwatch and uses an artificial neural network for classification. The proposed system is further enhanced using location information. We consider 11 activities, including both simple and daily activities. Experimental results show that various activities can be classified with an accuracy of 95%.

The Prediction of the Risk Level of Pulmonary Embolism and Deep Vein Thrombosis through Artificial Neural Network

2016 ◽  
Vol 24 (5) ◽  
pp. 274
Author(s):  
Laleh Agharezaei ◽  
Zhila Agharezaei ◽  
Ali Nemati ◽  
Kambiz Bahaadinbeigy ◽  
Farshid Keynia ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Pulmonary Embolism ◽  
Deep Vein Thrombosis ◽  
Risk Level ◽  
Vein Thrombosis ◽  
Artificial Neural ◽  
Deep Vein

scholarly journals Predictive Modelling for Blasting-Induced Vibrations from Open-Pit Excavations

2021 ◽  
Vol 11 (16) ◽  
pp. 7487
Author(s):  
Yo-Hyun Choi ◽  
Sean Seungwon Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Models ◽  
Predictive Modelling ◽  
Influential Factors ◽  
Open Pit ◽  
Site Factors ◽  
Empirical Formulas ◽  
Ground Conditions ◽  
Artificial Neural

Reliable estimates of peak particle velocity (PPV) from blasting-induced vibrations at a construction site play a crucial role in minimizing damage to nearby structures and maximizing blasting efficiency. However, reliably estimating PPV can be challenging due to complex connections between PPV and influential factors such as ground conditions. While many efforts have been made to estimate PPV reliably, discrepancies remain between measured and predicted PPVs. Here, we analyzed various methods for assessing PPV with several key relevant factors and 1,191 monitored field blasting records at 50 different open-pit sites across South Korea to minimize the discrepancies. Eight prediction models are used based on artificial neural network, conventional empirical formulas, and multivariable regression analyses. Seven influential factors were selected to develop the prediction models, including three newly included and four already formulated in empirical formulas. The three newly included factors were identified to have a significant influence on PPV, as well as the four existing factors, through a sensitivity analysis. The measured and predicted PPVs were compared to evaluate the performances of prediction models. The assessment of PPVs by an artificial neural network yielded the lowest errors, and site factors, K and m were proposed for preliminary open-pit blasting designs.

Flood Risk Pattern Recognition Using Integrated Chemometric Method and Artificial Neural Network: A Case Study in the Johor River Basin

2015 ◽  
Vol 74 (1) ◽  
Author(s):  
Ahmad Shakir Mohd Saudi ◽  
Azman Azid ◽  
Hafizan Juahir ◽  
Mohd Ekhwan Toriman ◽  
Mohammad Azizi Amran ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
River Basin ◽  
Monsoon Season ◽  
Strong Relationship ◽  
Suspended Solid ◽  
Risk Level ◽  
Statistical Process ◽  
Risk Class ◽  
Artificial Neural

Flood is a major problem in Johor river basin, which normally happened during monsoon season. However in this study, it shows that rainfall did not have a strong relationship for the changes of water level compared to suspended solid and stream flow, where both variables have p-values of <0.0001 and these variables also became the main factors in contributing to the flood occurrence based on Factor Analysis result. Time Series Analysis was being carried out and based on Statistical Process Control, the limitation has been set up for mitigation in controlling flood. All data beyond the Upper Control Limit was predicted to have High Risk to face flood and Emergency Response Plan should be implemented to prevent complication and destruction because of flood. The prediction for the risk level was carried out using the application of Artificial Neural Network (ANN), where the accuracy of prediction was very high, with the result of 96% for the level of accuracy in the prediction of risk class.

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